diff --git a/.gitignore b/.gitignore index 1e71cec..a1653e4 100644 --- a/.gitignore +++ b/.gitignore @@ -1,17 +1,16 @@ -# Benchmarking data -# benchmarking/data/citrus_output/ -# benchmarking/data/citrus_phenos/ -# benchmarking/data/splits/ -# benchmarking/data/ukb/* -# !benchmarking/data/ukb/ -# !benchmarking/data/ukb/*.py - -# benchmarking/prs/plink_output/* -# !benchmarking/prs/plink_output/ -# !benchmarking/prs/plink_output/*.py -# benchmarking/prs/prsice2_output/ +# Benchmarking benchmarking*/ +# Large plots +paper/pheno_sim/sim_configs/complex_pheno_1.png +paper/pheno_sim/sim_configs/complex_pheno_1_noise.png +paper/pheno_sim/sim_configs/complex_pheno_4.png + +# Paper eval +paper/data/ +paper/output/ +paper/pheno_sim/citrus*/ + # Tools benchmarking/PRSice2/ benchmarking/QCTOOL/ diff --git a/paper/pheno_sim/citrus_out_to_plink_fmt.py b/paper/pheno_sim/citrus_out_to_plink_fmt.py new file mode 100644 index 0000000..1fb6704 --- /dev/null +++ b/paper/pheno_sim/citrus_out_to_plink_fmt.py @@ -0,0 +1,58 @@ +"""Convert CITRUS output CSV to plink phenotype file format. + +Example output: + + #IID gene1 phenotype + 1110 23.31 22.22 + 2202 34.12 18.23 + ... + +""" + +import os + +import pandas as pd + +# # Write a function to replace main +# def citrus_out_to_plink_phenotype( +# sample_id_col='' +# ) + +import argparse + + +if __name__ == "__main__": + + # args + parser = argparse.ArgumentParser() + + parser.add_argument("desc") + + args = parser.parse_args() + + sample_id_col = 'sample_id' + + pheno_cols = ['phenotype'] + + csv_path = f'citrus_output/{args.desc}.csv' + output_path = f'citrus_phenos/{args.desc}.pheno' + + # function + df = pd.read_csv( + csv_path, + index_col=False + ) + + df = df[[sample_id_col] + pheno_cols] + + # Split FID and IID + df['FID'] = df[sample_id_col].str.split('_').map(lambda x: x[0]) + df['IID'] = df[sample_id_col].str.split('_').map(lambda x: x[1]) + + df[['FID', 'IID'] + pheno_cols].to_csv( + output_path, + sep='\t', + index=False + ) + + diff --git a/paper/pheno_sim/est_heritability.py b/paper/pheno_sim/est_heritability.py new file mode 100644 index 0000000..3616081 --- /dev/null +++ b/paper/pheno_sim/est_heritability.py @@ -0,0 +1,45 @@ +"""Estimate heritability of simulated CITRUS phenotypes.""" + +import json +import os + +from pheno_sim import PhenoSimulation +from pheno_sim.heritability_estimation import broad_sense_heritability + + +if __name__ == '__main__': + + sim_name = 'gepsi_lin_add_hFULL_hFULL' + config_dir = 'citrus_output' + + print(sim_name) + + # Load simulation config + with open(os.path.join(config_dir, sim_name + '_config.json'), 'r') as f: + sim_config = json.load(f) + + # Create simulation + sim = PhenoSimulation(sim_config) + + # Load genotype data + input_vals = sim.run_input_step() + + # Compute heritability + confidence_level = 95 + + heritability = broad_sense_heritability( + sim, + input_vals=input_vals, + n_samples=0.1, + confidence_level=confidence_level/100, + ) + + # Save results + with open( + os.path.join( + config_dir, sim_name + f'_heritability_ci{confidence_level}.json' + ), + 'w' + ) as f: + json.dump(heritability, f, indent=4) + diff --git a/paper/pheno_sim/gen_lin_add_sim.py b/paper/pheno_sim/gen_lin_add_sim.py new file mode 100644 index 0000000..cdc64a4 --- /dev/null +++ b/paper/pheno_sim/gen_lin_add_sim.py @@ -0,0 +1,121 @@ +"""Generate linear additive phenotype CITRUS simulation configs.""" + +import copy +import json + +import numpy as np +import pandas as pd + + +CONFIG_TEMPLATE = { + "input": [ + { + "file": "../data/ukb/vcf/phased_chr19.vcf", + "file_format": "vcf", + "reference_genome": "GRCh37", + "input_nodes": [ + { + "alias": "variants", + "type": "SNP", + "chr": "19", + "pos": [] + } + ] + } + ], + "simulation_steps": [ + { + "type": "RandomConstant", + "alias": "variants_betas", + "input_match_size": "variants", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.0, + "scale": 1.0 + }, + "by_feat": True + }, + { + "type": "Product", + "alias": "variant_effects", + "input_aliases": [ + "variants_betas", + "variants" + ] + }, + { + "type": "AdditiveCombine", + "alias": "combined_variant_effects", + "input_alias": "variant_effects" + }, + { + "type": "SumReduce", + "alias": "no_noise_phenotype", + "input_alias": "combined_variant_effects" + }, + { + "type": "Heritability", + "alias": "phenotype", + "input_alias": "no_noise_phenotype", + } + ] +} + + +class NpEncoder(json.JSONEncoder): + def default(self, obj): + if isinstance(obj, np.integer): + return int(obj) + if isinstance(obj, np.floating): + return float(obj) + if isinstance(obj, np.ndarray): + return obj.tolist() + return super(NpEncoder, self).default(obj) + + +def create_config(pos, heritability): + sim_config = copy.deepcopy(CONFIG_TEMPLATE) + + sim_config['input'][0]['input_nodes'][0]['pos'] = list(pos) + sim_config['simulation_steps'][-1]['heritability'] = float(heritability) + + return sim_config + + +if __name__ == '__main__': + + # Options + n_vars = [10, 100, 1000] + herit_vals = [0.05, 0.1, 0.2, 0.4, 0.8] + version_num = 1 + + config_save_dir = 'sim_configs' + + # Load variant info + var_info_path = '../data/ukb/chr19_var_info.csv' + var_df = pd.read_csv( + var_info_path + ) + + # Add position column + var_df['pos'] = var_df.locus.apply(lambda x: int(x.split(':')[1])) + + for n_var in n_vars: + for herit in herit_vals: + rng = np.random.default_rng(seed=147*version_num) + + # Select loci + pos = rng.choice( + var_df.pos, + size=n_var, + replace=False + ) + + sim_config = create_config(list(pos), herit) + + # Save config + h_str = str(herit).split('.')[1] + config_fname = f'lin_add_nvar{n_var}_h{h_str}_v{version_num}' + + with open(f'{config_save_dir}/{config_fname}.json', 'w') as f: + json.dump(sim_config, f, indent=4, cls=NpEncoder) \ No newline at end of file diff --git a/paper/pheno_sim/sim_configs/complex_pheno_1.json b/paper/pheno_sim/sim_configs/complex_pheno_1.json new file mode 100644 index 0000000..03d10b8 --- /dev/null +++ b/paper/pheno_sim/sim_configs/complex_pheno_1.json @@ -0,0 +1,407 @@ +{ + "input": [ + { + "file": "../data/ukb/vcf/phased_chr19.vcf", + "file_format": "vcf", + "reference_genome": "GRCh37", + "input_nodes": [ + { + "alias": "ENSG00000267443_transcribed", + "type": "SNP", + "chr": "19", + "pos": [ + 375779, + 376008, + 376382, + 376585, + 376653, + 378566, + 378597, + 379164, + 379227, + 384724, + 388463, + 390319, + 391125, + 392840, + 396396, + 398636, + 399132 + ] + }, + { + "alias": "ENSG00000267443_promoter", + "type": "SNP", + "chr": "19", + "pos": 399170 + }, + { + "alias": "CYP4F12_common", + "type": "SNP", + "chr": "19", + "pos": [ + 15797532, + 15800331, + 15807305, + 15807884, + 15807984, + 15830583, + 15839081, + 15839459 + ] + }, + { + "alias": "CYP4F12_rare", + "type": "SNP", + "chr": "19", + "pos": [ + 15807491, + 15782397, + 15850632, + 15839365, + 15887706 + ] + }, + { + "alias": "EID2B_common", + "type": "SNP", + "chr": "19", + "pos": [ + 39994711, + 40000638, + 40009683, + 40008118, + 40047633, + 40093111, + 40116048, + 40023308, + 40163241 + ] + }, + { + "alias": "EID2B_deleterious", + "type": "SNP", + "chr": "19", + "pos": [ + 39991203, + 39980412, + 40058427, + 40075510, + 39996347, + 40081651, + 40092547 + ] + } + ], + "sample_id_field": "s" + } + ], + "simulation_steps": [ + { + "type": "RandomConstant", + "alias": "ENSG00000267443_betas", + "input_match_size": "ENSG00000267443_transcribed", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.0, + "scale": 0.5 + }, + "by_feat": true + }, + { + "type": "Product", + "alias": "ENSG00000267443_effects", + "input_aliases": [ + "ENSG00000267443_betas", + "ENSG00000267443_transcribed" + ] + }, + { + "type": "SumReduce", + "alias": "ENSG00000267443_effect_sum", + "input_alias": "ENSG00000267443_effects" + }, + { + "type": "StandardScaler", + "alias": "ENSG00000267443_scaled", + "input_alias": "ENSG00000267443_effect_sum" + }, + { + "type": "Sigmoid", + "alias": "ENSG00000267443_damped_effect", + "input_alias": "ENSG00000267443_scaled" + }, + { + "type": "Distribution", + "alias": "ENSG00000267443_promoter_multiplier", + "input_match_size": "ENSG00000267443_damped_effect", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.8, + "scale": 0.025 + } + }, + { + "type": "Product", + "alias": "ENSG00000267443_effect_with_promoter", + "input_aliases": [ + "ENSG00000267443_damped_effect", + "ENSG00000267443_promoter_multiplier" + ] + }, + { + "type": "IfElse", + "alias": "ENSG00000267443_haplotype_values", + "input_cond_vals": "ENSG00000267443_promoter", + "input_if_vals": "ENSG00000267443_effect_with_promoter", + "input_else_vals": "ENSG00000267443_damped_effect" + }, + { + "type": "MeanCombine", + "alias": "ENSG00000267443", + "input_alias": "ENSG00000267443_haplotype_values", + "mean_type": "geometric" + }, + { + "type": "RandomConstant", + "alias": "CYP4F12_common_betas", + "input_match_size": "CYP4F12_common", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.0, + "scale": 0.2 + }, + "by_feat": true + }, + { + "type": "RandomConstant", + "alias": "CYP4F12_rare_betas", + "input_match_size": "CYP4F12_rare", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.0, + "scale": 0.4 + }, + "by_feat": true + }, + { + "type": "ReLU", + "alias": "CYP4F12_rare_betas_pos", + "input_alias": "CYP4F12_rare_betas", + "neg_slope": -1.0 + }, + { + "type": "Product", + "alias": "CYP4F12_common_effects", + "input_aliases": [ + "CYP4F12_common_betas", + "CYP4F12_common" + ] + }, + { + "type": "Product", + "alias": "CYP4F12_rare_effects", + "input_aliases": [ + "CYP4F12_rare_betas_pos", + "CYP4F12_rare" + ] + }, + { + "type": "Concatenate", + "alias": "CYP4F12_effects", + "input_aliases": [ + "CYP4F12_common_effects", + "CYP4F12_rare_effects" + ] + }, + { + "type": "SumReduce", + "alias": "CYP4F12_hap_val", + "input_alias": "CYP4F12_effects" + }, + { + "type": "Tanh", + "alias": "CYP4F12_hap_val_damped", + "input_alias": "CYP4F12_hap_val" + }, + { + "type": "MaxCombine", + "alias": "CYP4F12_max", + "input_alias": "CYP4F12_hap_val_damped" + }, + { + "type": "MeanCombine", + "alias": "CYP4F12_mean", + "input_alias": "CYP4F12_hap_val_damped" + }, + { + "type": "Concatenate", + "alias": "CYP4F12_comb_vals", + "input_aliases": [ + "CYP4F12_max", + "CYP4F12_mean" + ] + }, + { + "type": "Constant", + "alias": "CYP4F12_comb_weights", + "constant": [ + 0.8, + 0.2 + ], + "input_match_size": "CYP4F12_comb_vals" + }, + { + "type": "Product", + "alias": "CYP4F12_comb_vals_weighted", + "input_aliases": [ + "CYP4F12_comb_vals", + "CYP4F12_comb_weights" + ] + }, + { + "type": "MeanReduce", + "alias": "CYP4F12", + "input_alias": "CYP4F12_comb_vals_weighted" + }, + { + "type": "RandomConstant", + "alias": "EID2B_common_betas", + "input_match_size": "EID2B_common", + "dist_name": "uniform", + "dist_kwargs": { + "low": 0.01, + "high": 0.25 + }, + "by_feat": true + }, + { + "type": "Product", + "alias": "EID2B_common_effects", + "input_aliases": [ + "EID2B_common_betas", + "EID2B_common" + ] + }, + { + "type": "SumReduce", + "alias": "EID2B_common_effects_sum", + "input_alias": "EID2B_common_effects" + }, + { + "type": "GaussianNoise", + "alias": "EID2B_common_effects_sum_w_noise_unbounded", + "input_alias": "EID2B_common_effects_sum", + "noise_std": 0.025 + }, + { + "type": "Clip", + "alias": "EID2B_common_effects_sum_w_noise", + "input_alias": "EID2B_common_effects_sum_w_noise_unbounded", + "min_val": 0 + }, + { + "type": "RandomConstant", + "alias": "EID2B_deleterious_multiplier", + "input_match_size": "EID2B_deleterious", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.3, + "scale": 0.05 + }, + "by_feat": true + }, + { + "type": "Constant", + "alias": "constant_1", + "input_match_size": "EID2B_deleterious", + "constant": 1.0 + }, + { + "type": "IfElse", + "alias": "EID2B_multiplier_by_variant", + "input_cond_vals": "EID2B_deleterious", + "input_if_vals": "EID2B_deleterious_multiplier", + "input_else_vals": "constant_1" + }, + { + "type": "MinReduce", + "alias": "EID2B_deleterious_multiplier", + "input_alias": "EID2B_multiplier_by_variant" + }, + { + "type": "Product", + "alias": "EID2B_hap_val", + "input_aliases": [ + "EID2B_deleterious_multiplier", + "EID2B_common_effects_sum_w_noise" + ] + }, + { + "type": "MeanCombine", + "alias": "EID2B", + "input_alias": "EID2B_hap_val" + }, + { + "type": "ReLU", + "alias": "ReLU_EID2B", + "input_alias": "EID2B", + "threshold": 0.2, + "neg_slope": 1.0, + "pos_slope": 1.2 + }, + { + "type": "Product", + "alias": "EID2B_ENSG00000267443_interaction", + "input_aliases": [ + "ReLU_EID2B", + "ENSG00000267443" + ] + }, + { + "type": "StandardScaler", + "alias": "zscore_ENSG00000267443", + "input_alias": "ENSG00000267443" + }, + { + "type": "StandardScaler", + "alias": "zscore_CYP4F12", + "input_alias": "CYP4F12" + }, + { + "type": "Concatenate", + "alias": "concat_zscores", + "input_aliases": [ + "zscore_ENSG00000267443", + "zscore_CYP4F12" + ] + }, + { + "type": "Softmax", + "alias": "softmax_zscores", + "input_alias": "concat_zscores" + }, + { + "type": "MinReduce", + "alias": "min_softmax", + "input_alias": "softmax_zscores" + }, + { + "type": "Product", + "alias": "EID2B_relative_abundance_interaction", + "input_aliases": [ + "min_softmax", + "EID2B" + ] + }, + { + "type": "Sum", + "alias": "phenotype", + "input_aliases": [ + "CYP4F12", + "EID2B_ENSG00000267443_interaction", + "EID2B_relative_abundance_interaction" + ] + } + ] +} \ No newline at end of file diff --git a/paper/pheno_sim/sim_configs/complex_pheno_1_noise.json b/paper/pheno_sim/sim_configs/complex_pheno_1_noise.json new file mode 100644 index 0000000..fb5c8ec --- /dev/null +++ b/paper/pheno_sim/sim_configs/complex_pheno_1_noise.json @@ -0,0 +1,431 @@ +{ + "input": [ + { + "file": "../data/ukb/vcf/phased_chr19.vcf", + "file_format": "vcf", + "reference_genome": "GRCh37", + "input_nodes": [ + { + "alias": "ENSG00000267443_transcribed", + "type": "SNP", + "chr": "19", + "pos": [ + 375779, + 376008, + 376382, + 376585, + 376653, + 378566, + 378597, + 379164, + 379227, + 384724, + 388463, + 390319, + 391125, + 392840, + 396396, + 398636, + 399132 + ] + }, + { + "alias": "ENSG00000267443_promoter", + "type": "SNP", + "chr": "19", + "pos": 399170 + }, + { + "alias": "CYP4F12_common", + "type": "SNP", + "chr": "19", + "pos": [ + 15797532, + 15800331, + 15807305, + 15807884, + 15807984, + 15830583, + 15839081, + 15839459 + ] + }, + { + "alias": "CYP4F12_rare", + "type": "SNP", + "chr": "19", + "pos": [ + 15807491, + 15782397, + 15850632, + 15839365, + 15887706 + ] + }, + { + "alias": "EID2B_common", + "type": "SNP", + "chr": "19", + "pos": [ + 39994711, + 40000638, + 40009683, + 40008118, + 40047633, + 40093111, + 40116048, + 40023308, + 40163241 + ] + }, + { + "alias": "EID2B_deleterious", + "type": "SNP", + "chr": "19", + "pos": [ + 39991203, + 39980412, + 40058427, + 40075510, + 39996347, + 40081651, + 40092547 + ] + } + ], + "sample_id_field": "s" + } + ], + "simulation_steps": [ + { + "type": "RandomConstant", + "alias": "ENSG00000267443_betas", + "input_match_size": "ENSG00000267443_transcribed", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.0, + "scale": 0.5 + }, + "by_feat": true + }, + { + "type": "Product", + "alias": "ENSG00000267443_effects", + "input_aliases": [ + "ENSG00000267443_betas", + "ENSG00000267443_transcribed" + ] + }, + { + "type": "SumReduce", + "alias": "ENSG00000267443_effect_sum", + "input_alias": "ENSG00000267443_effects" + }, + { + "type": "StandardScaler", + "alias": "ENSG00000267443_scaled", + "input_alias": "ENSG00000267443_effect_sum" + }, + { + "type": "Sigmoid", + "alias": "ENSG00000267443_damped_effect", + "input_alias": "ENSG00000267443_scaled" + }, + { + "type": "Distribution", + "alias": "ENSG00000267443_promoter_multiplier", + "input_match_size": "ENSG00000267443_damped_effect", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.8, + "scale": 0.025 + } + }, + { + "type": "Product", + "alias": "ENSG00000267443_effect_with_promoter", + "input_aliases": [ + "ENSG00000267443_damped_effect", + "ENSG00000267443_promoter_multiplier" + ] + }, + { + "type": "IfElse", + "alias": "ENSG00000267443_haplotype_values", + "input_cond_vals": "ENSG00000267443_promoter", + "input_if_vals": "ENSG00000267443_effect_with_promoter", + "input_else_vals": "ENSG00000267443_damped_effect" + }, + { + "type": "MeanCombine", + "alias": "ENSG00000267443_no_noise", + "input_alias": "ENSG00000267443_haplotype_values", + "mean_type": "geometric" + }, + { + "type": "Heritability", + "alias": "ENSG00000267443", + "input_alias": "ENSG00000267443_no_noise", + "heritability": 0.8 + }, + { + "type": "RandomConstant", + "alias": "CYP4F12_common_betas", + "input_match_size": "CYP4F12_common", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.0, + "scale": 0.2 + }, + "by_feat": true + }, + { + "type": "RandomConstant", + "alias": "CYP4F12_rare_betas", + "input_match_size": "CYP4F12_rare", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.0, + "scale": 0.4 + }, + "by_feat": true + }, + { + "type": "ReLU", + "alias": "CYP4F12_rare_betas_pos", + "input_alias": "CYP4F12_rare_betas", + "neg_slope": -1.0 + }, + { + "type": "Product", + "alias": "CYP4F12_common_effects", + "input_aliases": [ + "CYP4F12_common_betas", + "CYP4F12_common" + ] + }, + { + "type": "Product", + "alias": "CYP4F12_rare_effects", + "input_aliases": [ + "CYP4F12_rare_betas_pos", + "CYP4F12_rare" + ] + }, + { + "type": "Concatenate", + "alias": "CYP4F12_effects", + "input_aliases": [ + "CYP4F12_common_effects", + "CYP4F12_rare_effects" + ] + }, + { + "type": "SumReduce", + "alias": "CYP4F12_hap_val", + "input_alias": "CYP4F12_effects" + }, + { + "type": "Tanh", + "alias": "CYP4F12_hap_val_damped", + "input_alias": "CYP4F12_hap_val" + }, + { + "type": "MaxCombine", + "alias": "CYP4F12_max", + "input_alias": "CYP4F12_hap_val_damped" + }, + { + "type": "MeanCombine", + "alias": "CYP4F12_mean", + "input_alias": "CYP4F12_hap_val_damped" + }, + { + "type": "Concatenate", + "alias": "CYP4F12_comb_vals", + "input_aliases": [ + "CYP4F12_max", + "CYP4F12_mean" + ] + }, + { + "type": "Constant", + "alias": "CYP4F12_comb_weights", + "constant": [ + 0.8, + 0.2 + ], + "input_match_size": "CYP4F12_comb_vals" + }, + { + "type": "Product", + "alias": "CYP4F12_comb_vals_weighted", + "input_aliases": [ + "CYP4F12_comb_vals", + "CYP4F12_comb_weights" + ] + }, + { + "type": "MeanReduce", + "alias": "CYP4F12_no_noise", + "input_alias": "CYP4F12_comb_vals_weighted" + }, + { + "type": "Heritability", + "alias": "CYP4F12", + "input_alias": "CYP4F12_no_noise", + "heritability": 0.8 + }, + { + "type": "RandomConstant", + "alias": "EID2B_common_betas", + "input_match_size": "EID2B_common", + "dist_name": "uniform", + "dist_kwargs": { + "low": 0.01, + "high": 0.25 + }, + "by_feat": true + }, + { + "type": "Product", + "alias": "EID2B_common_effects", + "input_aliases": [ + "EID2B_common_betas", + "EID2B_common" + ] + }, + { + "type": "SumReduce", + "alias": "EID2B_common_effects_sum", + "input_alias": "EID2B_common_effects" + }, + { + "type": "GaussianNoise", + "alias": "EID2B_common_effects_sum_w_noise_unbounded", + "input_alias": "EID2B_common_effects_sum", + "noise_std": 0.025 + }, + { + "type": "Clip", + "alias": "EID2B_common_effects_sum_w_noise", + "input_alias": "EID2B_common_effects_sum_w_noise_unbounded", + "min_val": 0 + }, + { + "type": "RandomConstant", + "alias": "EID2B_deleterious_multiplier", + "input_match_size": "EID2B_deleterious", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.3, + "scale": 0.05 + }, + "by_feat": true + }, + { + "type": "Constant", + "alias": "constant_1", + "input_match_size": "EID2B_deleterious", + "constant": 1.0 + }, + { + "type": "IfElse", + "alias": "EID2B_multiplier_by_variant", + "input_cond_vals": "EID2B_deleterious", + 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0.05 + } + ] +} \ No newline at end of file diff --git a/paper/pheno_sim/sim_configs/lin_add_nvar10_h1_v1.json b/paper/pheno_sim/sim_configs/lin_add_nvar10_h1_v1.json new file mode 100644 index 0000000..198164b --- /dev/null +++ b/paper/pheno_sim/sim_configs/lin_add_nvar10_h1_v1.json @@ -0,0 +1,65 @@ +{ + "input": [ + { + "file": "../data/ukb/vcf/phased_chr19.vcf", + "file_format": "vcf", + "reference_genome": "GRCh37", + "input_nodes": [ + { + "alias": "variants", + "type": "SNP", + "chr": "19", + "pos": [ + 37933936, + 54596619, + 58547516, + 19304899, + 41356246, + 35615179, + 2238667, + 50271502, + 29368930, + 49929193 + ] + } + ] + } + ], + "simulation_steps": [ + { + "type": "RandomConstant", + "alias": "variants_betas", + "input_match_size": "variants", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.0, + "scale": 1.0 + }, + "by_feat": true + }, + { + "type": "Product", + "alias": "variant_effects", + "input_aliases": [ + "variants_betas", + "variants" + ] + }, + { + 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] +} \ No newline at end of file diff --git a/paper/pheno_sim/sim_configs/lin_add_nvar10_h8_v1.json b/paper/pheno_sim/sim_configs/lin_add_nvar10_h8_v1.json new file mode 100644 index 0000000..49dc107 --- /dev/null +++ b/paper/pheno_sim/sim_configs/lin_add_nvar10_h8_v1.json @@ -0,0 +1,65 @@ +{ + "input": [ + { + "file": "../data/ukb/vcf/phased_chr19.vcf", + "file_format": "vcf", + "reference_genome": "GRCh37", + "input_nodes": [ + { + "alias": "variants", + "type": "SNP", + "chr": "19", + "pos": [ + 37933936, + 54596619, + 58547516, + 19304899, + 41356246, + 35615179, + 2238667, + 50271502, + 29368930, + 49929193 + ] + } + ] + } + ], + "simulation_steps": [ + { + "type": "RandomConstant", + "alias": "variants_betas", + "input_match_size": "variants", + "dist_name": "normal", + "dist_kwargs": { + "loc": 0.0, + "scale": 1.0 + }, + "by_feat": true + }, + { + "type": "Product", + "alias": "variant_effects", + "input_aliases": [ + "variants_betas", + "variants" + ] + }, + { + "type": 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"input_cond_vals": "Gene2_vars", + "input_if_vals": "Gene2_mult_vals", + "input_else_vals": "Gene2_ones_mat" + }, + { + "type": "ProductReduce", + "alias": "Gene2_mult_var_term", + "input_alias": "Gene2_mult_actual_vals" + }, + { + "type": "Product", + "alias": "Gene2_haps", + "input_aliases": [ + "Gene2_mult_var_term", + "Gene2_background" + ] + }, + { + "type": "Tanh", + "alias": "Gene2_haps_tanh", + "input_alias": "Gene2_haps" + }, + { + "type": "MaxCombine", + "alias": "Gene2", + "input_alias": "Gene2_haps_tanh" + }, + + { + "type": "Distribution", + "alias": "Gene3_background", + "input_match_size": "n_samples", + "dist_name": "normal", + "dist_kwargs": { + "loc": 1.0, + "scale": 0.2 + } + }, + { + "type": "RandomConstant", + "alias": "Gene3_mult_vals", + "input_match_size": "Gene3_vars", + "dist_name": "normal", + "dist_kwargs": { + "loc": 1.5, + "scale": 1.0 + }, + "by_feat": true + }, + { + "type": "Constant", + "alias": "Gene3_ones_mat", + "constant": 1.0, + "input_match_size": "Gene3_vars" + }, + { + "type": "IfElse", + "alias": "Gene3_mult_actual_vals", + "input_cond_vals": "Gene3_vars", + "input_if_vals": "Gene3_mult_vals", + "input_else_vals": "Gene3_ones_mat" + }, + { + "type": "ProductReduce", + "alias": "Gene3_mult_var_term", + "input_alias": "Gene3_mult_actual_vals" + }, + { + "type": "Product", + "alias": "Gene3_haps", + "input_aliases": [ + "Gene3_mult_var_term", + "Gene3_background" + ] + }, + { + "type": "Sigmoid", + "alias": "Gene3_haps_tanh", + "input_alias": "Gene3_haps" + }, + { + "type": "MeanCombine", + "alias": "Gene3", + "input_alias": "Gene3_haps_tanh" + }, + + { + "type": "Distribution", + "alias": "Gene4_background", + "input_match_size": "n_samples", + "dist_name": "normal", + "dist_kwargs": { + "loc": 1.0, + "scale": 0.1 + } + }, + { + "type": "RandomConstant", + "alias": "Gene4_mult_vals", + "input_match_size": "Gene4_vars", + "dist_name": "normal", + "dist_kwargs": { + "loc": 1.2, + "scale": 0.6 + }, + "by_feat": true + }, + { + "type": "Constant", + "alias": "Gene4_ones_mat", + "constant": 1.0, + "input_match_size": "Gene4_vars" + }, + { + "type": "IfElse", + "alias": "Gene4_mult_actual_vals", + "input_cond_vals": "Gene4_vars", + "input_if_vals": "Gene4_mult_vals", + "input_else_vals": "Gene4_ones_mat" + }, + { + "type": "ProductReduce", + "alias": "Gene4_mult_var_term", + "input_alias": "Gene4_mult_actual_vals" + }, + { + "type": "Product", + "alias": "Gene4_haps", + "input_aliases": [ + "Gene4_mult_var_term", + "Gene4_background" + ] + }, + { + "type": "MinCombine", + "alias": "Gene4", + "input_alias": "Gene4_haps" + }, + + { + "type": "Product", + "alias": "G1_G2", + "input_aliases": [ + "Gene1", + "Gene2" + ] + }, + { + "type": "Product", + "alias": "G3_G4", + "input_aliases": [ + "Gene3", + "Gene4" + ] + }, + + { + "type": "Concatenate", + "alias": "gene_vals", + "input_aliases": [ + "Gene1", + "Gene2", + "Gene3", + "Gene4" + ] + }, + { + "type": "Softmax", + "alias": "gene_softmax", + "input_alias": "gene_vals" + }, + { + "type": "Concatenate", + "alias": "gene_vals_mixed", + "input_aliases": [ + "Gene4", + "Gene3", + "Gene2", + "Gene1" + ] + }, + { + "type": "Product", + "alias": "softmax_x_gene_vals", + "input_aliases": [ + "gene_vals_mixed", + "gene_softmax" + ] + }, + { + "type": "SumReduce", + "alias": "softmax_x_gene_sum", + "input_alias": "softmax_x_gene_vals" + }, + + { + "type": "Sum", + "alias": "no_herit_phenotype", + "input_aliases": [ + "G1_G2", + "G3_G4", + "softmax_x_gene_sum" + ] + }, + { + "type": "Heritability", + "alias": "phenotype", + "input_alias": "no_herit_phenotype", + "heritability": 0.5 + } + ] +} \ No newline at end of file diff --git a/paper/pheno_sim/sim_configs/mult_pheno_2.png b/paper/pheno_sim/sim_configs/mult_pheno_2.png new file mode 100644 index 0000000..6e9ccc9 Binary files /dev/null and b/paper/pheno_sim/sim_configs/mult_pheno_2.png differ diff --git a/paper/pheno_sim/sim_configs/xor_pheno_1.json b/paper/pheno_sim/sim_configs/xor_pheno_1.json new file mode 100644 index 0000000..a421687 --- /dev/null +++ b/paper/pheno_sim/sim_configs/xor_pheno_1.json @@ -0,0 +1,69 @@ +{ + "input": [ + { + "file": "../data/geno_data/vcf/phased_chr19.vcf", + "file_format": "vcf", + "reference_genome": "GRCh37", + "input_nodes": [ + { + "alias": "var_1", + "type": "SNP", + "chr": "19", + "pos": [ + 24368053 + ] + }, + { + "alias": "var_2", + "type": "SNP", + "chr": "19", + "pos": [ + 46408356 + ] + } + ], + "sample_id_field": "s" + } + ], + "simulation_steps": [ + { + "type": "Constant", + "alias": "XOR_true", + "constant": 1.0, + "input_match_size": "var_1" + }, + { + "type": "Constant", + "alias": "XOR_false", + "constant": 0.0, + "input_match_size": "var_1" + }, + { + "type": "Concatenate", + "alias": "vars", + "input_aliases": [ + "var_1", + "var_2" + ] + }, + { + "type": "SumReduce", + "alias": "vars_sum", + "input_alias": "vars" + }, + { + "type": "IfElse", + "alias": "hap_val", + "input_cond_vals": "vars_sum", + "input_if_vals": "XOR_true", + "input_else_vals": "XOR_false", + "threshold": 1.0, + "comparison": "eq" + }, + { + "type": "MeanCombine", + "alias": "phenotype", + "input_alias": "hap_val" + } + ] +} \ No newline at end of file diff --git a/paper/pheno_sim/sim_configs/xor_pheno_1.png b/paper/pheno_sim/sim_configs/xor_pheno_1.png new file mode 100644 index 0000000..8b37205 Binary files /dev/null and b/paper/pheno_sim/sim_configs/xor_pheno_1.png differ diff --git a/paper/pheno_sim/sim_configs/xor_pheno_2.json b/paper/pheno_sim/sim_configs/xor_pheno_2.json new file mode 100644 index 0000000..7bbe440 --- /dev/null +++ b/paper/pheno_sim/sim_configs/xor_pheno_2.json @@ -0,0 +1,69 @@ +{ + "input": [ + { + "file": "../data/geno_data/vcf/phased_chr19.vcf", + "file_format": "vcf", + "reference_genome": "GRCh37", + "input_nodes": [ + { + "alias": "var_1", + "type": "SNP", + "chr": "19", + "pos": [ + 24368053 + ] + }, + { + "alias": "var_2", + "type": "SNP", + "chr": "19", + "pos": [ + 24451636 + ] + } + ], + "sample_id_field": "s" + } + ], + "simulation_steps": [ + { + "type": "Constant", + "alias": "XOR_true", + "constant": 1.0, + "input_match_size": "var_1" + }, + { + "type": "Constant", + "alias": "XOR_false", + "constant": 0.0, + "input_match_size": "var_1" + }, + { + "type": "Concatenate", + "alias": "vars", + "input_aliases": [ + "var_1", + "var_2" + ] + }, + { + "type": "SumReduce", + "alias": "vars_sum", + "input_alias": "vars" + }, + { + "type": "IfElse", + "alias": "hap_val", + "input_cond_vals": "vars_sum", + "input_if_vals": "XOR_true", + "input_else_vals": "XOR_false", + "threshold": 1.0, + "comparison": "eq" + }, + { + "type": "MeanCombine", + "alias": "phenotype", + "input_alias": "hap_val" + } + ] +} \ No newline at end of file diff --git a/paper/pheno_sim/simulate_lin_add_pheno.sh b/paper/pheno_sim/simulate_lin_add_pheno.sh new file mode 100755 index 0000000..79c2083 --- /dev/null +++ b/paper/pheno_sim/simulate_lin_add_pheno.sh @@ -0,0 +1,28 @@ +# Simulate phenotypes using CITRUS + +OUTPUT_DIR=citrus_output + +N_VARS=(10 100 1000) +H_VALS=(05 1 2 4 8) +VERSION_NUM=1 + +# Loop over number of variables and heritability +for N_VAR in "${N_VARS[@]}"; do + for H_VAL in "${H_VALS[@]}"; do + SIM_NAME=lin_add_nvar${N_VAR}_h${H_VAL}_v${VERSION_NUM} + SIM_CONFIG_FILE=sim_configs/${SIM_NAME} + + # Run citrus simulate + + JAVA_HOME=/home/rdevito/anaconda3/envs/citrus_env/ + + citrus simulate \ + -c ${SIM_CONFIG_FILE} \ + -o ${OUTPUT_DIR} \ + -f ${SIM_NAME}.csv \ + --output_config_filename ${SIM_NAME}_config.json + + # Rewrite phenotype in plink format + python citrus_out_to_plink_fmt.py ${SIM_NAME} + done +done \ No newline at end of file diff --git a/paper/workflows/GWAS/run_GWAS.sh b/paper/workflows/GWAS/run_GWAS.sh new file mode 100755 index 0000000..1557627 --- /dev/null +++ b/paper/workflows/GWAS/run_GWAS.sh @@ -0,0 +1,69 @@ +# Run Plink2 GWAS on CITRUS simulated phenotype + +# PHENO=complex_pheno_1 +# PHENO=complex_pheno_1_noise +# PHENO=complex_pheno_2 +# PHENO=complex_pheno_3 +# PHENO=complex_pheno_4 +# PHENO=mult_pheno_1_more_noise +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=lin_add_nvar100_h05_v1 +# PHENO=lin_add_nvar100_h1_v1 +# PHENO=lin_add_nvar100_h2_v1 +# PHENO=lin_add_nvar100_h4_v1 +# PHENO=lin_add_nvar100_h8_v1 +# PHENO=lin_add_nvar1000_h2_v1 +# PHENO=lin_add_nvar1000_h4_v1 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +# PHENO=gepsi_lin_add_h0625 +# PHENO=gepsi_lin_add_h125 +# PHENO=gepsi_lin_add_h25 +# PHENO=gepsi_lin_add_h5 +# PHENO=gepsi_lin_add_hFULL +# PHENO=gepsi_lin_add_n10_hFULL +PHENO=gepsi_lin_add_n2_h001 + +# PHENO=gepsi_lin_add_h0625_gcta +# PHENO=gepsi_lin_add_h125_gcta +# PHENO=gepsi_lin_add_h25_gcta +# PHENO=gepsi_lin_add_h5_gcta +# PHENO=gepsi_lin_add_hFULL_gcta + +# PHENO_DIR=pheno_sim/citrus_phenos +PHENO_DIR=output/gepsi_phenos +# PHENO_DIR=output/gcta_phenos + +echo ${PHENO} +echo ${PHENO_DIR} + +SPLITS_DIR=data/splits +TRAIN_SPLIT_FNAME=train_samples.txt +GENO_DIR=data/geno_data/pgen +PGEN_FNAME=imputed_chr19 +PC_FNAME=data/geno_data/PCs/pcs.tsv + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_gwas_plink2:latest + +# Start docker env +module load singularitypro + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + plink2 --glm 'hide-covar' \ + --pfile ${GENO_DIR}/${PGEN_FNAME} \ + --keep ${SPLITS_DIR}/${TRAIN_SPLIT_FNAME} \ + --pheno ${PHENO_DIR}/${PHENO}.pheno \ + --covar ${PC_FNAME} \ + --out output/GWAS/${PHENO}" \ No newline at end of file diff --git a/paper/workflows/aml_basil_vars/fit_automl_prs.py b/paper/workflows/aml_basil_vars/fit_automl_prs.py new file mode 100644 index 0000000..9ef1245 --- /dev/null +++ b/paper/workflows/aml_basil_vars/fit_automl_prs.py @@ -0,0 +1,472 @@ +"""Fit AutoML PRS model. + +Underlying model is either a (possibly regularized) linear regression +or a LightGBM model. Specifics of the model are defined in the model +configuration JSON file. + +Requires: + + - A parquet file containing the genotype data. + - A JSON file containing the variant subsets for different p-value + and window size combinations. + - A tab-separated file containing the phenotype data. + - A tab-separated file containing the covariate data. + - One ID per line text files containing the IDs of the samples to use + for the training, validation, and test sets. + - A model configuration JSON file. + +Training configuration JSON file requires the keys: + + - model_type (str): If specifying one p-value and window size threshold, + one of 'lgbm', 'elastic_net', or 'npart_elastic_net'. If searching + over multiple thresholds, one of 'lgbm_multi_thresh'. TODO: linear versions + - metric (str): Valid metric with FLAML see https://microsoft.github.io/FLAML/docs/Use-Cases/Task-Oriented-AutoML#optimization-metric + Valid options include 'r2', 'mae', 'mse', 'rmse', 'mape'. + - task (str): One of 'regression', 'classification'. + - time_budget (int): Time budget in seconds for the model fitting. + - early_stopping (bool): Whether to use early stopping. If not provided, + defaults to False. + +And these keys are required if not using a 'multi_thresh' model: + + - p_val (str): p-value threshold for variant inclusion. Must correspond + to a key in the variant subsets JSON file. + - window (str): Window size for variant inclusion. Must correspond to a + key under the p_val key in the variant subsets JSON file. + +Outputs: + + - The training configuration JSON file ('training_config.json'). + - Best model configuration as a JSON file ('best_model_config.json'). + - Predictions for the validation and test sets as CSV files named + 'val_preds.csv' and 'test_preds.csv'. + - A JSON file 'runtime.json' with the runtime of the model fitting in + seconds under the the key 'runtime_seconds'. + - The log file from FLAML AutoML.fit() ('fit.log'). + +Args: + + * -t, --training-config: Path to the training configuration JSON file. + * -g, --geno-parquet: Path to the parquet file containing the genotype + data. + * -s, --var-subsets: Path to the JSON file containing the variant + subsets for different p-value and window size combinations. + * -p, --pheno: Path to the tab-separated file containing the phenotype + data. + * -c, --covars: Path to the tab-separated file containing the covariate + data. + * --train-ids: Path to the text file containing the IDs of the + samples to use for the training set. + * --val-ids: Path to the text file containing the IDs of the + samples to use for the validation set. + * --test-ids: Path to the text file containing the IDs of the + samples to use for the test set. + * -o, --out-dir: Directory in which to save the output files. + Default: '.'. + * -i, --id-col: Name of the column in the genotype data that contains + the sample IDs. Default: 'IID'. + + +python fit_automl_prs.py -g ../dev_data/geno.parquet -v ../dev_data/var_subsets.json -p ../dev_data/sim_pheno.tsv -c ../dev_data/covars.tsv --train-ids ../dev_data/train_ids.txt --val-ids ../dev_data/val_ids.txt --test-ids ../dev_data/test_ids.txt -t ../dev_data/basic_config.json -o ../dev_data/output +""" + +import argparse +import json +import pickle +import os +import time +from pprint import pprint + +import numpy as np +import pandas as pd +import polars as pl +import matplotlib.pyplot as plt +from flaml import AutoML +from flaml.automl.data import get_output_from_log +from flaml import tune + +from automl_prs import PRSTask + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser( + description='Fit AutoML PRS model.' + ) + parser.add_argument( + '-t', '--training-config', + required=True, + help='Path to the training configuration JSON file.' + ) + parser.add_argument( + '-g', '--geno-parquet', + required=True, + help='Path to the parquet file containing the genotype data.' + ) + parser.add_argument( + '-v', '--var-subsets', + required=True, + help='Path to the JSON file containing the variant subsets for ' + 'different p-value and window size combinations.' + ) + parser.add_argument( + '-p', '--pheno', + required=True, + help='Path to the tab-separated file containing the phenotype data.' + ) + parser.add_argument( + '-c', '--covars', + required=True, + help='Path to the tab-separated file containing the covariate data.' + ) + parser.add_argument( + '--train-ids', + required=True, + help='Path to the text file containing the IDs of the samples to ' + 'use for the training set.' + ) + parser.add_argument( + '--val-ids', + required=True, + help='Path to the text file containing the IDs of the samples to ' + 'use for the validation set.' + ) + parser.add_argument( + '--test-ids', + required=True, + help='Path to the text file containing the IDs of the samples to ' + 'use for the test set.' + ) + parser.add_argument( + '-o', '--out-dir', + default='.', + help='Directory in which to save the output files.' + ) + parser.add_argument( + '-i', '--id-col', + default='IID', + help='Name of the column in the genotype data that contains the ' + 'sample IDs.' + ) + return parser.parse_args() + + +def main(): + args = parse_args() + print('Args:', flush=True) + pprint(vars(args)) + + # Load model configuration + print('Loading training configuration...', flush=True) + with open(args.training_config, 'r') as f: + training_config = json.load(f) + + if 'early_stopping' not in training_config: + training_config['early_stopping'] = False + + multi_thresh = training_config['model_type'].endswith('multi_thresh') + + # Save training config to out_dir + with open(os.path.join(args.out_dir, 'training_config.json'), 'w') as f: + json.dump(training_config, f, indent=4) + + # Load sample ID sets as lists + print('Loading sample ID sets...', flush=True) + with open(args.train_ids, 'r') as f: + train_ids = f.read().splitlines() + with open(args.val_ids, 'r') as f: + val_ids = f.read().splitlines() + with open(args.test_ids, 'r') as f: + test_ids = f.read().splitlines() + + # Load variant subsets and create hyperparameter space + print('Loading variant subsets...', flush=True) + with open(args.var_subsets, 'r') as f: + var_subsets = json.load(f) + + if multi_thresh: + # Flatten var_subsets by combining p-value and window size keys + new_var_subsets = dict() + for p_val in var_subsets: + for window in var_subsets[p_val]: + new_var_subsets[ + f"p-val:{p_val}, window:{window}" + ] = var_subsets[p_val][window] + var_subsets = new_var_subsets + + cutoff_keys = list(var_subsets.keys()) + # cutoff_counts = [len(var_subsets[k]) for k in cutoff_keys] + + cutoff_hp_space = { + 'filter_threshold': { + 'domain': tune.choice(cutoff_keys), + # 'low_cost_init_value': cutoff_keys[np.argmin(cutoff_counts)], + # 'cat_hp_cost': np.log(cutoff_counts).tolist(), + } + } + else: + included_vars = var_subsets[training_config['p_val']][training_config['window']] + print(f'\tIncluded variants: {len(included_vars)}', flush=True) + + # Load phenotype and covariate data + print('Loading phenotype data...', flush=True) + pheno_df = pl.read_csv( + args.pheno, + separator='\t', + ) + + pheno_df_cols = [c for c in pheno_df.columns if c != args.id_col] + assert len(pheno_df_cols) == 1, 'Phenotype file must have exactly one phenotype column.' + pheno_name = pheno_df_cols[0] + + print('Loading covariate data...', flush=True) + covars_df = pl.read_csv( + args.covars, + separator='\t', + ) + covar_names = [col for col in covars_df.columns if col != args.id_col] + + # Load genotype data for samples in the training and validation sets + print('Scanning genotype data...', flush=True) + geno_lazy = pl.scan_parquet(args.geno_parquet) + + print('Loading training genotype data...', flush=True) + if multi_thresh: + train_geno_df = geno_lazy.filter( + pl.col(args.id_col).is_in(train_ids) + ).collect(streaming=True) + else: + train_geno_df = geno_lazy.filter( + pl.col(args.id_col).is_in(train_ids) + ).select( + [args.id_col] + included_vars + ).collect(streaming=True) + + print(train_geno_df) + + print('Loading validation genotype data...', flush=True) + if multi_thresh: + val_geno_df = geno_lazy.filter( + pl.col(args.id_col).is_in(val_ids) + ).collect(streaming=True) + else: + val_geno_df = geno_lazy.filter( + pl.col(args.id_col).is_in(val_ids) + ).select( + [args.id_col] + included_vars + ).collect(streaming=True) + + train_df = train_geno_df.join( + pheno_df, + args.id_col + ).join( + covars_df, + args.id_col + ) + train_ids = train_df[args.id_col].to_numpy() + train_labels = train_df[pheno_name].to_numpy() + + train_df = train_df.drop(args.id_col, pheno_name) + + val_df = val_geno_df.join( + pheno_df, + args.id_col + ).join( + covars_df, + args.id_col + ) + val_ids = val_df[args.id_col].to_numpy() + val_labels = val_df[pheno_name].to_numpy() + + val_df = val_df.drop(args.id_col, pheno_name) + + # Fit AutoML PRS model + + # Create AutoML object and set default parameters + automl = AutoML( + auto_augment=False, + hpo_method='bs', # BlendSearch + log_type='all', + log_training_metric=True, + skip_transform=True, + retrain_full=False, + sample=False, + early_stop=True, + starting_points='static', + verbose=3, + ensemble=False, + ) + + # Fit model + start_time = time.time() + + if multi_thresh: + automl.fit( + X_train=train_df, + y_train=train_labels, + X_val=val_df, + y_val=val_labels, + task=PRSTask(training_config['task']), + estimator_list=[training_config['model_type']], + custom_hp={ + training_config['model_type']: cutoff_hp_space + }, + fit_kwargs_by_estimator={ + training_config['model_type']: { + 'var_sets_map': var_subsets, + 'covar_cols': covar_names, + 'print_params': True, + } + }, + time_budget=training_config['time_budget'], + early_stop=training_config['early_stopping'], + metric=training_config['metric'], + log_file_name=os.path.join(args.out_dir, 'fit.log') + ) + else: + automl.fit( + X_train=train_df, + y_train=train_labels, + X_val=val_df, + y_val=val_labels, + task=PRSTask(training_config['task']), + estimator_list=[training_config['model_type']], + fit_kwargs_by_estimator={ + training_config['model_type']: { + 'print_params': True, + } + }, + time_budget=training_config['time_budget'], + early_stop=training_config['early_stopping'], + metric=training_config['metric'], + log_file_name=os.path.join(args.out_dir, 'fit.log') + ) + + end_time = time.time() + runtime_seconds = end_time - start_time + print( + f'\nModel fitting runtime: {runtime_seconds / 3600:.2f} hours', + flush=True + ) + + # Save runtime to JSON + with open(os.path.join(args.out_dir, 'runtime.json'), 'w') as f: + json.dump({'runtime_seconds': runtime_seconds}, f) + + # Print best model + print(f'\nBest model: {automl.best_estimator}', flush=True) + pprint(automl.best_config) + + # Save best model config as JSON + with open(os.path.join(args.out_dir, 'best_model_config.json'), 'w') as f: + json.dump({ + 'best_estimator': automl.best_estimator, + 'best_config': automl.best_config, + }, + f, + indent=4 + ) + + # Save best model + with open(os.path.join(args.out_dir, 'best_model.pkl'), 'wb') as f: + pickle.dump(automl.model, f, protocol=4) + + # Plot learning curve + ( + time_history, best_valid_loss_history, valid_loss_history, + config_history, metric_history + ) = get_output_from_log( + os.path.join(args.out_dir, 'fit.log'), + time_budget=training_config['time_budget'], + ) + + if training_config['metric'] in {'r2'}: + best_valid_loss_history = 1 - np.array(best_valid_loss_history) + valid_loss_history = 1 - np.array(valid_loss_history) + + # Plot best curve and all results curve on same plot + plt.step( + time_history, + best_valid_loss_history, + where='post', + label='Best Model' + ) + plt.plot( + time_history, + valid_loss_history, + '--', + label='All Models' + ) + plt.ylim( + bottom=0, + top=max(max(best_valid_loss_history), max(valid_loss_history)) * 1.05 + ) + plt.legend() + plt.title('Learning Curve') + plt.xlabel('Wall Clock Time (s)') + plt.ylabel(training_config['metric'].title()) + plt.savefig( + os.path.join(args.out_dir, 'learning_curve.png'), + dpi=200 + ) + plt.close() + + # Load test data and evaluate best model + del train_df, train_labels + + print('Loading test genotype data...', flush=True) + if multi_thresh: + test_geno_df = geno_lazy.filter( + pl.col(args.id_col).is_in(test_ids) + ).collect(streaming=True) + else: + test_geno_df = geno_lazy.filter( + pl.col(args.id_col).is_in(test_ids) + ).select( + [args.id_col] + included_vars + ).collect(streaming=True) + + # Join genotype data with phenotype and covariate data, then pop labels + # and sample IDs + test_df = test_geno_df.join( + pheno_df, + args.id_col + ).join( + covars_df, + args.id_col + ) + test_ids = test_df[args.id_col].to_numpy() + # test_labels = test_df[pheno_name].to_numpy() + + test_df = test_df.drop(args.id_col, pheno_name) + + # Make predictions for validation and test sets + print( + '\nPredicting for validation and test sets with best model', + flush=True + ) + val_preds = automl.predict(val_df) + test_preds = automl.predict(test_df) + + # Save predictions as CSV files with sample IDs + val_preds_df = pd.DataFrame({ + args.id_col: val_ids, + 'pred': val_preds, + }) + val_preds_df.to_csv( + os.path.join(args.out_dir, 'val_preds.csv'), + index=False + ) + + test_preds_df = pd.DataFrame({ + args.id_col: test_ids, + 'pred': test_preds, + }) + test_preds_df.to_csv( + os.path.join(args.out_dir, 'test_preds.csv'), + index=False + ) + + +if __name__ == '__main__': + main() diff --git a/paper/workflows/aml_basil_vars/lgbm_v0h1_basil.json b/paper/workflows/aml_basil_vars/lgbm_v0h1_basil.json new file mode 100644 index 0000000..6c833e3 --- /dev/null +++ b/paper/workflows/aml_basil_vars/lgbm_v0h1_basil.json @@ -0,0 +1,9 @@ +{ + "model_type": "lgbm", + "metric": "r2", + "task": "regression", + "time_budget": 900, + "early_stopping": true, + "p_val": "basil", + "window": "included" +} \ No newline at end of file diff --git a/paper/workflows/aml_basil_vars/run_aml_basil_vars.sh b/paper/workflows/aml_basil_vars/run_aml_basil_vars.sh new file mode 100755 index 0000000..2ab34d6 --- /dev/null +++ b/paper/workflows/aml_basil_vars/run_aml_basil_vars.sh @@ -0,0 +1,81 @@ +#!/bin/bash + +# Run PRSice2 PRS on CITRUS simulated phenotype + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +# PHENO=gepsi_lin_add_h0625 +# PHENO=gepsi_lin_add_h125 +# PHENO=gepsi_lin_add_h25 +# PHENO=gepsi_lin_add_h5 +# PHENO=gepsi_lin_add_hFULL + +# PHENO=gepsi_lin_add_h0625_gcta +# PHENO=gepsi_lin_add_h125_gcta +# PHENO=gepsi_lin_add_h25_gcta +# PHENO=gepsi_lin_add_h5_gcta +PHENO=gepsi_lin_add_hFULL_gcta + +# PHENO_DIR=pheno_sim/citrus_phenos +# PHENO_DIR=output/gepsi_phenos +PHENO_DIR=output/gcta_phenos + +echo ${PHENO} +echo ${PHENO_DIR} + +TEMP_DIR=/tmp +OUT_DIR=output/aml_basil_vars/${PHENO} +FILTER_OUT_DIR=output/aml_filter_vars_basil/${PHENO} + +SPLITS_DIR=data/splits +TRAIN_SPLIT_FNAME=train_samples.txt +VAL_SPLIT_FNAME=val_samples.txt +TEST_SPLIT_FNAME=test_samples.txt +GENO_DIR=data/geno_data/pgen +PGEN_FNAME=imputed_chr19_1ID +PC_DIR=data/geno_data/PCs + +BASIL_INCL_FILE=output/basil/${PHENO}/included_features.csv + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_aml:latest + +# Start docker env +module load singularitypro + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + mkdir -p ${OUT_DIR} && \ + awk 'NR==1 {print \"IID\tphenotype\"; next} {print \$1\"_\"\$2\"\t\"\$3}' \ + ${PHENO_DIR}/${PHENO}.pheno > ${OUT_DIR}/${PHENO}_1ID.pheno \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${TRAIN_SPLIT_FNAME} > ${TEMP_DIR}/${TRAIN_SPLIT_FNAME} \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${VAL_SPLIT_FNAME} > ${TEMP_DIR}/${VAL_SPLIT_FNAME} \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${TEST_SPLIT_FNAME} > ${TEMP_DIR}/${TEST_SPLIT_FNAME} \ + && \ + python3 workflows/aml_basil_vars/fit_automl_prs.py \ + --training-config /mnt/citrus/CITRUS/paper/workflows/aml_basil_vars/lgbm_v0h1_basil.json \ + --geno-parquet ${FILTER_OUT_DIR}/filtered_vars.parquet \ + --var-subsets ${FILTER_OUT_DIR}/filtered_vars.json \ + --pheno ${OUT_DIR}/${PHENO}_1ID.pheno \ + --covars ${PC_DIR}/pcs_1ID.tsv \ + --train-ids ${TEMP_DIR}/${TRAIN_SPLIT_FNAME} \ + --val-ids ${TEMP_DIR}/${VAL_SPLIT_FNAME} \ + --test-ids ${TEMP_DIR}/${TEST_SPLIT_FNAME} \ + -o ${OUT_DIR} " \ No newline at end of file diff --git a/paper/workflows/aml_filter_vars/filter_vars_by_basil.py b/paper/workflows/aml_filter_vars/filter_vars_by_basil.py new file mode 100644 index 0000000..00730f5 --- /dev/null +++ b/paper/workflows/aml_filter_vars/filter_vars_by_basil.py @@ -0,0 +1,119 @@ +"""Filter variants for inclusion as PRS model using just SNPs used by BASIL. + +Saves variant IDs to a text file, which can be used as input to plink +--extract to filter the genotype data to just these variants. + +Has three outputs: + +1. Text file with variant IDs one-per-line are clump lead SNPs. This can be +used as input to plink --extract to filter the genotype data to just these +variants. Will be named 'filtered_vars_all.txt'. + +2. A JSON file in the format of those that store which variants are included +for each p-value and window size combination. Will be named +'filtered_vars_raw.json'. First level key is 'clump', second level key is +'lead', and values are lists of variant IDs. + +3. A meta data JSON file, named 'fitered_vars_meta.json'. The meta data +section includes info on: + - the number of variants that pass each threshold + - the parameters of the filtering process. + +Args: + +* -b, --basil-included-file: Path to the CSV (or one ID per line text file) + containing the variant IDs that were included in the BASIL model. +* -o, --out-dir: Directory in which to save the output files. + Default: '.'. + +Example usage: + +```bash +python filter_vars_by_pval.py \ + -b /path/to/basil_included_file.csv \ + -o /path/to/output_dir +``` +""" + +import argparse +import os +import json +from itertools import product +from collections import defaultdict + +import pandas as pd +from tqdm import tqdm + + +def parse_args(): + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter + ) + parser.add_argument( + '-b', '--basil-included-file', + required=True, + help='Path to the CSV (or one ID per line text file) containing the ' + 'variant IDs that were included in the BASIL model.' + ) + parser.add_argument( + '-o', '--out-dir', + default='.', + help='Directory in which to save the output files. Default: \'.\'.' + ) + + return parser.parse_args() + + +if __name__ == '__main__': + + # Parse command line arguments + args = parse_args() + + # Load BASIL included variants + incl_id_series = pd.read_csv( + args.basil_included_file, + header=None, + ).iloc[:, 0] + + # Drop IDs that do not contain an underscore + incl_id_series = incl_id_series[incl_id_series.str.contains(r'_')] + + # Drop everything after the last underscore + incl_id_series = incl_id_series.apply(lambda x: '_'.join(x.split('_')[:-1])) + + # Get + incl_variants = incl_id_series.to_list() + + # Create output JSON dicts + var_ids_sets = { + "basil": { + "included": incl_variants + } + } + + meta_dict = { + "filtering": { + "basil": True, + }, + } + + # Save all variant IDs that pass any filter + all_sig_variants_file = os.path.join(args.out_dir, 'filtered_vars_all.txt') + with open(all_sig_variants_file, 'w') as f: + f.write('\n'.join(incl_variants)) + + # Add counts to meta data section + meta_dict["filtering"]["n_var_total"] = len(incl_variants) # type: ignore + meta_dict["filtering"]["n_var_threshold"] = { # type: ignore + "basil": {"included": len(incl_variants)} + } + + # Save JSON output + var_ids_sets_file = os.path.join(args.out_dir, 'filtered_vars_raw.json') + with open(var_ids_sets_file, 'w') as f: + json.dump(var_ids_sets, f, indent=4) + + meta_dict_file = os.path.join(args.out_dir, 'filtered_vars_meta.json') + with open(meta_dict_file, 'w') as f: + json.dump(meta_dict, f, indent=4) diff --git a/paper/workflows/aml_filter_vars/filter_vars_by_pval.py b/paper/workflows/aml_filter_vars/filter_vars_by_pval.py new file mode 100644 index 0000000..bf7de60 --- /dev/null +++ b/paper/workflows/aml_filter_vars/filter_vars_by_pval.py @@ -0,0 +1,239 @@ +"""Filter variants for inclusion as PRS model input by Bonferonni corrected +p-value & window size. + +Will only include thresholds with a number of variants less than or equal +to --max-variants. + +Saves superset of all variant IDs that pass any filter to a text file, which +can be used as input to plink --extract to filter the genotype data to just +these variants. + +Has three outputs: + +1. Text file with variant IDs one-per-line that pass all the the p-value +and window size filters (the superset of all pairs of p-value and window +size combinations). This can be used as input to plink --extract to filter +the genotype data to just these variants. Will be named 'filtered_vars_all.txt'. + +2. A JSON file that stores which variants are included for each p-value and +window size combination. Will be named 'filtered_vars_raw.json'. First level +keys are p-value thresholds, second level keys are window sizes, and values are +lists of variant IDs. + +3. A meta data JSON file, named 'fitered_vars_meta.json'. The meta data +section includes info on: + - the number of variants that pass each threshold + - the parameters of the filtering process. + +Args: + +* -s, --sum-stats-file: Path to whitespace delimited summary statistics + file that includes the var_id_col, p_val_col, chrom_col, and pos_col + columns. +* -m, --max-variants: Maximum number of variants to include in the PRS model. + Default: 80,000. +* -p, --pval-thresh: P-value threshold(s) to use for filtering. +* -w, --window-bp: Window size(s) in base pairs around each variant below the + p-value threshold. Default: 0. +* -o, --out-dir: Directory in which to save the output files. + Default: '.'. +* --var-id-col: Name of the column in the summary statistics file that + contains the variant IDs. Default: 'ID'. +* --p-val-col: Name of the column in the summary statistics file that contains + the p-values. Default: 'P'. +* --chrom-col: Name of the column in the summary statistics file that contains + the chromosome numbers. Default: '#CHROM'. +* --pos-col: Name of the column in the summary statistics file that contains + the base pair positions. Default: 'POS'. + +Example usage: + +```bash +python filter_vars_by_pval.py \ + -s /path/to/sum_stats.tsv \ + -p 5e-8 1e-5 0.05 \ + -w 0 10000 100000 \ + -o /path/to/output_dir \ + --var-id-col ID \ + --p-val-col P \ + --chrom-col '#CHROM' \ + --pos-col POS +``` +""" + +import argparse +import os +import json +from itertools import product +from collections import defaultdict + +import pandas as pd +from tqdm import tqdm + + +def parse_args(): + parser = argparse.ArgumentParser( + description=__doc__, + formatter_class=argparse.RawDescriptionHelpFormatter + ) + parser.add_argument( + '-s', '--sum-stats-file', + required=True, + help='Path to summary statistics file that includes the var_id_col, ' + 'p_val_col, chrom_col, and pos_col columns.' + ) + parser.add_argument( + '-m', '--max-variants', + type=int, + default=80_000, + help='Maximum number of variants to include in the PRS model.' + ) + parser.add_argument( + '-p', '--pval-thresh', + nargs='+', + required=True, + help='P-value threshold(s) to use for filtering.' + ) + parser.add_argument( + '-w', '--window-bp', + nargs='+', + type=int, + default=[0], + help='Window size(s) in base pairs around each variant below the ' + 'p-value threshold. Default: 0.' + ) + parser.add_argument( + '-o', '--out-dir', + default='.', + help='Directory in which to save the filtered variant files. Default: ' + '\'\'.' + ) + parser.add_argument( + '--var-id-col', + default='ID', + help='Name of the column in the summary statistics file that contains ' + 'the variant IDs. Default: \'ID\'.' + ) + parser.add_argument( + '--p-val-col', + default='P', + help='Name of the column in the summary statistics file that contains ' + 'the p-values. Default: \'P\'.' + ) + parser.add_argument( + '--chrom-col', + default='#CHROM', + help='Name of the column in the summary statistics file that contains ' + 'the chromosome numbers. Default: \'#CHROM\'.' + ) + parser.add_argument( + '--pos-col', + default='POS', + help='Name of the column in the summary statistics file that contains ' + 'the base pair positions. Default: \'POS\'.' + ) + + return parser.parse_args() + + +if __name__ == '__main__': + + # Parse command line arguments + args = parse_args() + + # Load summary statistics + ss_df = pd.read_csv(args.sum_stats_file, sep='\t') + + # Bonferonni correct p-values + ss_df[args.p_val_col] = ss_df[args.p_val_col] * len(ss_df) + + # Save all variant IDs that pass any filter + all_sig_variants = set() + + # Create output JSON dicts + var_ids_sets = defaultdict(dict) # First level keys are p-value + # thresholds, second level keys are + # window sizes, and values are lists of + # variant IDs + + meta_dict = { + "filtering": { + "p_val_col": args.p_val_col, + "chrom_col": args.chrom_col, + "pos_col": args.pos_col, + "pval_thresh": args.pval_thresh, + "window_bp": args.window_bp, + "max_variants": args.max_variants, + }, + } + + # Loop through each p-value threshold and window size and filter variants + for p_val_thresh_str, window_bp in tqdm( + product(args.pval_thresh, args.window_bp), + desc='Filtering variants', + total=len(args.pval_thresh) * len(args.window_bp) + ): + print(f"Filtering variants for p-value threshold: {p_val_thresh_str} window: {window_bp}") + + # Filter by p-value + pval_thresh = float(p_val_thresh_str) + + sig_variants = ss_df[ss_df[args.p_val_col] < pval_thresh] + + # Check if number of significant variants is less than max_variants + if len(sig_variants) > args.max_variants: + print(f"\tMax number of variants exceeded: {len(sig_variants)} > {args.max_variants}") + continue + + if window_bp > 0: + variant_ids = set(sig_variants[args.var_id_col]) + + # Loop through each significant variant and extract nearby variants + for _, variant in sig_variants.iterrows(): + nearby_variants = ss_df[ + (ss_df[args.chrom_col] == variant[args.chrom_col]) & + (ss_df[args.pos_col] >= variant[args.pos_col] - window_bp) & + (ss_df[args.pos_col] <= variant[args.pos_col] + window_bp) + ] + + variant_ids.update(nearby_variants[args.var_id_col]) + + sig_variants = ss_df[ss_df[args.var_id_col].isin(variant_ids)] + + if len(sig_variants) > args.max_variants: + print(f"\tMax number of variants exceeded: {len(sig_variants)} > {args.max_variants}") + continue + + # Add selected variants to output JSON dict + var_ids_sets[p_val_thresh_str][window_bp] = sig_variants[ + args.var_id_col + ].to_list() + + all_sig_variants.update(sig_variants[args.var_id_col]) + + print(f"Added {len(sig_variants)} variants for p-val: {p_val_thresh_str} window: {window_bp}") + print(f"\tTotal included variants: {len(all_sig_variants)}") + + # Save all variant IDs that pass any filter + all_sig_variants_file = os.path.join(args.out_dir, 'filtered_vars_all.txt') + with open(all_sig_variants_file, 'w') as f: + f.write('\n'.join(all_sig_variants)) + + # Add counts to meta data section + meta_dict["filtering"]["n_var_total"] = len(all_sig_variants) + meta_dict["filtering"]["n_var_threshold"] = defaultdict(dict) + + for p_val_thresh_str in var_ids_sets.keys(): + for window_bp in var_ids_sets[p_val_thresh_str].keys(): + meta_dict["filtering"]["n_var_threshold"][p_val_thresh_str][window_bp] = len( + var_ids_sets[p_val_thresh_str][window_bp] + ) + + # Save JSON output + var_ids_sets_file = os.path.join(args.out_dir, 'filtered_vars_raw.json') + with open(var_ids_sets_file, 'w') as f: + json.dump(var_ids_sets, f, indent=4) + + meta_dict_file = os.path.join(args.out_dir, 'filtered_vars_meta.json') + with open(meta_dict_file, 'w') as f: + json.dump(meta_dict, f, indent=4) diff --git a/paper/workflows/aml_filter_vars/raw_to_input_parquet.py b/paper/workflows/aml_filter_vars/raw_to_input_parquet.py new file mode 100644 index 0000000..61b70fe --- /dev/null +++ b/paper/workflows/aml_filter_vars/raw_to_input_parquet.py @@ -0,0 +1,166 @@ +"""Create parquet file which will be used as input to auto-ML models. + +Each p-value and window size combination will use a subset of the features +from the genotype data. This script saves a JSON file 'filtered_vars.json' +that contains the variant sets for each p-value and window size combination +with names that will match the parquet files. + +IID is used as the sample ID column. + +Args: + +* -f, --filtered-vars-raw: Path to the 'filtered_vars_raw.json' file output by + 'filter_vars_by_pval.py'. This file contains the variant sets for each + p-value and window size combination with names that are based on the + original PGEN file. The .raw file output by plink2 modifies the variant + names to include the allele the dosage is for, which this script corrects + for in the output JSON file. +* -r, --raw-geno: Path to the raw genotype data TSV file. This will be used + to map column names and then create the parquet file. +* -o, --out-dir: Directory in which to save the parquet files. Default: '.'. +* --out-parquet-fname: Name of output parquet file. Default: + 'filtered_vars.parquet'. +* --out-json-fname: Name of output JSON file. Contains updated mapping + of which variants are part of which sets (to match changes by plink2 + export). Default: 'filtered_vars.json'. +""" + +import argparse +import os +import json +from collections import defaultdict + +import pandas as pd +import numpy as np +import polars as pl +from tqdm.autonotebook import tqdm + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser( + description='Create parquet file which will be used as input to ' + 'auto-ML models.' + ) + parser.add_argument( + '-f', '--filtered-vars-raw', + required=True, + help='Path to the \'filtered_vars_raw.json\' file output by ' + '\'filter_vars_by_pval.py\'. This file contains the variant sets ' + 'for each p-value and window size combination with names that are ' + 'based on the original PGEN file. The .raw file output by plink2 ' + 'modifies the variant names to include the allele the dosage is ' + 'for, which this script corrects for in the output JSON file.' + ) + parser.add_argument( + '-r', '--raw-geno', + required=True, + help='Path to the raw genotype data TSV file. This will be used to ' + 'map column names and then create the parquet file.' + ) + parser.add_argument( + '-o', '--out-dir', + default='.', + help='Directory in which to save the parquet files. Default: \'.\'.' + ) + parser.add_argument( + '--out-parquet-fname', + default='filtered_vars.parquet', + help='Name of output parquet file. Default: \'filtered_vars.parquet\'.' + ) + parser.add_argument( + '--out-json-fname', + default='filtered_vars.json', + help='Name of output JSON file. Contains updated mapping of which ' + 'variants are part of which sets (to match changes by plink2 ' + 'export). Default: \'filtered_vars.json\'.' + ) + return parser.parse_args() + + +def get_var_name_mapping(col_name): + """Given a variant column name from the plink2 raw export file, returns + the original variant name and the raw variant name as a tuple. + + Gets the original variant name by removing the '_{allele_dosage_is_for}' + part of the column name. + """ + return ( + '_'.join(col_name.split('_')[:-1]), + col_name + ) + + +if __name__ == '__main__': + + args = parse_args() + + # Load variant sets + with open(os.path.join(args.filtered_vars_raw), 'r') as f: + raw_var_sets = json.load(f) + + # print(raw_var_sets.keys()) + + # Load raw genotype data header with pandas + print('Loading raw genotype header...') + geno_header = pd.read_csv( + args.raw_geno, + sep='\t', + nrows=0, + ) + + # Map variant column names + print('Mapping variant column names...') + nongeno_cols = set(['FID', 'IID', 'PAT', 'MAT', 'SEX', 'PHENOTYPE']) + geno_col_mapping = dict( + [get_var_name_mapping(c) for c in geno_header.columns if c not in nongeno_cols] + ) + geno_col_set = set(geno_col_mapping.keys()) + + # Create updated variant sets + print('Updating variant sets...') + updated_var_sets = defaultdict(dict) + for p_val in raw_var_sets.keys(): + for window in raw_var_sets[p_val].keys(): + vars = geno_col_set.intersection(raw_var_sets[p_val][window]) + updated_var_sets[p_val][window] = [ + geno_col_mapping[var] for var in vars + ] + + # Save updated variant sets + print('Saving updated variant sets...') + with open(os.path.join(args.out_dir, args.out_json_fname), 'w') as f: + json.dump(updated_var_sets, f) + + # Scan and sink with polars + raw_dtypes = {'IID': pl.String} + for col in geno_col_mapping.values(): + raw_dtypes[col] = pl.Float32 # type: ignore + + lazy_df = pl.scan_csv( + args.raw_geno, + separator='\t', + low_memory=False, + dtypes=raw_dtypes, + null_values=['__NA__', 'NA', '-9'] + ).select( + *list(raw_dtypes.keys()) + ) + + print(lazy_df.describe()) + + # Check that there are no missing values + print('Checking for missing values...') + null_counts = lazy_df.null_count().collect(streaming=True) + n_missing = null_counts.sum_horizontal().item() + if n_missing > 0: + print( + f'Found {n_missing} missing values in the genotype data.' + ) + + # Sink to parquet + print('Saving parquet file...') + lazy_df.sink_parquet( + os.path.join(args.out_dir, args.out_parquet_fname), + compression="snappy" + ) diff --git a/paper/workflows/aml_filter_vars/run_filter_vars.sh b/paper/workflows/aml_filter_vars/run_filter_vars.sh new file mode 100755 index 0000000..6aaf747 --- /dev/null +++ b/paper/workflows/aml_filter_vars/run_filter_vars.sh @@ -0,0 +1,75 @@ +#!/bin/bash + +# Run PRSice2 PRS on CITRUS simulated phenotype + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +# PHENO=gepsi_lin_add_h0625 +# PHENO=gepsi_lin_add_h125 +# PHENO=gepsi_lin_add_h25 +# PHENO=gepsi_lin_add_h5 +# PHENO=gepsi_lin_add_hFULL + +# PHENO=gepsi_lin_add_h0625_gcta +# PHENO=gepsi_lin_add_h125_gcta +# PHENO=gepsi_lin_add_h25_gcta +# PHENO=gepsi_lin_add_h5_gcta +PHENO=gepsi_lin_add_hFULL_gcta + +echo ${PHENO} + +MAX_N_VARS=10000 + +TEMP_DIR=/tmp +OUT_DIR=output/aml_filter_vars/${PHENO} +GWAS_OUT_DIR=output/GWAS + +GWAS_SS=output/GWAS/${PHENO}.phenotype.glm.linear +SPLITS_DIR=data/splits +VAL_SPLIT_FNAME=val_samples.txt +TEST_SPLIT_FNAME=test_samples.txt +GENO_DIR=data/geno_data/pgen +PGEN_FNAME=imputed_chr19_1ID + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_aml_filter_vars:latest + +# Start docker env +module load singularitypro + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + echo 'Filtering variants by p-value and window size' && \ + python3 /home/AutoML_PRS/data_preprocessing/filter_vars_by_pval.py \ + --sum-stats-file ${GWAS_SS} \ + -p 1e-5 1e-8 1e-16 1e-24 1e-32 1e-40 1e-64 1e-128 1e-256 \ + --window-bp 0 5000 20000 50000 100000 \ + -m ${MAX_N_VARS} \ + -o ${TEMP_DIR} \ + && \ + echo 'Filter and convert to Python readable format' && \ + mkdir -p ${OUT_DIR} && \ + plink2 \ + --pfile ${GENO_DIR}/${PGEN_FNAME} \ + --extract ${TEMP_DIR}/filtered_vars_all.txt \ + --export A \ + --out ${OUT_DIR}/filtered_doseage_table \ + && \ + python3 workflows/aml_filter_vars/raw_to_input_parquet.py \ + -f ${TEMP_DIR}/filtered_vars_raw.json \ + -r ${OUT_DIR}/filtered_doseage_table.raw \ + -o ${OUT_DIR} " + +# -p 1e-5 1e-8 1e-14 1e-20 1e-26 \ +# --window-bp 0 5000 10000 25000 100000 \ \ No newline at end of file diff --git a/paper/workflows/aml_filter_vars_basil/run_filter_vars_basil.sh b/paper/workflows/aml_filter_vars_basil/run_filter_vars_basil.sh new file mode 100755 index 0000000..2cd6b2b --- /dev/null +++ b/paper/workflows/aml_filter_vars_basil/run_filter_vars_basil.sh @@ -0,0 +1,72 @@ +#!/bin/bash + +# Run PRSice2 PRS on CITRUS simulated phenotype + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +# PHENO=gepsi_lin_add_h0625 +# PHENO=gepsi_lin_add_h125 +# PHENO=gepsi_lin_add_h25 +# PHENO=gepsi_lin_add_h5 +# PHENO=gepsi_lin_add_hFULL + +# PHENO=gepsi_lin_add_h0625_gcta +# PHENO=gepsi_lin_add_h125_gcta +# PHENO=gepsi_lin_add_h25_gcta +# PHENO=gepsi_lin_add_h5_gcta +PHENO=gepsi_lin_add_hFULL_gcta + +echo ${PHENO} + +TEMP_DIR=/tmp +OUT_DIR=output/aml_filter_vars_basil/${PHENO} +GWAS_OUT_DIR=output/GWAS + +SPLITS_DIR=data/splits +VAL_SPLIT_FNAME=val_samples.txt +TEST_SPLIT_FNAME=test_samples.txt +GENO_DIR=data/geno_data/pgen +PGEN_FNAME=imputed_chr19_1ID + +BASIL_INCL_FILE=output/basil/${PHENO}/included_features.csv + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_aml_filter_vars:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + echo 'Filtering variants to BASIL included variants' && \ + mkdir -p ${OUT_DIR} && \ + python3 workflows/aml_filter_vars/filter_vars_by_basil.py \ + --basil-included-file ${BASIL_INCL_FILE} \ + -o ${OUT_DIR} \ + && \ + echo 'Filter and convert to Python readable format' && \ + plink2 \ + --pfile ${GENO_DIR}/${PGEN_FNAME} \ + --extract ${OUT_DIR}/filtered_vars_all.txt \ + --export A \ + --out ${OUT_DIR}/filtered_doseage_table \ + && \ + python3 workflows/aml_filter_vars/raw_to_input_parquet.py \ + -f ${OUT_DIR}/filtered_vars_raw.json \ + -r ${OUT_DIR}/filtered_doseage_table.raw \ + -o ${OUT_DIR} " diff --git a/paper/workflows/aml_filter_vars_prsice/run_filter_vars_prsice.sh b/paper/workflows/aml_filter_vars_prsice/run_filter_vars_prsice.sh new file mode 100755 index 0000000..53470fc --- /dev/null +++ b/paper/workflows/aml_filter_vars_prsice/run_filter_vars_prsice.sh @@ -0,0 +1,72 @@ +#!/bin/bash + +# Run PRSice2 PRS on CITRUS simulated phenotype + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +# PHENO=gepsi_lin_add_h0625 +# PHENO=gepsi_lin_add_h125 +# PHENO=gepsi_lin_add_h25 +# PHENO=gepsi_lin_add_h5 +# PHENO=gepsi_lin_add_hFULL + +# PHENO=gepsi_lin_add_h0625_gcta +# PHENO=gepsi_lin_add_h125_gcta +# PHENO=gepsi_lin_add_h25_gcta +# PHENO=gepsi_lin_add_h5_gcta +PHENO=gepsi_lin_add_hFULL_gcta + +echo ${PHENO} + +TEMP_DIR=/tmp +OUT_DIR=output/aml_filter_vars_prsice/${PHENO} + +PRSICE_OUT_DIR=output/prsice/${PHENO} +PRSICE_CLUMPS=${PRSICE_OUT_DIR}/prs_prsice2_clump_${PHENO}.clumps +PRSICE_PVAL=${PRSICE_OUT_DIR}/prsice2_${PHENO}_best_p_thresh.txt + +SPLITS_DIR=data/splits +VAL_SPLIT_FNAME=val_samples.txt +TEST_SPLIT_FNAME=test_samples.txt +GENO_DIR=data/geno_data/pgen +PGEN_FNAME=imputed_chr19_1ID + +BASIL_INCL_FILE=output/basil/${PHENO}/included_features.csv + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_aml_filter_vars:latest + +# Start docker env +module load singularitypro + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + echo 'Filtering variants to PRSice included variants' && \ + mkdir -p ${OUT_DIR} && \ + python3 /home/AutoML_PRS/data_preprocessing/filter_vars_by_clump.py \ + --clumps-file ${PRSICE_CLUMPS} \ + --pval-thresh-file ${PRSICE_PVAL} \ + -o ${OUT_DIR} \ + && \ + echo 'Filter and convert to Python readable format' && \ + plink2 \ + --pfile ${GENO_DIR}/${PGEN_FNAME} \ + --extract ${OUT_DIR}/filtered_vars_all.txt \ + --export A \ + --out ${OUT_DIR}/filtered_doseage_table \ + && \ + python3 workflows/aml_filter_vars/raw_to_input_parquet.py \ + -f ${OUT_DIR}/filtered_vars_raw.json \ + -r ${OUT_DIR}/filtered_doseage_table.raw \ + -o ${OUT_DIR} " diff --git a/paper/workflows/aml_prsice_vars/lgbm_v0h1_prsice.json b/paper/workflows/aml_prsice_vars/lgbm_v0h1_prsice.json new file mode 100644 index 0000000..c381e04 --- /dev/null +++ b/paper/workflows/aml_prsice_vars/lgbm_v0h1_prsice.json @@ -0,0 +1,9 @@ +{ + "model_type": "lgbm", + "metric": "r2", + "task": "regression", + "time_budget": 900, + "early_stopping": true, + "p_val": "clump", + "window": "lead" +} \ No newline at end of file diff --git a/paper/workflows/aml_prsice_vars/run_aml_prsice_vars.sh b/paper/workflows/aml_prsice_vars/run_aml_prsice_vars.sh new file mode 100755 index 0000000..11cc14a --- /dev/null +++ b/paper/workflows/aml_prsice_vars/run_aml_prsice_vars.sh @@ -0,0 +1,79 @@ +#!/bin/bash + +# Run PRSice2 PRS on CITRUS simulated phenotype + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +# PHENO=gepsi_lin_add_h0625 +# PHENO=gepsi_lin_add_h125 +# PHENO=gepsi_lin_add_h25 +# PHENO=gepsi_lin_add_h5 +# PHENO=gepsi_lin_add_hFULL + +# PHENO=gepsi_lin_add_h0625_gcta +# PHENO=gepsi_lin_add_h125_gcta +# PHENO=gepsi_lin_add_h25_gcta +# PHENO=gepsi_lin_add_h5_gcta +PHENO=gepsi_lin_add_hFULL_gcta + +# PHENO_DIR=pheno_sim/citrus_phenos +# PHENO_DIR=output/gepsi_phenos +PHENO_DIR=output/gcta_phenos + +echo ${PHENO} +echo ${PHENO_DIR} + +TEMP_DIR=/tmp +OUT_DIR=output/aml_prsice_vars/${PHENO} +FILTER_OUT_DIR=output/aml_filter_vars_prsice/${PHENO} + +SPLITS_DIR=data/splits +TRAIN_SPLIT_FNAME=train_samples.txt +VAL_SPLIT_FNAME=val_samples.txt +TEST_SPLIT_FNAME=test_samples.txt +GENO_DIR=data/geno_data/pgen +PGEN_FNAME=imputed_chr19_1ID +PC_DIR=data/geno_data/PCs + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_aml:latest + +# Start docker env +module load singularitypro + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + mkdir -p ${OUT_DIR} && \ + awk 'NR==1 {print \"IID\tphenotype\"; next} {print \$1\"_\"\$2\"\t\"\$3}' \ + ${PHENO_DIR}/${PHENO}.pheno > ${OUT_DIR}/${PHENO}_1ID.pheno \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${TRAIN_SPLIT_FNAME} > ${TEMP_DIR}/${TRAIN_SPLIT_FNAME} \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${VAL_SPLIT_FNAME} > ${TEMP_DIR}/${VAL_SPLIT_FNAME} \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${TEST_SPLIT_FNAME} > ${TEMP_DIR}/${TEST_SPLIT_FNAME} \ + && \ + python3 workflows/aml_basil_vars/fit_automl_prs.py \ + --training-config /mnt/citrus/CITRUS/paper/workflows/aml_prsice_vars/lgbm_v0h1_prsice.json \ + --geno-parquet ${FILTER_OUT_DIR}/filtered_vars.parquet \ + --var-subsets ${FILTER_OUT_DIR}/filtered_vars.json \ + --pheno ${OUT_DIR}/${PHENO}_1ID.pheno \ + --covars ${PC_DIR}/pcs_1ID.tsv \ + --train-ids ${TEMP_DIR}/${TRAIN_SPLIT_FNAME} \ + --val-ids ${TEMP_DIR}/${VAL_SPLIT_FNAME} \ + --test-ids ${TEMP_DIR}/${TEST_SPLIT_FNAME} \ + -o ${OUT_DIR} " \ No newline at end of file diff --git a/paper/workflows/aml_thresh_vars/en_v0_15min.json b/paper/workflows/aml_thresh_vars/en_v0_15min.json new file mode 100644 index 0000000..037bd55 --- /dev/null +++ b/paper/workflows/aml_thresh_vars/en_v0_15min.json @@ -0,0 +1,7 @@ +{ + "model_type": "elastic_net_multi_thresh", + "metric": "r2", + "task": "regression", + "time_budget": 900, + "early_stopping": true +} \ No newline at end of file diff --git a/paper/workflows/aml_thresh_vars/lgbm_v0h1.json b/paper/workflows/aml_thresh_vars/lgbm_v0h1.json new file mode 100644 index 0000000..34767a5 --- /dev/null +++ b/paper/workflows/aml_thresh_vars/lgbm_v0h1.json @@ -0,0 +1,7 @@ +{ + "model_type": "lgbm_multi_thresh", + "metric": "r2", + "task": "regression", + "time_budget": 900, + "early_stopping": true +} \ No newline at end of file diff --git a/paper/workflows/aml_thresh_vars/run_elastic_net.sh b/paper/workflows/aml_thresh_vars/run_elastic_net.sh new file mode 100755 index 0000000..be204c1 --- /dev/null +++ b/paper/workflows/aml_thresh_vars/run_elastic_net.sh @@ -0,0 +1,64 @@ +#!/bin/bash + +# Run LGBM training with p-val window thresholds + +# PHENO=complex_pheno_1 +# PHENO=complex_pheno_1_noise +# PHENO=complex_pheno_2 +# PHENO=complex_pheno_3 +# PHENO=complex_pheno_4 +# PHENO=mult_pheno_1_more_noise +PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_1 +# PHENO=lin_add_nvar1000_h2_v1 +# PHENO=lin_add_nvar1000_h4_v1 +# PHENO=compound_het_1 +# PHENO=compound_het_2 + +echo ${PHENO} + +TEMP_DIR=/tmp +PHENO_DIR=pheno_sim/citrus_phenos +OUT_DIR=output/aml_thresh_vars_elastic_net/${PHENO} +FILTER_OUT_DIR=output/aml_filter_vars/${PHENO} + +SPLITS_DIR=data/splits +TRAIN_SPLIT_FNAME=train_samples.txt +VAL_SPLIT_FNAME=val_samples.txt +TEST_SPLIT_FNAME=test_samples.txt +GENO_DIR=data/geno_data/pgen +PGEN_FNAME=imputed_chr19_1ID + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_aml:latest + +# Start docker env +module load singularitypro + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + mkdir -p ${OUT_DIR} && \ + awk 'NR==1 {print \"IID\tphenotype\"; next} {print \$1\"_\"\$2\"\t\"\$3}' \ + ${PHENO_DIR}/${PHENO}.pheno > ${OUT_DIR}/${PHENO}_1ID.pheno \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${TRAIN_SPLIT_FNAME} > ${TEMP_DIR}/${TRAIN_SPLIT_FNAME} \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${VAL_SPLIT_FNAME} > ${TEMP_DIR}/${VAL_SPLIT_FNAME} \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${TEST_SPLIT_FNAME} > ${TEMP_DIR}/${TEST_SPLIT_FNAME} \ + && \ + python3 workflows/aml_basil_vars/fit_automl_prs.py \ + --training-config /mnt/citrus/CITRUS/paper/workflows/aml_thresh_vars/en_v0_15min.json \ + --geno-parquet ${FILTER_OUT_DIR}/filtered_vars.parquet \ + --var-subsets ${FILTER_OUT_DIR}/filtered_vars.json \ + --pheno ${OUT_DIR}/${PHENO}_1ID.pheno \ + --train-ids ${TEMP_DIR}/${TRAIN_SPLIT_FNAME} \ + --val-ids ${TEMP_DIR}/${VAL_SPLIT_FNAME} \ + --test-ids ${TEMP_DIR}/${TEST_SPLIT_FNAME} \ + -o ${OUT_DIR} " \ No newline at end of file diff --git a/paper/workflows/aml_thresh_vars/run_lgbm.sh b/paper/workflows/aml_thresh_vars/run_lgbm.sh new file mode 100755 index 0000000..970668b --- /dev/null +++ b/paper/workflows/aml_thresh_vars/run_lgbm.sh @@ -0,0 +1,79 @@ +#!/bin/bash + +# Run LGBM training with p-val window thresholds + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +# PHENO=gepsi_lin_add_h0625 +# PHENO=gepsi_lin_add_h125 +# PHENO=gepsi_lin_add_h25 +# PHENO=gepsi_lin_add_h5 +# PHENO=gepsi_lin_add_hFULL + +PHENO=gepsi_lin_add_h0625_gcta +# PHENO=gepsi_lin_add_h125_gcta +# PHENO=gepsi_lin_add_h25_gcta +# PHENO=gepsi_lin_add_h5_gcta +# PHENO=gepsi_lin_add_hFULL_gcta + +# PHENO_DIR=pheno_sim/citrus_phenos +# PHENO_DIR=output/gepsi_phenos +PHENO_DIR=output/gcta_phenos + +echo ${PHENO} +echo ${PHENO_DIR} + +TEMP_DIR=/tmp +OUT_DIR=output/aml_thresh_vars/${PHENO} +FILTER_OUT_DIR=output/aml_filter_vars/${PHENO} + +SPLITS_DIR=data/splits +TRAIN_SPLIT_FNAME=train_samples.txt +VAL_SPLIT_FNAME=val_samples.txt +TEST_SPLIT_FNAME=test_samples.txt +GENO_DIR=data/geno_data/pgen +PGEN_FNAME=imputed_chr19_1ID +PC_DIR=data/geno_data/PCs + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_aml:latest + +# Start docker env +module load singularitypro + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + mkdir -p ${OUT_DIR} && \ + awk 'NR==1 {print \"IID\tphenotype\"; next} {print \$1\"_\"\$2\"\t\"\$3}' \ + ${PHENO_DIR}/${PHENO}.pheno > ${OUT_DIR}/${PHENO}_1ID.pheno \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${TRAIN_SPLIT_FNAME} > ${TEMP_DIR}/${TRAIN_SPLIT_FNAME} \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${VAL_SPLIT_FNAME} > ${TEMP_DIR}/${VAL_SPLIT_FNAME} \ + && \ + awk '{print \$1\"_\"\$2}' \ + ${SPLITS_DIR}/${TEST_SPLIT_FNAME} > ${TEMP_DIR}/${TEST_SPLIT_FNAME} \ + && \ + python3 workflows/aml_basil_vars/fit_automl_prs.py \ + --training-config /mnt/citrus/CITRUS/paper/workflows/aml_thresh_vars/lgbm_v0h1.json \ + --geno-parquet ${FILTER_OUT_DIR}/filtered_vars.parquet \ + --var-subsets ${FILTER_OUT_DIR}/filtered_vars.json \ + --pheno ${OUT_DIR}/${PHENO}_1ID.pheno \ + --covars ${PC_DIR}/pcs_1ID.tsv \ + --train-ids ${TEMP_DIR}/${TRAIN_SPLIT_FNAME} \ + --val-ids ${TEMP_DIR}/${VAL_SPLIT_FNAME} \ + --test-ids ${TEMP_DIR}/${TEST_SPLIT_FNAME} \ + -o ${OUT_DIR} " \ No newline at end of file diff --git a/paper/workflows/basil/run_basil.sh b/paper/workflows/basil/run_basil.sh new file mode 100755 index 0000000..dd7c3d8 --- /dev/null +++ b/paper/workflows/basil/run_basil.sh @@ -0,0 +1,85 @@ +# Run BASIL PRS on CITRUS simulated phenotype + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +# PHENO=gepsi_lin_add_h0625 +# PHENO=gepsi_lin_add_h125 +# PHENO=gepsi_lin_add_h25 +# PHENO=gepsi_lin_add_h5 +# PHENO=gepsi_lin_add_hFULL + +PHENO=gepsi_lin_add_h0625_gcta +# PHENO=gepsi_lin_add_h125_gcta +# PHENO=gepsi_lin_add_h25_gcta +# PHENO=gepsi_lin_add_h5_gcta +# PHENO=gepsi_lin_add_hFULL_gcta + +# PHENO_DIR=pheno_sim/citrus_phenos +# PHENO_DIR=output/gepsi_phenos +PHENO_DIR=output/gcta_phenos + +echo ${PHENO} +echo ${PHENO_DIR} + +R_SCRIPT=workflows/basil/run_basil_expanse.R +R_SCRIPT_COVAR=workflows/basil/run_basil_expanse_covars.R +TEMP_DIR=/tmp +OUT_DIR=output/basil/${PHENO} +GWAS_OUT_DIR=output/GWAS + +SPLITS_DIR=data/splits +TRAIN_SPLIT_FNAME=train_samples.txt +VAL_SPLIT_FNAME=val_samples.txt +TEST_SPLIT_FNAME=test_samples.txt +GENO_DIR=data/geno_data/pgen +PGEN_FNAME=imputed_chr19_1ID +PC_DIR=data/geno_data/PCs + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_basil:latest +N_THREADS=$(lscpu | grep "^CPU(s):" | awk '{print $2}') + +# Start docker env +module load singularitypro + +# With PC covars +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + Rscript ${R_SCRIPT_COVAR} \ + --pheno_file ${PHENO_DIR}/${PHENO}.pheno \ + --pheno_name phenotype \ + --covar_file ${PC_DIR}/pcs.tsv \ + --pheno_desc ${PHENO} \ + --geno_file ${GENO_DIR}/${PGEN_FNAME} \ + --train_samples ${SPLITS_DIR}/${TRAIN_SPLIT_FNAME} \ + --val_samples ${SPLITS_DIR}/${VAL_SPLIT_FNAME} \ + --test_samples ${SPLITS_DIR}/${TEST_SPLIT_FNAME} \ + --alpha 1.0 \ + --n_iter 20 " + +# w/o covars +# singularity exec --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} \ +# bash -c "cd /mnt/citrus/CITRUS/paper && \ +# Rscript ${R_SCRIPT} \ +# --pheno_file ${PHENO_DIR}/${PHENO}.pheno \ +# --pheno_name phenotype \ +# --pheno_desc ${PHENO} \ +# --geno_file ${GENO_DIR}/${PGEN_FNAME} \ +# --train_samples ${SPLITS_DIR}/${TRAIN_SPLIT_FNAME} \ +# --val_samples ${SPLITS_DIR}/${VAL_SPLIT_FNAME} \ +# --test_samples ${SPLITS_DIR}/${TEST_SPLIT_FNAME} \ +# --alpha 1.0 \ +# --n_iter 20 " diff --git a/paper/workflows/basil/run_basil_expanse.R b/paper/workflows/basil/run_basil_expanse.R new file mode 100644 index 0000000..9cd0ee7 --- /dev/null +++ b/paper/workflows/basil/run_basil_expanse.R @@ -0,0 +1,247 @@ +# Scrit to fit BASIL PRS model and then make prediction on test +# validation set samples on SDSC Expanse. +# +# Predictions are saved as test_preds.csv and val_preds.csv with columns +# 'IID' and 'pred'. +# +# Args: +# --pheno_file: Path to phenotype file. +# --pheno_name: Name of phenotype column in phenotype file. +# --geno_file: Path to genotype PGEN file. +# --train_samples: Path to file with list of samples to use for training. +# --val_samples: Path to file with list of samples to use for validation. +# Predictions for these samples will be saved in val_preds.csv. +# --test_samples: Path to file with list of samples. Predictions for these +# samples will be saved in test_preds.csv. +# --alpha: Alpha value for BASIL model. 1 is LASSO, 0 is ridge, and +# anything in between is elastic net. +# --n_iter: Number of iterations for BASIL model. Default is 50. + + +options(error=traceback) + +library(argparse) + +# Parse arguments function +parse_args <- function() { + # create parser object + parser <- ArgumentParser() + + # add arguments + parser$add_argument( + "--pheno_file", + help="Path to phenotype file" + ) + parser$add_argument( + "--pheno_name", + help="Name of phenotype column in phenotype file" + ) + parser$add_argument( + "--pheno_desc", + help="Name of phenotype for saving" + ) + parser$add_argument( + "--geno_file", + help="Path to genotype PGEN file" + ) + parser$add_argument( + "--train_samples", + help="Path to file with list of samples to use for training" + ) + parser$add_argument( + "--val_samples", + help="Path to file with list of samples to use for validation" + ) + parser$add_argument( + "--test_samples", + help="Path to file with list of samples to use for testing" + ) + parser$add_argument( + "--alpha", + type="double", + help="Alpha value for BASIL model. 1 is LASSO, 0 is ridge, and anything in between is elastic net" + ) + parser$add_argument( + "--n_iter", + type="integer", + help="Number of iterations for BASIL model. Default is 50" + ) + + # parse arguments + args <- parser$parse_args() + return(args) +} + + +### Main script ### + +# Load libraries +library(snpnet) +library(parallel) +library(jsonlite) + +# Get number of cores +num.cores <- detectCores(logical = FALSE) +print(paste("Number of cores:", num.cores)) + +# Parse arguments +args <- parse_args() + +# Create fit config +fit.config <- list( + nCores = num.cores, + niter = args$n_iter, # max number of iterations (default 50) + use.glmnetPlus = TRUE, # recommended for faster computation + early.stopping = TRUE, # whether to stop based on validation performance (default TRUE) + plink2.path = "plink2", # path to plink2 program + zstdcat.path = "zstdcat", # path to zstdcat program + results.dir = ".", + KKT.verbose = TRUE, + verbose = TRUE +) + +## Create combined covariate and phenotype file ## + +# Load phenotype and covariate files +pheno.data <- read.table( + args$pheno_file, + header = TRUE, + sep = "" +) + +# Inner join and get covariate names +covariates <- setdiff(names(pheno.data), c("FID", "IID", args$pheno_name)) + +## Add column indicating train/val/test split and format IDs ## + +# Read in sample IDs +train.ids <- read.table( + args$train_samples, + header = FALSE, + sep = "", + col.names = c("FID", "IID") +) +val.ids <- read.table( + args$val_samples, + header = FALSE, + sep = "", + col.names = c("FID", "IID") +) +test.ids <- read.table( + args$test_samples, + header = FALSE, + sep = "", + col.names = c("FID", "IID") +) + +# Add column indicating train/val/test split +pheno.data$split <- "none" +pheno.data$split[pheno.data$IID %in% train.ids$IID] <- "train" +pheno.data$split[pheno.data$IID %in% val.ids$IID] <- "val" +pheno.data$split[pheno.data$IID %in% test.ids$IID] <- "test" + +# Rename FID to #FID +# names(pheno.data)[names(pheno.data) == 'FID'] <- '#FID' + +print(table(pheno.data$split)) +head(pheno.data) + +## Save covariate and phenotype files ## +covar_path = file.path( + "/mnt/citrus/CITRUS/paper/output/basil", + args$pheno_desc, + "nongeno_data.tsv" +) +write.table( + pheno.data, + file = covar_path, + sep = "\t", + row.names = FALSE +) + +## Fit BASIL model ## +print("Fitting BASIL model...") + +start_time <- Sys.time() + +fit_snpnet <- snpnet( + genotype.pfile = args$geno_file, + phenotype.file = covar_path, + phenotype = args$pheno_name, + # covariates = covariates, + configs = fit.config, + family = "gaussian", + split.col = "split", + alpha=args$alpha, +) + +# Print runtime +end_time <- Sys.time() +execution_time <- difftime(end_time, start_time, units = "secs") +print(execution_time) + +# Create output dir +dir.create( + file.path( + "/mnt/citrus/CITRUS/paper/output/basil", + args$pheno_desc + ), + showWarnings = FALSE +) + +# Save features included in model +write.csv( + fit_snpnet$features.to.keep, + file = file.path( + "/mnt/citrus/CITRUS/paper/output/basil", + args$pheno_desc, + "included_features.csv" + ), + row.names = FALSE +) + +# Make predictions +snpnet_preds = predict_snpnet( + fit = fit_snpnet, + new_genotype_file=args$geno_file, + new_phenotype_file = covar_path, + phenotype = args$pheno_name, + split_col = "split", + split_name = list("val", "test"), + family = "gaussian" +) + +min_lambda_col <- names(which.max(snpnet_preds$metric$val)) +min_lambda_col + +# Extract predictions and row names for val set +val_predictions <- snpnet_preds$prediction$val[, min_lambda_col] +val_IIDs <- names(val_predictions) + +# Save predictions for val set +val_preds <- data.frame(IID = val_IIDs, pred = val_predictions) +write.csv( + val_preds, + file = file.path( + "/mnt/citrus/CITRUS/paper/output/basil", + args$pheno_desc, + "val_preds.csv" + ), + row.names = FALSE +) + +# Extract predictions and row names for test set +test_predictions <- snpnet_preds$prediction$test[, min_lambda_col] +test_IIDs <- names(test_predictions) + +# Save predictions for test set +test_preds <- data.frame(IID = test_IIDs, pred = test_predictions) +write.csv( + test_preds, + file = file.path( + "/mnt/citrus/CITRUS/paper/output/basil", + args$pheno_desc, + "test_preds.csv" + ), + row.names = FALSE +) diff --git a/paper/workflows/basil/run_basil_expanse_covars.R b/paper/workflows/basil/run_basil_expanse_covars.R new file mode 100644 index 0000000..f66c9cd --- /dev/null +++ b/paper/workflows/basil/run_basil_expanse_covars.R @@ -0,0 +1,270 @@ +# Scrit to fit BASIL PRS model and then make prediction on test +# validation set samples on SDSC Expanse. +# +# Predictions are saved as test_preds.csv and val_preds.csv with columns +# 'IID' and 'pred'. +# +# Args: +# --pheno_file: Path to phenotype file. +# --pheno_name: Name of phenotype column in phenotype file. +# --covar_file: Path to covariate file. +# --geno_file: Path to genotype PGEN file. +# --train_samples: Path to file with list of samples to use for training. +# --val_samples: Path to file with list of samples to use for validation. +# Predictions for these samples will be saved in val_preds.csv. +# --test_samples: Path to file with list of samples. Predictions for these +# samples will be saved in test_preds.csv. +# --alpha: Alpha value for BASIL model. 1 is LASSO, 0 is ridge, and +# anything in between is elastic net. +# --n_iter: Number of iterations for BASIL model. Default is 50. + + +options(error=traceback) + +library(argparse) + +# Parse arguments function +parse_args <- function() { + # create parser object + parser <- ArgumentParser() + + # add arguments + parser$add_argument( + "--pheno_file", + help="Path to phenotype file" + ) + parser$add_argument( + "--pheno_name", + help="Name of phenotype column in phenotype file" + ) + parser$add_argument( + "--pheno_desc", + help="Name of phenotype for saving" + ) + parser$add_argument( + "--covar_file", + help="Path to covariate file" + ) + parser$add_argument( + "--geno_file", + help="Path to genotype PGEN file" + ) + parser$add_argument( + "--train_samples", + help="Path to file with list of samples to use for training" + ) + parser$add_argument( + "--val_samples", + help="Path to file with list of samples to use for validation" + ) + parser$add_argument( + "--test_samples", + help="Path to file with list of samples to use for testing" + ) + parser$add_argument( + "--alpha", + type="double", + help="Alpha value for BASIL model. 1 is LASSO, 0 is ridge, and anything in between is elastic net" + ) + parser$add_argument( + "--n_iter", + type="integer", + help="Number of iterations for BASIL model. Default is 50" + ) + + # parse arguments + args <- parser$parse_args() + return(args) +} + + +### Main script ### + +# Load libraries +library(snpnet) +library(parallel) +library(jsonlite) + +# Get number of cores +num.cores <- detectCores(logical = FALSE) +print(paste("Number of cores:", num.cores)) + +# Parse arguments +args <- parse_args() + +# Create fit config +fit.config <- list( + nCores = num.cores, + niter = args$n_iter, # max number of iterations (default 50) + use.glmnetPlus = TRUE, # recommended for faster computation + early.stopping = TRUE, # whether to stop based on validation performance (default TRUE) + plink2.path = "plink2", # path to plink2 program + zstdcat.path = "zstdcat", # path to zstdcat program + results.dir = ".", + KKT.verbose = TRUE, + verbose = TRUE +) + +## Create combined covariate and phenotype file ## + +# Load phenotype and covariate files +pheno.data <- read.table( + args$pheno_file, + header = TRUE, + sep = "" +) +covar.data <- read.table( + args$covar_file, + header = TRUE, + sep = "" +) + +# Inner join and get covariate names +nongeno.data <- merge( + pheno.data, + covar.data, + by.x = c("FID", "IID"), + by.y = c("FID", "IID") +) +covariates <- setdiff(names(nongeno.data), c("FID", "IID", args$pheno_name)) + +## Add column indicating train/val/test split and format IDs ## + +# Read in sample IDs +train.ids <- read.table( + args$train_samples, + header = FALSE, + sep = "", + col.names = c("FID", "IID") +) +val.ids <- read.table( + args$val_samples, + header = FALSE, + sep = "", + col.names = c("FID", "IID") +) +test.ids <- read.table( + args$test_samples, + header = FALSE, + sep = "", + col.names = c("FID", "IID") +) + +# Add column indicating train/val/test split +nongeno.data$split <- "none" +nongeno.data$split[nongeno.data$IID %in% train.ids$IID] <- "train" +nongeno.data$split[nongeno.data$IID %in% val.ids$IID] <- "val" +nongeno.data$split[nongeno.data$IID %in% test.ids$IID] <- "test" + +# Rename FID to #FID +# names(nongeno.data)[names(nongeno.data) == 'FID'] <- '#FID' + +print(table(nongeno.data$split)) +head(nongeno.data) +print(covariates) + +## Save covariate and phenotype files ## +covar_path = file.path( + # "/mnt/citrus/CITRUS/paper/output/basil", + # args$pheno_desc, + # "nongeno_data.tsv" + "/tmp/nongeno_data.tsv" +) +write.table( + nongeno.data, + file = covar_path, + sep = "\t", + row.names = FALSE +) + +## Fit BASIL model ## +print("Fitting BASIL model...") + +start_time <- Sys.time() + +fit_snpnet <- snpnet( + genotype.pfile = args$geno_file, + phenotype.file = covar_path, + phenotype = args$pheno_name, + covariates = covariates, + configs = fit.config, + family = "gaussian", + split.col = "split", + alpha=args$alpha, +) + +# Print runtime +end_time <- Sys.time() +execution_time <- difftime(end_time, start_time, units = "secs") +print(execution_time) + +# Create output dir +dir.create( + file.path( + "/mnt/citrus/CITRUS/paper/output/basil", + args$pheno_desc + ), + showWarnings = FALSE +) + +# Save features included in model +write.csv( + fit_snpnet$features.to.keep, + file = file.path( + "/mnt/citrus/CITRUS/paper/output/basil", + args$pheno_desc, + "included_features.csv" + ), + row.names = FALSE +) + +print("\n\n\n\nCOMPLETE TRAINING") + +# Make predictions +snpnet_preds = predict_snpnet( + fit = fit_snpnet, + new_genotype_file=args$geno_file, + new_phenotype_file = covar_path, + covariate_names = covariates, + phenotype = args$pheno_name, + split_col = "split", + split_name = list("val", "test"), + family = "gaussian" +) + +print("\n\n\n\nCOMPLETE PREDS") + +min_lambda_col <- names(which.max(snpnet_preds$metric$val)) +print(min_lambda_col) + +# Extract predictions and row names for val set +val_predictions <- snpnet_preds$prediction$val[, min_lambda_col] +val_IIDs <- names(val_predictions) + +# Save predictions for val set +val_preds <- data.frame(IID = val_IIDs, pred = val_predictions) +write.csv( + val_preds, + file = file.path( + "/mnt/citrus/CITRUS/paper/output/basil", + args$pheno_desc, + "val_preds.csv" + ), + row.names = FALSE +) + +# Extract predictions and row names for test set +test_predictions <- snpnet_preds$prediction$test[, min_lambda_col] +test_IIDs <- names(test_predictions) + +# Save predictions for test set +test_preds <- data.frame(IID = test_IIDs, pred = test_predictions) +write.csv( + test_preds, + file = file.path( + "/mnt/citrus/CITRUS/paper/output/basil", + args$pheno_desc, + "test_preds.csv" + ), + row.names = FALSE +) diff --git a/paper/workflows/gcta_pheno_sim/__MACOSX/._gcta-1.94.1-linux-kernel-3-x86_64 b/paper/workflows/gcta_pheno_sim/__MACOSX/._gcta-1.94.1-linux-kernel-3-x86_64 new file mode 100755 index 0000000..8da9c4a Binary files /dev/null and b/paper/workflows/gcta_pheno_sim/__MACOSX/._gcta-1.94.1-linux-kernel-3-x86_64 differ diff --git a/paper/workflows/gcta_pheno_sim/__MACOSX/gcta-1.94.1-linux-kernel-3-x86_64/._.DS_Store 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0000000..e594ef9 Binary files /dev/null and b/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/.DS_Store differ diff --git a/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/MIT_License.txt b/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/MIT_License.txt new file mode 100644 index 0000000..f7b9c8f --- /dev/null +++ b/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/MIT_License.txt @@ -0,0 +1,7 @@ +Copyright (C) 2010-2017 Jian Yang + +Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. diff --git a/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/README.txt b/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/README.txt new file mode 100644 index 0000000..082b403 --- /dev/null +++ b/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/README.txt @@ -0,0 +1,36 @@ +PROGRAM: GCTA + +DESCRIPTION: Genome-wide Complex Trait Analysis + +AUTHOR: Jian Yang, Hong Lee, Mike Goddard, Zhili Zheng, Jian Zeng, Zhihong Zhu, +Andrew Bakshi, Robert Marie and Peter Visscher + +CONTACT: jian.yang@uq.edu.au + +2010-2017 + +The binary are released under MIT License. As the license of the dependency packages, +you shall turn to the licenses for the usage other than academic. + +DOCUMENTATION: http://cnsgenomics.com/software/gcta/ + +INSTALLATION: When you have downloaded a zip or gzipped archive with an +executable binary, no installation is necessary. NOTE: If there is a +bin folder, you should put all files in that folder all together, copy +gcta64 out only will not work. + +USAGE: Type "gcta64" or "./gcta64" from the command line followed by the +options of choice (see documentation) NOTE: you probably need to run +"chmod a+x gcta" to get the correct permission to execute the program. + +EXAMPLE DATA: Four example files test.bed, test.bim, test.fam and test.phen +are included in the distribution; for example, once GCTA is installed try running: + + gcta64 --bfile test --make-grm --out test + + gcta64 --reml --grm test --pheno test.phen --out test + + etc... + +In Mac or Windows version, the example files locates in the parent folder of gcta64. + diff --git a/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/gcta64 b/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/gcta64 new file mode 100755 index 0000000..46d7e43 Binary files /dev/null and b/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/gcta64 differ diff --git a/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/test.bed b/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/test.bed new file mode 100644 index 0000000..28401a3 --- /dev/null +++ b/paper/workflows/gcta_pheno_sim/gcta-1.94.1-linux-kernel-3-x86_64/test.bed @@ -0,0 +1,2628 @@ 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-0.172677020388401 diff --git a/paper/workflows/gcta_pheno_sim/output_file b/paper/workflows/gcta_pheno_sim/output_file new file mode 100644 index 0000000..1d374c9 --- /dev/null +++ b/paper/workflows/gcta_pheno_sim/output_file @@ -0,0 +1 @@ +FID IID phenotype diff --git a/paper/workflows/gcta_pheno_sim/run_gcta_sim.sh b/paper/workflows/gcta_pheno_sim/run_gcta_sim.sh new file mode 100755 index 0000000..1295331 --- /dev/null +++ b/paper/workflows/gcta_pheno_sim/run_gcta_sim.sh @@ -0,0 +1,37 @@ +# Run GEPSi simulation + +HERIT='5' +PHENO=gepsi_lin_add_h${HERIT}_gcta + +CONFIG_DIR=output/gcta_sim_configs +OUT_DIR=output/gcta_phenos +TEMP_DIR=/tmp + +BED_DIR=data/geno_data/bed + +DOCKER_URL=gcr.io/ucsd-medicine-cast/gepsi:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +# Run GEPSi simulation +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows/gcta_pheno_sim && \ + mkdir -p ../../${OUT_DIR} && \ + ./gcta-1.94.1-linux-kernel-3-x86_64/gcta64 \ + --bfile ../../${BED_DIR}/imputed_chr19 \ + --simu-qt \ + --simu-causal-loci ../../${CONFIG_DIR}/${PHENO}.csv \ + --simu-hsq 0.${HERIT} \ + --out ../../${OUT_DIR}/${PHENO} && \ + awk 'BEGIN {FS=\" \"; OFS=\"\\t\"; print \"FID\", \"IID\", \"phenotype\"} {print \$1, \$2, \$3}' \ + ../../${OUT_DIR}/${PHENO}.phen > ../../${OUT_DIR}/${PHENO}.pheno " + + +# --simu-hsq 0.${HERIT} \ \ No newline at end of file diff --git a/paper/workflows/gepsi/run_gepsi.sh b/paper/workflows/gepsi/run_gepsi.sh new file mode 100755 index 0000000..3a8b0a7 --- /dev/null +++ b/paper/workflows/gepsi/run_gepsi.sh @@ -0,0 +1,55 @@ +# Run GEPSi simulation + +HERIT='001' + +# Check if HERIT is equal to FULL +if [ "$HERIT" = "FULL" ]; then + HERIT_FLOAT=1.0 +else + HERIT_FLOAT="0.${HERIT}" +fi + +N_CAUSAL=2 + +# Check if N_CAUSAL is 100 +if [ "$N_CAUSAL" -eq 100 ]; then + OUT_DIR="output/gepsi_sim_out/lin_add_h${HERIT}" +else + OUT_DIR="output/gepsi_sim_out/lin_add_n${N_CAUSAL}_h${HERIT}" +fi + +DATA_DIR=output/gepsi_fmt_data +TEMP_DIR=/tmp + +DOCKER_URL=gcr.io/ucsd-medicine-cast/gepsi:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +# Run GEPSi simulation +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + pip install PyYAML && \ + mkdir -p ${OUT_DIR} && \ + cd ${OUT_DIR} && \ + gepsi phenotype \ + -dp ../../../${DATA_DIR}/ \ + -data chr19_None \ + --heritability ${HERIT_FLOAT} \ + -pname phenotype \ + -cut 0.0 \ + -mask 0.0 \ + -df 0.0 \ + -rf 0.0 \ + -cf 0.0 \ + -num_snps ${N_CAUSAL} \ + --causal_snp_mode random " + +# Remember to manually move output to the output dir this creates +# --heritability 0.${HERIT} \ \ No newline at end of file diff --git a/paper/workflows/gepsi_prepro/run_gepsi_prepro.sh b/paper/workflows/gepsi_prepro/run_gepsi_prepro.sh new file mode 100755 index 0000000..59e5bef --- /dev/null +++ b/paper/workflows/gepsi_prepro/run_gepsi_prepro.sh @@ -0,0 +1,46 @@ +# Run GEPSi simulation + +OUT_DIR=output/gepsi_fmt_data +TEMP_DIR=/tmp + +GENO_DIR=data/geno_data/pgen +PGEN_FNAME=imputed_chr19_1ID + +SAMPLE_FILE=data/splits/all_samples.txt + +PLINK_DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_aml_filter_vars:latest +GEPSI_DOCKER_URL=gcr.io/ucsd-medicine-cast/gepsi:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${PLINK_DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + mkdir -p ${OUT_DIR} && \ + plink2 \ + --pfile ${GENO_DIR}/${PGEN_FNAME} \ + --geno 0 \ + --maf 0.01 \ + --make-just-bim \ + --out ${OUT_DIR}/chr19 \ + && \ + plink2 \ + --pfile ${GENO_DIR}/${PGEN_FNAME} \ + --geno 0 \ + --maf 0.01 \ + --export A \ + --out ${OUT_DIR}/chr19 " + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${GEPSI_DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/${OUT_DIR} && \ + ls && \ + gepsi genotype \ + --config genotype.yaml " diff --git a/paper/workflows/gepsi_reformat_pheno/reformat_pheno.py b/paper/workflows/gepsi_reformat_pheno/reformat_pheno.py new file mode 100644 index 0000000..d1188a5 --- /dev/null +++ b/paper/workflows/gepsi_reformat_pheno/reformat_pheno.py @@ -0,0 +1,88 @@ +"""Reformat GEPSi output to .pheno file. + +Args: + -g, --gepsi-out-dir: Directory containing GEPSi output files. + -d, --gepsi-out-desc: Description part of GEPSi output file names. + -o, --output-dir: Directory to write .pheno files to. + -p, --pheno-file: Name of .pheno file to write to. + -s, --snplist-file: Path of CSV file containing SNP list. + -samp, --sample-file: Header then one ID per line, in the same order + as the phenotype file. +""" + +import argparse +import pickle +import os + +import pandas as pd + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("-g", "--gepsi-out-dir", required=True) + parser.add_argument("-d", "--gepsi-out-desc", required=True) + parser.add_argument("-o", "--output-dir", required=True) + parser.add_argument("-p", "--pheno-file", required=True) + parser.add_argument("-s", "--snplist-file", required=True) + parser.add_argument("-samp", "--sample-file", required=True) + return parser.parse_args() + + +if __name__ == '__main__': + + args = parse_args() + + # Load GEPSi output pickle files + gene_fname = f"causal_genes_{args.gepsi_out_desc}_phenotype.pkl" + snp_idx_fname = f"causal_snp_idx_{args.gepsi_out_desc}_phenotype.pkl" + effect_size_fname = f"effect_size_{args.gepsi_out_desc}_phenotype.pkl" + pheno_fname = f"phenotype_{args.gepsi_out_desc}_phenotype.pkl" + + with open(os.path.join(args.gepsi_out_dir, gene_fname), 'rb') as f: + gene_data = pickle.load(f) + with open(os.path.join(args.gepsi_out_dir, snp_idx_fname), 'rb') as f: + snp_idx_data = pickle.load(f) + with open(os.path.join(args.gepsi_out_dir, effect_size_fname), 'rb') as f: + effect_size_data = pickle.load(f) + with open(os.path.join(args.gepsi_out_dir, pheno_fname), 'rb') as f: + pheno_data = pickle.load(f) + + # Load SNP list + snp_df = pd.read_csv(args.snplist_file, sep='\s+') + + # Load sample list + sample_df = pd.read_csv(args.sample_file) + + # Create pheno file + sample_df['IID'] = sample_df['IID'].str.split('_').apply(lambda x: x[0]) + + pheno_df = pd.DataFrame({ + 'FID': sample_df['IID'].values, + 'IID': sample_df['IID'].values, + 'phenotype': pheno_data + }) + + pheno_df.to_csv( + os.path.join(args.output_dir, args.pheno_file + '.pheno'), + sep='\t', + index=False + ) + + # Get causal SNPs and weights + snp_df = snp_df.loc[list(snp_idx_data.keys())] + + effect_sizes = [ + effect_size_data[idx][1] for idx in snp_idx_data.keys() + ] + + snp_df['effect_size'] = effect_sizes + + snp_df.to_csv( + os.path.join(args.output_dir, args.pheno_file + '_causal_snps_info.csv'), + index=False + ) + + g='../../output/gepsi_sim_out/lin_add_h2' + d='chr19_None' + s='../../output/gepsi_fmt_data/snplist_chr19_None.csv' + samp='../../output/gepsi_fmt_data/sample_ids.txt' \ No newline at end of file diff --git a/paper/workflows/gepsi_reformat_pheno/run_reformat_pheno.sh b/paper/workflows/gepsi_reformat_pheno/run_reformat_pheno.sh new file mode 100755 index 0000000..5248809 --- /dev/null +++ b/paper/workflows/gepsi_reformat_pheno/run_reformat_pheno.sh @@ -0,0 +1,39 @@ +# Run GEPSi simulation + +HERIT='001' +N_CAUSAL=2 + +# Check if N_CAUSAL is 100 +if [ "$N_CAUSAL" -eq 100 ]; then + FNAME=lin_add_h${HERIT} + SIM_DIR="output/gepsi_sim_out/${FNAME}" +else + FNAME=lin_add_n${N_CAUSAL}_h${HERIT} + SIM_DIR="output/gepsi_sim_out/${FNAME}" +fi + +OUT_DIR=output/gepsi_phenos +DATA_DIR=output/gepsi_fmt_data + +DOCKER_URL=gcr.io/ucsd-medicine-cast/gepsi:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +# Run GEPSi simulation +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows/gepsi_reformat_pheno && \ + mkdir -p ../../${OUT_DIR} && \ + python3.9 reformat_pheno.py \ + -g ../../${SIM_DIR} \ + -d chr19_None \ + -s ../../${DATA_DIR}/snplist_chr19_None.csv \ + -samp ../../${DATA_DIR}/sample_ids.txt \ + -o ../../${OUT_DIR} \ + -p gepsi_${FNAME} " \ No newline at end of file diff --git a/paper/workflows/gepsi_to_citrus/gepsi_to_citrus.py b/paper/workflows/gepsi_to_citrus/gepsi_to_citrus.py new file mode 100644 index 0000000..43ec320 --- /dev/null +++ b/paper/workflows/gepsi_to_citrus/gepsi_to_citrus.py @@ -0,0 +1,119 @@ +"""Create CITRUS simulation config from GEPSi sim info. + +Args: + -g, --gepsi-out-dir: Directory containing GEPSi output files. + -d, --gepsi-out-desc: Description part of GEPSi output file names. + -h, --herit: Heritability + -o, --output-dir: Directory to write simulation config to. +""" + +import argparse +import os +import json + +import pandas as pd + + +# Template needs pos, betas, and heritability added +TEMPLAT_CONFIG = { + "input": [ + { + "file": "../data/geno_data/vcf/phased_chr19.vcf", + "file_format": "vcf", + "reference_genome": "GRCh37", + "input_nodes": [ + { + "alias": "variants", + "type": "SNP", + "chr": "19", + "pos": None + } + ] + } + ], + "simulation_steps": [ + { + "type": "Constant", + "alias": "variants_betas", + "input_match_size": "variants", + "constant": None + }, + { + "type": "Product", + "alias": "variant_effects", + "input_aliases": [ + "variants_betas", + "variants" + ] + }, + { + "type": "AdditiveCombine", + "alias": "combined_variant_effects", + "input_alias": "variant_effects" + }, + { + "type": "SumReduce", + "alias": "no_noise_phenotype", + "input_alias": "combined_variant_effects" + }, + { + "type": "Heritability", + "alias": "phenotype", + "input_alias": "no_noise_phenotype", + "heritability": None + } + ] +} + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("-g", "--gepsi-out-dir", required=True) + parser.add_argument("-d", "--gepsi-out-desc", required=True) + parser.add_argument("-h2", "--herit", required=True) + parser.add_argument("-o", "--output-dir", required=True) + return parser.parse_args() + + +if __name__ == '__main__': + + args = parse_args() + + # Load GEPSi SNP info + causal_snps = pd.read_csv( + os.path.join( + args.gepsi_out_dir, f"{args.gepsi_out_desc}_causal_snps_info.csv" + ) + ) + + # Create simulation config + sim_config = TEMPLAT_CONFIG + + # Pos + sim_config['input'][0]['input_nodes'][0]['pos'] = causal_snps[ + 'Position' + ].values.tolist() + + # Betas + sim_config['simulation_steps'][0]['constant'] = causal_snps[ + 'effect_size' + ].values.tolist() + + # Heritability + sim_config['simulation_steps'][-1]['heritability'] = float(args.herit) + + # Save JSON config + h = str(args.herit).split('.')[-1] + with open(os.path.join( + args.output_dir, f"{args.gepsi_out_desc}_h{h}.json" + ), 'w') as f: + json.dump( + sim_config, + f, + indent=4 + ) + + + # g='../../output/gepsi_phenos' + # d='gepsi_lin_add_h2' + # h2=0.2 diff --git a/paper/workflows/gepsi_to_citrus/run_gepsi_to_citrus.sh b/paper/workflows/gepsi_to_citrus/run_gepsi_to_citrus.sh new file mode 100755 index 0000000..0c0f5c0 --- /dev/null +++ b/paper/workflows/gepsi_to_citrus/run_gepsi_to_citrus.sh @@ -0,0 +1,28 @@ +# Run GEPSi simulation + +HERIT='0625' +PHENO=gepsi_lin_add_h${HERIT} + +GEPSI_DIR=output/gepsi_phenos +OUT_DIR=pheno_sim/sim_configs +TEMP_DIR=/tmp + +DOCKER_URL=gcr.io/ucsd-medicine-cast/gepsi:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +# Run GEPSi simulation +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows/gepsi_to_citrus && \ + python3.9 gepsi_to_citrus.py \ + -g ../../${GEPSI_DIR} \ + -d ${PHENO} \ + -h2 0.${HERIT} \ + -o ../../${OUT_DIR} " \ No newline at end of file diff --git a/paper/workflows/gepsi_to_gcta/gepsi_to_gcta.py b/paper/workflows/gepsi_to_gcta/gepsi_to_gcta.py new file mode 100644 index 0000000..bb5ccca --- /dev/null +++ b/paper/workflows/gepsi_to_gcta/gepsi_to_gcta.py @@ -0,0 +1,82 @@ +"""Create GCTA simulation config from GEPSi sim info. + +Args: + -g, --gepsi-out-dir: Directory containing GEPSi output files. + -d, --gepsi-out-desc: Description part of GEPSi output file names. + -h, --herit: Heritability + -v, --variant-info: Variant info file + -o, --output-dir: Directory to write simulation config to. +""" + +import argparse +import os +import json +import math + +import numpy as np +import pandas as pd + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument("-g", "--gepsi-out-dir", required=True) + parser.add_argument("-d", "--gepsi-out-desc", required=True) + parser.add_argument("-h2", "--herit", required=True) + parser.add_argument("-v", "--variant-info", required=True) + parser.add_argument("-o", "--output-dir", required=True) + return parser.parse_args() + + +if __name__ == '__main__': + + args = parse_args() + print(args) + + # Load GEPSi SNP info + causal_snps = pd.read_csv( + os.path.join( + args.gepsi_out_dir, f"{args.gepsi_out_desc}_causal_snps_info.csv" + ) + ) + + # Load variant info + var_info = pd.read_csv(args.variant_info)[[ + 'locus', 'variant_qc.AF' + ]] + var_info['Position'] = var_info['locus'].str.split(':').apply(lambda x: x[1]).astype(int) + var_info['p_0'] = var_info['variant_qc.AF'].str.strip('[]').str.split(',').apply( + lambda x: float(x[0].strip()) + ) + var_info['p_1'] = var_info['variant_qc.AF'].str.strip('[]').str.split(',').apply( + lambda x: float(x[1].strip()) + ) + var_info['2p(1-p)'] = var_info['p_0'] * var_info['p_1'] * 2 + var_info['sqrt(2p(1-p))'] = var_info['2p(1-p)'].apply( + lambda x: math.sqrt(x) + ) + + # Join + causal_snps = causal_snps.merge( + var_info, + on='Position', + how='left', + ) + + # Rescale effect size + causal_snps['GCTA_u'] = ( + # causal_snps['effect_size'] * causal_snps['sqrt(2p(1-p))'] * -1 + (causal_snps['effect_size'] * causal_snps['sqrt(2p(1-p))'] * -1) / 2 * 0.7763515390547042 + ) + + # Output + gcta_output = causal_snps[['ID', 'GCTA_u']] + + h = str(args.herit).split('.')[-1] + gcta_output.to_csv( + os.path.join( + args.output_dir, f"{args.gepsi_out_desc}_gcta.csv" + ), + sep='\t', + header=False, + index=False, + ) \ No newline at end of file diff --git a/paper/workflows/gepsi_to_gcta/run_gepsi_to_gcta.sh b/paper/workflows/gepsi_to_gcta/run_gepsi_to_gcta.sh new file mode 100755 index 0000000..b11465f --- /dev/null +++ b/paper/workflows/gepsi_to_gcta/run_gepsi_to_gcta.sh @@ -0,0 +1,32 @@ +# Run GEPSi simulation + +HERIT='0625' +PHENO=gepsi_lin_add_h${HERIT} + +GEPSI_DIR=output/gepsi_phenos +OUT_DIR=output/gcta_sim_configs +TEMP_DIR=/tmp + +VAR_INFO=data/geno_data/chr19_var_info.csv + +DOCKER_URL=gcr.io/ucsd-medicine-cast/gepsi:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +# Run GEPSi simulation +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows/gepsi_to_gcta && \ + mkdir -p ../../${OUT_DIR} && \ + python3.9 gepsi_to_gcta.py \ + -g ../../${GEPSI_DIR} \ + -d ${PHENO} \ + -h2 0.${HERIT} \ + -v ../../${VAR_INFO} \ + -o ../../${OUT_DIR} " \ No newline at end of file diff --git a/paper/workflows/heritability/run_herit.sh b/paper/workflows/heritability/run_herit.sh new file mode 100755 index 0000000..44cc867 --- /dev/null +++ b/paper/workflows/heritability/run_herit.sh @@ -0,0 +1,28 @@ +# Estimate heritability + +TEMP_DIR=/tmp + +DOCKER_URL=gcr.io/ucsd-medicine-cast/citrus:latest +N_THREADS=$(lscpu | grep "^CPU(s):" | awk '{print $2}') + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://gcr.io/ucsd-medicine-cast/citrus:latest + +singularity exec --containall --cleanenv --writable-tmpfs \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/pheno_sim/ && \ + export SPARK_DRIVER_MEMORY=10G && \ + export SPARK_EXECUTOR_MEMORY=10G && \ + export SPARK_DRIVER_MEMORY_OVERHEAD=3g && \ + export SPARK_EXECUTOR_MEMORY_OVERHEAD=3g && \ + python3 est_heritability.py " + + # export SPARK_DRIVER_MEMORY=10G && \ + # export SPARK_EXECUTOR_MEMORY=10G && \ + # export SPARK_DRIVER_MEMORY_OVERHEAD=50g && \ + # export SPARK_EXECUTOR_MEMORY_OVERHEAD=50g && \ \ No newline at end of file diff --git a/paper/workflows/plot_all/herit.csv b/paper/workflows/plot_all/herit.csv new file mode 100644 index 0000000..ceffff4 --- /dev/null +++ b/paper/workflows/plot_all/herit.csv @@ -0,0 +1,2 @@ +sim_name,heritability +mult_pheno_1,0.39542135595772854 diff --git a/paper/workflows/plot_all/plot_all.py b/paper/workflows/plot_all/plot_all.py new file mode 100644 index 0000000..1450fe6 --- /dev/null +++ b/paper/workflows/plot_all/plot_all.py @@ -0,0 +1,324 @@ +import json +import os +from itertools import product + +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +from tqdm import tqdm + + +if __name__ == '__main__': + + results_dir = '../../output' + pheno_name = 'phenotype' + + # sim_names = [ + # 'lin_add_nvar1000_h2_v1', + # 'lin_add_nvar1000_h4_v1', + # 'compound_het', + # 'compound_het_2', + # 'xor_pheno_2', + # 'xor_pheno_1', + # 'complex_pheno_1_noise', + # 'complex_pheno_2', + # 'complex_pheno_3', + # 'complex_pheno_4', + # 'mult_pheno_1', + # 'mult_pheno_2', + # ] + # sim_names = [ + # 'lin_add_nvar100_h2_v1', + # 'lin_add_nvar100_h4_v1', + # 'mult_pheno_1', + # 'mult_pheno_2', + # 'compound_het', + # 'compound_het_2', + # 'xor_pheno_2', + # 'xor_pheno_1', + # ] + # sim_names = [ + # 'lin_add_nvar100_h05_v1', + # 'lin_add_nvar100_h1_v1', + # 'lin_add_nvar100_h2_v1', + # 'lin_add_nvar100_h4_v1', + # 'lin_add_nvar100_h8_v1', + # 'gepsi_lin_add_h15', + # 'gepsi_lin_add_h15_h15', + # 'gepsi_lin_add_h15_gcta', + # 'gepsi_lin_add_hFULL', + # 'gepsi_lin_add_hFULL_hFULL', + # 'gepsi_lin_add_hFULL_gcta', + # ] + # sim_names = [ + # 'mult_pheno_1', # Multiplicative 1 + # 'mult_pheno_2', # Multiplicative 2 + # 'compound_het', # Compound het close pair of SNPs + # 'compound_het_2', # Compound het far pair of SNPs + # 'xor_pheno_2', # XOR close + # 'xor_pheno_1', # XOR far + # 'lin_add_nvar100_h05_v1', # CITRUS only lin-add h^2 = 0.05, 100 causal + # 'lin_add_nvar100_h1_v1', # CITRUS only lin-add h^2 = 0.1, 100 causal + # 'lin_add_nvar100_h2_v1', # CITRUS only lin-add h^2 = 0.2, 100 causal + # 'lin_add_nvar100_h4_v1', # CITRUS only lin-add h^2 = 0.4, 100 causal + # 'lin_add_nvar100_h8_v1', # CITRUS only lin-add h^2 = 0.4, 100 causal + # 'gepsi_lin_add_h15', # Common lin-add GEPSI h^2 = 0.15 (100 causal for all) + # 'gepsi_lin_add_h15_h15', # Common lin-add CITRUS h^2 = 0.15 + # 'gepsi_lin_add_h15_gcta', # Common lin-add GCTA h^2 = 0.15 + # 'gepsi_lin_add_hFULL', # Common lin-add GEPSI h^2 = 1.0 + # 'gepsi_lin_add_hFULL_hFULL',# Common lin-add CITRUS h^2 = 1.0 + # 'gepsi_lin_add_hFULL_gcta', # Common lin-add GCTA h^2 = 1.0 + # ] + sim_names = [ + # 'mult_pheno_1', # Multiplicative 1 + # 'mult_pheno_2', # Multiplicative 2 + # 'compound_het', # Compound het close pair of SNPs + # 'compound_het_2', # Compound het far pair of SNPs + 'xor_pheno_2', # XOR close + # 'xor_pheno_1', # XOR far + # 'gepsi_lin_add_h0625_h0625',# CITRUS lin-add h^2 = 0.0625 + # 'gepsi_lin_add_h0625', # GEPSi lin-add h^2 = 0.0625 + # 'gepsi_lin_add_h0625_gcta', # GCTA lin-add h^2 = 0.0625 + # 'gepsi_lin_add_h125_h125', # CITRUS lin-add h^2 = 0.125 + # 'gepsi_lin_add_h125', # GEPSi lin-add h^2 = 0.125 + # 'gepsi_lin_add_h125_gcta', # GCTA lin-add h^2 = 0.125 + # 'gepsi_lin_add_h25_h25', # CITRUS lin-add h^2 = 0.25 + # 'gepsi_lin_add_h25', # GEPSi lin-add h^2 = 0.25 + # 'gepsi_lin_add_h25_gcta', # GCTA lin-add h^2 = 0.25 + # 'gepsi_lin_add_h5_h5', # CITRUS lin-add h^2 = 0.5 + # 'gepsi_lin_add_h5', # GEPSi lin-add h^2 = 0.5 + # 'gepsi_lin_add_h5_gcta', # GCTA lin-add h^2 = 0.5 + # 'gepsi_lin_add_hFULL_hFULL',# CITRUS lin-add h^2 = 1.0 + # 'gepsi_lin_add_hFULL', # GEPSi lin-add h^2 = 1.0 + # 'gepsi_lin_add_hFULL_gcta', # GCTA lin-add h^2 = 1.0 + ] + + model_names = [ + 'PRSice-2', + 'BASIL', + 'LGBM-Threshold', + 'LGBM-PRSice', + 'LGBM-BASIL', + ] + + feat_sel_dict = { + 'PRSice-2': 'PRSice', + 'BASIL': 'BASIL', + 'LGBM-PRSice': 'PRSice', + 'LGBM-BASIL': 'BASIL', + 'LGBM-Threshold': 'p-val + window' + } + + ci = "95" + herit_data_dir = '../../pheno_sim/citrus_output' + + # Load heritability data + herit_data = [] + + for sim_name in sim_names: + herit_json_path = f"{herit_data_dir}/{sim_name}_heritability_ci{ci}.json" + + if not os.path.isfile(herit_json_path): + continue + + with open(herit_json_path, 'r') as f: + sim_herit_data = json.load(f) + + herit_data.append({ + "sim_name": sim_name, + "heritability": sim_herit_data['H2'] + }) + + herit_data = pd.DataFrame(herit_data) + + print('Herit') + print(herit_data.head(20)) + herit_data.to_csv('herit.csv', index=False) + + # Load all results + res_list = [] + + n_total = len(sim_names) * len(model_names) + + # Desc to model output dir + desc_to_outdir = { + 'PRSice-2': 'prsice', + 'BASIL': 'basil', + 'LGBM-PRSice': 'aml_prsice_vars', + 'LGBM-BASIL': 'aml_basil_vars', + 'LGBM-Threshold': 'aml_thresh_vars', + } + + for sim_name, model_name in tqdm( + product(sim_names, model_names), + desc='Loading performance data', + total=n_total, + ncols=100 + ): + res_fname = 'scores.json' + + res_path = os.path.join( + results_dir, desc_to_outdir[model_name], sim_name, res_fname + ) + + # Check that results exist + if not os.path.isfile(res_path): + print(f"CANNOT FIND: {res_path}") + continue + + # Load JSON of results + with open(res_path, 'r') as f: + res_data = json.load(f) + + res_data['Simulation Name'] = sim_name + res_data['Model'] = model_name + res_data['Feature Selection'] = feat_sel_dict[model_name] + + res_list.append(res_data) + + # Create dataframe of results + res_df = pd.DataFrame(res_list, ) + + # Add R^2 column based on test scores + res_df['R^2'] = res_df['test'].apply(lambda x: float(x['r2'])) + + # Make point plots + sns.set_style("whitegrid", {"axes.yaxis.grid": True}) + + # Save results df as csv + res_df.to_csv('results.csv', index=False) + + palette_prof_2 = [ + '#4E9A06', # Olive Green (Group C - C-A) + '#EFB73E', # Sun Yellow (Group C - C-B) + '#8AE234', # Light Green (Group C - C-A) + '#FFCE56', # Lighter Sun Yellow (Group C - C-B) + '#3465A4', # Royal Blue (Group A) + '#729FCF', # Light Royal Blue (Group A) + '#A40000', # Red (Group B) + '#EF2929' # Bright Red (Group B) + ] + + palette = palette_prof_2 + + print(res_df) + + g = sns.catplot( + kind='point', + data=res_df, + col='Simulation Name', + x='Model', + y='R^2', + hue='Model', + dodge=True, + hue_order=model_names, + # palette=palette, + sharey=True, + legend_out=False, + aspect=0.6, + markersize=20, + linewidth=10, + markers='_', + ) + + # Set y lims + y_min = 0 + y_max = 1.02 + for ax in g.axes.flat: + ax.set_ylim(y_min, y_max) + + # Make lines transparent + for ax in g.axes.flat: + for line in ax.lines: + line.set_alpha(0.5) + + # Add heritability data as dashed red line + for ax, sim_name in zip(g.axes.flat, sim_names): + # Filter herit_data for the current sim_name + herit_value = herit_data[herit_data["sim_name"] == sim_name]["heritability"].values + + # Check if there is a heritability value for the current sim_name + if len(herit_value) > 0: + ax.axhline( + y=herit_value[0], + color='#9955BB', + linestyle='--', + linewidth=3, + ) + # Set y-limit to 1.1 times the heritability value + # ax.set_ylim(0, 1.1 * herit_value[0]) + else: + print(f"No herit: {sim_name}") + + # Set the title to just the simulation name + if '_noise' in sim_name: + ax.set_title(sim_name.replace('_noise', '')) + elif 'gcta' in sim_name: + if 'FULL' in sim_name: + ax.set_title( + f"GCTA (H^2 = 1.0)" + ) + else: + gcta_h2 = sim_name.split('h')[1].split('_')[0] + ax.set_title( + f"GCTA (H^2 = 0.{gcta_h2})" + ) + elif 'gepsi' in sim_name: + if len(sim_name.split('h')) == 3: + gep_h2 = sim_name.split('h')[1].strip('_') + citrus_h2 = sim_name.split('h')[2] + + if gep_h2 == 'FULL': + gep_h2 = '1.0' + else: + gep_h2 = f"0.{gep_h2}" + + if citrus_h2 == 'FULL': + citrus_h2 = '1.0' + else: + citrus_h2 = f"0.{citrus_h2}" + + ax.set_title( + f"CITRUS (H^2 = {citrus_h2})" + ) + + elif 'FULL' in sim_name: + ax.set_title(f"GEPSi (H^2 = 1.0)") + else: + herit = sim_name.split('h')[1] + ax.set_title(f"GEPSi (H^2 = 0.{herit})") + elif 'lin_add' in sim_name: + herit = sim_name.split('h')[1].split('_')[0] + ax.set_title(f"Linear Additive (H^2 = 0.{herit})") + elif 'mult_pheno' in sim_name: + version_num = sim_name.split('_')[-1] + ax.set_title(f"Multiplicative {version_num}") + elif sim_name == "compound_het": + ax.set_title("Compound Het. (close)") + elif sim_name == "compound_het_2": + ax.set_title("Compound Het. (far)") + elif sim_name == "xor_pheno_2": + ax.set_title("Haplotype XOR (close)") + elif sim_name == "xor_pheno_1": + ax.set_title("Haplotype XOR (far)") + else: + ax.set_title(sim_name) + + # Rotate and left-align x labels + ax.set_xticklabels(ax.get_xticklabels(), rotation=-25, ha='left') + + # Shift x-tick labels to the left + from matplotlib.transforms import ScaledTranslation + dx = -0.10 # Adjust the value as needed + dy = 0 # No vertical shift + offset = ScaledTranslation(dx, dy, ax.figure.dpi_scale_trans) + for label in ax.xaxis.get_majorticklabels(): + label.set_transform(label.get_transform() + offset) + + g.set(ylim=(0, None)) + plt.tight_layout(w_pad=-1.0, rect=(0, 0, 1, 1)) + plt.savefig('r2_pointplot.png', dpi=600) + plt.close() + + # print(herit_data) + # print(res_df) \ No newline at end of file diff --git a/paper/workflows/plot_all/r2_pointplot.png b/paper/workflows/plot_all/r2_pointplot.png new file mode 100644 index 0000000..4228dda Binary files /dev/null and b/paper/workflows/plot_all/r2_pointplot.png differ diff --git a/paper/workflows/plot_all/results.csv b/paper/workflows/plot_all/results.csv new file mode 100644 index 0000000..481b844 --- /dev/null +++ b/paper/workflows/plot_all/results.csv @@ -0,0 +1,6 @@ +val,test,test_wb,test_nwb,Simulation Name,Model,Feature Selection,R^2 +"{'mse': 0.785274281010795, 'r2': 0.21807306414604133, 'mae': 0.7058715303282735, 'mape': 4.015055954602919, 'pearson_r': 0.4669829377461679, 'spearman_r': 0.4514129264066933}","{'mse': 0.7744312282133303, 'r2': 0.21503187006823765, 'mae': 0.7017610797917309, 'mape': 3.257397742930936, 'pearson_r': 0.4638176733467907, 'spearman_r': 0.446831201123695}","{'mse': 0.7754381363437405, 'r2': 0.21787388294597865, 'mae': 0.701692902456716, 'mape': 3.1346905935415337, 'pearson_r': 0.4668170218838325, 'spearman_r': 0.45007479489766483}","{'mse': 0.7692012011519797, 'r2': 0.19910772642100938, 'mae': 0.7021152027711071, 'mape': 3.894756497464379, 'pearson_r': 0.4470046606664253, 'spearman_r': 0.4290363362662805}",mult_pheno_1,PRSice-2,PRSice,0.21503187006823765 +"{'mse': 0.6866191062134374, 'r2': 0.3124318018228791, 'mae': 0.6603470054399613, 'mape': 5.226527603343583, 'pearson_r': 0.5589977771768755, 'spearman_r': 0.5424659581163407}","{'mse': 0.6711207834238603, 'r2': 0.3155383285609644, 'mae': 0.6558712816887481, 'mape': 2.672414412499285, 'pearson_r': 0.5618394217372689, 'spearman_r': 0.5399954899484091}","{'mse': 0.6726555953429066, 'r2': 0.3176869672105941, 'mae': 0.6568858240772144, 'mape': 2.7594927896794874, 'pearson_r': 0.563838148262876, 'spearman_r': 0.5417881756034486}","{'mse': 0.6631542911202665, 'r2': 0.30229562554937717, 'mae': 0.6506052656843846, 'mape': 2.220431200089403, 'pearson_r': 0.5510519479306437, 'spearman_r': 0.530207859741984}",mult_pheno_1,BASIL,BASIL,0.3155383285609644 +"{'mse': 0.6834419020576651, 'r2': 0.31947136786107844, 'mae': 0.6592741596913005, 'mape': 4.783451419039387, 'pearson_r': 0.5653577449360879, 'spearman_r': 0.5451897207306265}","{'mse': 0.6715191601437792, 'r2': 0.3193441584638611, 'mae': 0.6548164299607597, 'mape': 3.550773315610735, 'pearson_r': 0.5651735956985589, 'spearman_r': 0.5440356831702178}","{'mse': 0.6724945817151696, 'r2': 0.32170530273791365, 'mae': 0.6551907810299781, 'mape': 3.0684992204175683, 'pearson_r': 0.5673114958416211, 'spearman_r': 0.5460394963158947}","{'mse': 0.6664526788427299, 'r2': 0.30608948557049576, 'mae': 0.6528719961407046, 'mape': 6.055775006103999, 'pearson_r': 0.5534518766810252, 'spearman_r': 0.5330084006579648}",mult_pheno_1,LGBM-Threshold,p-val + window,0.3193441584638611 +"{'mse': 0.6850687901758081, 'r2': 0.31785141458875266, 'mae': 0.6601053728457593, 'mape': 4.8803721373540085, 'pearson_r': 0.5638744431924766, 'spearman_r': 0.5436714492340506}","{'mse': 0.6744065902054229, 'r2': 0.31641744206448974, 'mae': 0.6560352427648288, 'mape': 3.566546791903885, 'pearson_r': 0.5625210136308659, 'spearman_r': 0.5416322780659861}","{'mse': 0.6750121272158717, 'r2': 0.3191660439696318, 'mae': 0.6561115138870016, 'mape': 3.076534272565594, 'pearson_r': 0.5649955029043998, 'spearman_r': 0.5437812749547168}","{'mse': 0.6712613430317743, 'r2': 0.30108270452351615, 'mae': 0.6556390794795042, 'mape': 6.11174298124852, 'pearson_r': 0.5489935431279263, 'spearman_r': 0.5298518021964693}",mult_pheno_1,LGBM-PRSice,PRSice,0.31641744206448974 +"{'mse': 0.6812766115895174, 'r2': 0.3216274284655506, 'mae': 0.6585049709000937, 'mape': 4.886209646006848, 'pearson_r': 0.5673571688547691, 'spearman_r': 0.5471986231281639}","{'mse': 0.6696370814319307, 'r2': 0.3212518447153989, 'mae': 0.6538018143927553, 'mape': 3.5217885994935276, 'pearson_r': 0.5669545642805414, 'spearman_r': 0.5456462545454813}","{'mse': 0.6707001373907752, 'r2': 0.32351522374376684, 'mae': 0.6542083657840938, 'mape': 3.048724397044519, 'pearson_r': 0.5690270893348655, 'spearman_r': 0.5475088477736596}","{'mse': 0.6641154143954137, 'r2': 0.3085230452612021, 'mae': 0.6516901274231964, 'mape': 5.978952769532496, 'pearson_r': 0.5556245340159666, 'spearman_r': 0.5353606504764662}",mult_pheno_1,LGBM-BASIL,BASIL,0.3212518447153989 diff --git a/paper/workflows/plot_all/run_plot_all.sh b/paper/workflows/plot_all/run_plot_all.sh new file mode 100755 index 0000000..fe14bff --- /dev/null +++ b/paper/workflows/plot_all/run_plot_all.sh @@ -0,0 +1,17 @@ +# Score PRS predictions + +PHENO_DIR=pheno_sim/citrus_phenos +SPLITS_DIR=data/splits +TEMP_DIR=/tmp + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_score_preds:latest + +# Start docker env +module load singularitypro + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows/plot_all && \ + pip install tqdm && \ + python3 plot_all.py " \ No newline at end of file diff --git a/paper/workflows/plot_citrus_v_gepsi/citrus_vs_gcta_pheno.png b/paper/workflows/plot_citrus_v_gepsi/citrus_vs_gcta_pheno.png new file mode 100644 index 0000000..ed0ea20 Binary files /dev/null and b/paper/workflows/plot_citrus_v_gepsi/citrus_vs_gcta_pheno.png differ diff --git a/paper/workflows/plot_citrus_v_gepsi/citrus_vs_gepsi_pheno.png b/paper/workflows/plot_citrus_v_gepsi/citrus_vs_gepsi_pheno.png new file mode 100644 index 0000000..b42fed9 Binary files /dev/null and b/paper/workflows/plot_citrus_v_gepsi/citrus_vs_gepsi_pheno.png differ diff --git a/paper/workflows/plot_citrus_v_gepsi/gepsi_vs_gcta_pheno.png b/paper/workflows/plot_citrus_v_gepsi/gepsi_vs_gcta_pheno.png new file mode 100644 index 0000000..97cb3ca Binary files /dev/null and b/paper/workflows/plot_citrus_v_gepsi/gepsi_vs_gcta_pheno.png differ diff --git a/paper/workflows/plot_citrus_v_gepsi/plot_citrus_v_gepsi.py b/paper/workflows/plot_citrus_v_gepsi/plot_citrus_v_gepsi.py new file mode 100644 index 0000000..bfa5d3b --- /dev/null +++ b/paper/workflows/plot_citrus_v_gepsi/plot_citrus_v_gepsi.py @@ -0,0 +1,206 @@ +import json +import os +from itertools import product + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns + + +if __name__ == '__main__': + + citrus_pheno_dir = '../../pheno_sim/citrus_phenos' + gepsi_pheno_dir = '../../output/gepsi_phenos' + gcta_pheno_dir = '../../output/gcta_phenos' + + results_dir = '../../output' + pheno_name = 'phenotype' + + pheno_h2_fnames = ( # CITRUS, GEPSi, GCTA + 'gepsi_lin_add_h5_h5', + 'gepsi_lin_add_h5', + 'gepsi_lin_add_h5_gcta' + ) + pheno_hfull_fnames = ( + 'gepsi_lin_add_hFULL_hFULL', + 'gepsi_lin_add_hFULL', + 'gepsi_lin_add_hFULL_gcta' + ) + + # Load pheno data + citrus_h2 = pd.read_csv( + os.path.join(citrus_pheno_dir, pheno_h2_fnames[0] + '.pheno'), + sep='\s+' + ) + citrus_hfull = pd.read_csv( + os.path.join(citrus_pheno_dir, pheno_hfull_fnames[0] + '.pheno'), + sep='\s+' + ) + gepsi_h2 = pd.read_csv( + os.path.join(gepsi_pheno_dir, pheno_h2_fnames[1] + '.pheno'), + sep='\s+' + ) + gepsi_hfull = pd.read_csv( + os.path.join(gepsi_pheno_dir, pheno_hfull_fnames[1] + '.pheno'), + sep='\s+' + ) + gcta_h2 = pd.read_csv( + os.path.join(gcta_pheno_dir, pheno_h2_fnames[2] + '.pheno'), + sep='\s+' + ) + gcta_hfull = pd.read_csv( + os.path.join(gcta_pheno_dir, pheno_hfull_fnames[2] + '.pheno'), + sep='\s+' + ) + + # Join h2 and hfull for CITRUS and GEPSi on 'FID' and 'IID'. Suffix + # columns with CITRUS/GEPSI + sim_h2 = citrus_h2.merge( + gepsi_h2, on=['FID', 'IID'], suffixes=('_citrus', '_gepsi') + ) + sim_h2 = sim_h2.merge( + gcta_h2, on=['FID', 'IID'], suffixes=('', '_gcta') + ) + sim_h2 = sim_h2.rename(columns={'phenotype': 'phenotype_gcta'}) + print(sim_h2.head()) + + sim_hfull = citrus_hfull.merge( + gepsi_hfull, on=['FID', 'IID'], suffixes=('_citrus', '_gepsi') + ) + sim_hfull = sim_hfull.merge( + gcta_hfull, on=['FID', 'IID'], suffixes=('', '_gcta') + ) + sim_hfull = sim_hfull.rename(columns={'phenotype': 'phenotype_gcta'}) + print(sim_hfull.head()) + + + # Plot h2 and hfull as side by side scatter plots pf phenotype_citrus vs + # phenotype_gepsi + alpha = 0.5 + + fig, ax = plt.subplots(1, 2, figsize=(12, 6)) + g = sns.scatterplot( + x='phenotype_gepsi', + y='phenotype_citrus', + data=sim_h2, + alpha=alpha, + ax=ax[0] + ) + g.set_title('Heritability = 0.5') + ax[0].set_aspect('equal', adjustable='datalim') + + g = sns.scatterplot( + x='phenotype_gepsi', + y='phenotype_citrus', + data=sim_hfull, + alpha=alpha, + ax=ax[1] + ) + g.set_title('Heritability = 1.0') + ax[1].set_aspect('equal', adjustable='datalim') + + fig.suptitle('CITRUS vs GEPSi Phenotypes') + plt.tight_layout() + plt.savefig( + 'citrus_vs_gepsi_pheno.png', + dpi=600 + ) + plt.close() + + same_vals = np.isclose( + sim_hfull['phenotype_citrus'], + sim_hfull['phenotype_gepsi'], + rtol=1e-5, + atol=0.0 + ) + print(np.sum(same_vals)) + print(len(same_vals)) + print(np.sum(same_vals)/len(same_vals)) + print(sim_hfull.loc[~same_vals]) + print(np.all( + sim_hfull.loc[~same_vals]['IID'].astype(str).str.contains('-') + )) + + # Plot h2 and hfull as side by side scatter plots pf phenotype_citrus vs + # phenotype_gcta + fig, ax = plt.subplots(1, 2, figsize=(12, 6)) + g = sns.scatterplot( + x='phenotype_gcta', + y='phenotype_citrus', + data=sim_h2, + alpha=alpha, + ax=ax[0] + ) + g.set_title('Heritability = 0.5') + ax[0].set_aspect('equal', adjustable='datalim') + + g = sns.scatterplot( + x='phenotype_gcta', + y='phenotype_citrus', + data=sim_hfull, + alpha=alpha, + ax=ax[1] + ) + g.set_title('Heritability = 1.0') + ax[1].set_aspect('equal', adjustable='datalim') + + fig.suptitle('CITRUS vs GCTA Phenotypes') + plt.tight_layout() + plt.savefig( + 'citrus_vs_gcta_pheno.png', + dpi=600 + ) + plt.close() + + same_vals = np.isclose( + sim_hfull['phenotype_citrus'], + sim_hfull['phenotype_gcta'], + rtol=1e-5, + atol=0.0 + ) + print(np.sum(same_vals)) + print(len(same_vals)) + print(np.sum(same_vals)/len(same_vals)) + + # Plot h2 and hfull as side by side scatter plots pf phenotype_gepsi vs + # phenotype_gcta + fig, ax = plt.subplots(1, 2, figsize=(12, 6)) + g = sns.scatterplot( + x='phenotype_gcta', + y='phenotype_gepsi', + data=sim_h2, + alpha=alpha, + ax=ax[0] + ) + g.set_title('Heritability = 0.5') + ax[0].set_aspect('equal', adjustable='datalim') + + g = sns.scatterplot( + x='phenotype_gcta', + y='phenotype_gepsi', + data=sim_hfull, + alpha=alpha, + ax=ax[1] + ) + g.set_title('Heritability = 1.0') + ax[1].set_aspect('equal', adjustable='datalim') + + fig.suptitle('GEPSi vs GCTA Phenotypes') + plt.tight_layout() + plt.savefig( + 'gepsi_vs_gcta_pheno.png', + dpi=600 + ) + plt.close() + + same_vals = np.isclose( + sim_hfull['phenotype_gepsi'], + sim_hfull['phenotype_gcta'], + rtol=1e-5, + atol=0.0 + ) + print(np.sum(same_vals)) + print(len(same_vals)) + print(np.sum(same_vals)/len(same_vals)) + \ No newline at end of file diff --git a/paper/workflows/plot_citrus_v_gepsi/run_citrus_v_gepsi.sh b/paper/workflows/plot_citrus_v_gepsi/run_citrus_v_gepsi.sh new file mode 100755 index 0000000..1609bd9 --- /dev/null +++ b/paper/workflows/plot_citrus_v_gepsi/run_citrus_v_gepsi.sh @@ -0,0 +1,20 @@ +# Score PRS predictions + +PHENO_DIR=pheno_sim/citrus_phenos +SPLITS_DIR=data/splits +TEMP_DIR=/tmp + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_score_preds:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows/plot_citrus_v_gepsi && \ + python3 plot_citrus_v_gepsi.py " \ No newline at end of file diff --git a/paper/workflows/plot_manhattan/plot_manhattan.py b/paper/workflows/plot_manhattan/plot_manhattan.py new file mode 100644 index 0000000..c4d4380 --- /dev/null +++ b/paper/workflows/plot_manhattan/plot_manhattan.py @@ -0,0 +1,83 @@ +"""Make Manhattan and QQ plots from plink2 GLM output. + +Args: + -p, --pheno: Name of phenotype. + -g, --gwas-out-dir: Directory with GWAS sumary stats and where plots will + be saved. +""" + +import argparse +from pprint import pprint + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from geneview import manhattanplot, qqplot + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser( + description='Create Manhattan and QQ plots.' + ) + parser.add_argument( + '-p', '--pheno', + required=True, + help='Name of phenotype.' + ) + parser.add_argument( + '-g', '--gwas-out-dir', + required=True, + help='Directory with GWAS sumary stats and where plots will be saved.' + ) + return parser.parse_args() + + +if __name__ == '__main__': + # main() + + # Read args + args = parse_args() + print('Args:', flush=True) + pprint(vars(args)) + + # Read summary stats + ss_df = pd.read_csv( + f"{args.gwas_out_dir}/{args.pheno}.phenotype.glm.linear", + sep='\s+' + ) + + # Set 0 p-vals to next lowest + low_fill_val = ss_df[ss_df.P != 0].P.min() + ss_df.loc[ss_df.P == 0, 'P'] = low_fill_val + + # Bonferonni correct p-values + n_tests = len(ss_df) + print(n_tests) + ss_df['P_bonf'] = ss_df['P'] * n_tests + ss_df['P_bonf'] = ss_df['P_bonf'].clip(upper=1.0) + + print(ss_df.P_bonf.min()) + print(-np.log10(ss_df.P_bonf.min())) + + # Plot Manhattan + ax = manhattanplot( + data=ss_df, + pv="P", + CHR="19", + xlabel="Chromosome 19" + ) + plt.title(f"{args.pheno} Manhattan Plot") + plt.savefig( + f"{args.gwas_out_dir}/{args.pheno}_manhattan.png", + dpi=400 + ) + + # Plot QQ + ax = qqplot( + data=ss_df["P"] + ) + plt.savefig( + f"{args.gwas_out_dir}/{args.pheno}_qq.png", + dpi=400 + ) \ No newline at end of file diff --git a/paper/workflows/plot_manhattan/plot_manhattan_lin_add.py b/paper/workflows/plot_manhattan/plot_manhattan_lin_add.py new file mode 100644 index 0000000..38477c2 --- /dev/null +++ b/paper/workflows/plot_manhattan/plot_manhattan_lin_add.py @@ -0,0 +1,115 @@ +"""Make Manhattan and QQ plots from plink2 GLM output. + +Args: + -p, --pheno: Name of phenotype. + -g, --gwas-out-dir: Substring after 'h' that gives heritability level. +""" + +import argparse +from pprint import pprint + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +from geneview import manhattanplot, qqplot + + +def parse_args(): + """Parse command line arguments.""" + parser = argparse.ArgumentParser( + description='Create Manhattan and QQ plots.' + ) + parser.add_argument( + '--herit', + required=True, + help='Substring after "h" that gives heritability level.' + ) + parser.add_argument( + '-g', '--gwas-out-dir', + required=True, + help='Directory with GWAS sumary stats and where plots will be saved.' + ) + return parser.parse_args() + + +def prepare_data(file_path): + """Read and prepare summary stats.""" + df = pd.read_csv(file_path, sep='\s+') + + # Set 0 p-vals to next lowest + low_fill_val = df[df.P != 0].P.min() + df.loc[df.P == 0, 'P'] = low_fill_val + + # Bonferroni correct p-values + df['P_bonf'] = df['P'] * len(df) + df['P_bonf'] = df['P_bonf'].clip(upper=1.0) + + return df + + +if __name__ == '__main__': + # main() + + # Read args + args = parse_args() + print('Args:', flush=True) + pprint(vars(args)) + + # Read summary stats + citrus_ss = prepare_data( + f"{args.gwas_out_dir}/gepsi_lin_add_h{args.herit}_h{args.herit}.phenotype.glm.linear" + ) + gepsi_ss = prepare_data( + f"{args.gwas_out_dir}/gepsi_lin_add_h{args.herit}.phenotype.glm.linear" + ) + gcta_ss = prepare_data( + f"{args.gwas_out_dir}/gepsi_lin_add_h{args.herit}_gcta.phenotype.glm.linear" + ) + + # Create Manhattan plot figure + herit_str = 1.0 if args.herit == 'FULL' else '0.' + args.herit + + fig, axs = plt.subplots( + nrows=3, + ncols=1, + figsize=(10, 15), + sharex=True, + sharey=True, + ) + fig.suptitle(f'Linear-additive (H^2={herit_str}) Manhattan Plots', fontsize=16) + + # CITRUS Manhattan Plot + manhattanplot(data=citrus_ss, pv="P_bonf", CHR="19", xlabel="Chromosome 19", ax=axs[0]) + axs[0].set_title('CITRUS Manhattan Plot') + + # GEPSi Manhattan Plot + manhattanplot(data=gepsi_ss, pv="P_bonf", CHR="19", xlabel="Chromosome 19", ax=axs[1]) + axs[1].set_title('GEPSi Manhattan Plot') + + # GCTA Manhattan Plot + manhattanplot(data=gcta_ss, pv="P_bonf", CHR="19", xlabel="Chromosome 19", ax=axs[2]) + axs[2].set_title('GCTA Manhattan Plot') + + plt.tight_layout(rect=[0, 0.03, 1, 0.97]) + plt.savefig(f"{args.gwas_out_dir}/all_lin_add_h{args.herit}_manhattan_plots.png", dpi=400) + plt.close() + + # Create QQ plot figure + fig, axs = plt.subplots(nrows=3, ncols=1, figsize=(5, 15), sharex=True) + fig.suptitle(f'Linear-additive (H^2={herit_str}) QQ Plots', fontsize=16) + + # CITRUS QQ Plot + qqplot(data=citrus_ss["P_bonf"], ax=axs[0]) + axs[0].set_title('CITRUS QQ Plot') + + # GEPSi QQ Plot + qqplot(data=gepsi_ss["P_bonf"], ax=axs[1]) + axs[1].set_title('GEPSi QQ Plot') + + # GCTA QQ Plot + qqplot(data=gcta_ss["P_bonf"], ax=axs[2]) + axs[2].set_title('GCTA QQ Plot') + + plt.tight_layout(rect=[0, 0.03, 1, 0.97]) + plt.savefig(f"{args.gwas_out_dir}/all_lin_add_h{args.herit}_qq_plots.png", dpi=400) + plt.close() \ No newline at end of file diff --git a/paper/workflows/plot_manhattan/run_plot_lin_add_manhattan.sh b/paper/workflows/plot_manhattan/run_plot_lin_add_manhattan.sh new file mode 100755 index 0000000..7d68539 --- /dev/null +++ b/paper/workflows/plot_manhattan/run_plot_lin_add_manhattan.sh @@ -0,0 +1,26 @@ +# Plot Manhattan and QQ plots for GWAS + +# HERIT=0625 +# HERIT=125 +HERIT=25 +# HERIT=5 +# HERIT=FULL + +GWAS_OUT_DIR=../../output/GWAS + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_score_preds:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows/plot_manhattan && \ + pip install geneview && \ + python3 plot_manhattan_lin_add.py \ + --herit ${HERIT} --gwas-out-dir ${GWAS_OUT_DIR} " \ No newline at end of file diff --git a/paper/workflows/plot_manhattan/run_plot_manhattan.sh b/paper/workflows/plot_manhattan/run_plot_manhattan.sh new file mode 100755 index 0000000..abf3b5b --- /dev/null +++ b/paper/workflows/plot_manhattan/run_plot_manhattan.sh @@ -0,0 +1,44 @@ +# Plot Manhattan and QQ plots for GWAS + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +# PHENO=gepsi_lin_add_h0625 +# PHENO=gepsi_lin_add_h125 +# PHENO=gepsi_lin_add_h25 +# PHENO=gepsi_lin_add_h5 +# PHENO=gepsi_lin_add_hFULL +PHENO=gepsi_lin_add_n2_h001 + +# PHENO=gepsi_lin_add_h0625_gcta +# PHENO=gepsi_lin_add_h125_gcta +# PHENO=gepsi_lin_add_h25_gcta +# PHENO=gepsi_lin_add_h5_gcta +# PHENO=gepsi_lin_add_hFULL_gcta + +GWAS_OUT_DIR=../../output/GWAS + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_score_preds:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows/plot_manhattan && \ + pip install geneview && \ + python3 plot_manhattan.py --pheno ${PHENO} --gwas-out-dir ${GWAS_OUT_DIR} " \ No newline at end of file diff --git a/paper/workflows/plot_shap/feat_sel.png b/paper/workflows/plot_shap/feat_sel.png new file mode 100644 index 0000000..d089f12 Binary files /dev/null and b/paper/workflows/plot_shap/feat_sel.png differ diff --git a/paper/workflows/plot_shap/plot_shap.py b/paper/workflows/plot_shap/plot_shap.py new file mode 100644 index 0000000..6bb6905 --- /dev/null +++ b/paper/workflows/plot_shap/plot_shap.py @@ -0,0 +1,112 @@ +"""Plot feature selection performance.""" + +import json +import os + +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns + + +if __name__ == '__main__': + + sim_names = [ + 'mult_pheno_1', + 'mult_pheno_2', + 'compound_het', + 'compound_het_2', + 'xor_pheno_2', + 'xor_pheno_1', + 'gepsi_lin_add_h0625_h0625', + 'gepsi_lin_add_h125_h125', + 'gepsi_lin_add_h25_h25', + 'gepsi_lin_add_h5_h5', + 'gepsi_lin_add_hFULL_hFULL', + ] + + results_dir = '../../output/shap_feat_sel' + scores_fname = 'feature_selection_scores.json' + + # Load scores + scores = [] + for sim_name in sim_names: + with open(os.path.join(results_dir, sim_name, scores_fname), 'r') as f: + sim_scores = json.load(f) + + for method in sim_scores: + scores.append({ + 'simulation': sim_name, + 'feature_selection_method': method, + 'recall': sim_scores[method]['recall'], + 'precision': sim_scores[method]['precision'], + # 'f1': sim_scores[method]['f1'], + 'shapley_fraction': sim_scores[method]['shapley_fraction'], + }) + + scores = pd.DataFrame(scores) + + # Melt scores + melted_scores = pd.melt( + scores, + id_vars=['simulation', 'feature_selection_method'], + value_vars=['recall', 'precision', 'shapley_fraction'], + var_name='metric', + value_name='score', + ) + + # Plot + # Each row is a metric, each column is a simulation + sns.set_context("talk", font_scale=1.5) + sns.set_style("whitegrid", {"axes.yaxis.grid": True}) + + g = sns.catplot( + data=melted_scores, + x='feature_selection_method', + order=['prsice', 'basil', 'thresholds'], + y='score', + row='metric', + row_order=['shapley_fraction', 'recall', 'precision'], + col='simulation', + hue='feature_selection_method', + kind='bar', + sharey='row', + margin_titles=True, + ) + + # Modify titles + for ax, sim_name in zip(g.axes.flat, sim_names): + # Set the title to just the simulation name + if '_noise' in sim_name: + ax.set_title(sim_name.replace('_noise', '')) + elif 'lin_add' in sim_name: + herit = sim_name.split('h')[1].split('_')[0] + if herit == 'FULL': + ax.set_title(f"Linear Additive\n(H^2 = 1.0)") + else: + ax.set_title(f"Linear Additive\n(H^2 = 0.{herit})") + elif 'mult_pheno' in sim_name: + version_num = sim_name.split('_')[-1] + ax.set_title(f"Multiplicative {version_num}") + elif sim_name == "compound_het": + ax.set_title("Compound Het. (close)") + elif sim_name == "compound_het_2": + ax.set_title("Compound Het. (far)") + elif sim_name == "xor_pheno_2": + ax.set_title("Haplotype XOR (close)") + elif sim_name == "xor_pheno_1": + ax.set_title("Haplotype XOR (far)") + else: + ax.set_title(sim_name) + + # Set row titles + row_titles = ["Shapley fraction", "Recall", "Precision"] + for ax, title in zip(g.axes[:,0], row_titles): + ax.set_ylabel(title, labelpad=20) + + # Adjust the font size of x-category labels and remove x-axis titles + g.set(xlabel='') + for ax in g.axes.flat: + ax.tick_params(axis='x', labelsize=18) + + plt.savefig('feat_sel.png', dpi=600) + plt.close() \ No newline at end of file diff --git a/paper/workflows/plot_shap/run_plot_shap.sh b/paper/workflows/plot_shap/run_plot_shap.sh new file mode 100755 index 0000000..7447cfa --- /dev/null +++ b/paper/workflows/plot_shap/run_plot_shap.sh @@ -0,0 +1,20 @@ +# Plot feature selection performance + +PHENO_DIR=pheno_sim/citrus_phenos +SPLITS_DIR=data/splits +TEMP_DIR=/tmp + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_score_preds:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows/plot_shap && \ + python3 plot_shap.py " \ No newline at end of file diff --git a/paper/workflows/plot_sim/run_plot_sim.sh b/paper/workflows/plot_sim/run_plot_sim.sh new file mode 100755 index 0000000..bd62d2a --- /dev/null +++ b/paper/workflows/plot_sim/run_plot_sim.sh @@ -0,0 +1,39 @@ +# Plot simulation graph + +# PHENO=complex_pheno_1 +# PHENO=complex_pheno_1_noise +# PHENO=complex_pheno_2 +# PHENO=complex_pheno_3 +# PHENO=complex_pheno_4 +# PHENO=mult_pheno_1_more_noise +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=lin_add_nvar1000_h2_v1 +# PHENO=lin_add_nvar1000_h4_v1 +# PHENO=compound_het +PHENO=gepsi_lin_add_h25_h25 + +echo ${PHENO} + +TEMP_DIR=/tmp +SIM_CONFIG=sim_configs + +DOCKER_URL=gcr.io/ucsd-medicine-cast/citrus:latest +N_THREADS=$(lscpu | grep "^CPU(s):" | awk '{print $2}') + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://gcr.io/ucsd-medicine-cast/citrus:latest + +singularity exec --containall --cleanenv --writable-tmpfs \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/pheno_sim/ && \ + citrus plot \ + --config_file ${SIM_CONFIG}/${PHENO}.json \ + --out ${SIM_CONFIG}/${PHENO} \ + --format png " \ No newline at end of file diff --git a/paper/workflows/prs_anova/critical_difference_diagram_datasets.png b/paper/workflows/prs_anova/critical_difference_diagram_datasets.png new file mode 100644 index 0000000..d67a417 Binary files /dev/null and b/paper/workflows/prs_anova/critical_difference_diagram_datasets.png differ diff --git a/paper/workflows/prs_anova/critical_difference_diagram_models.png b/paper/workflows/prs_anova/critical_difference_diagram_models.png new file mode 100644 index 0000000..7c4da41 Binary files /dev/null and b/paper/workflows/prs_anova/critical_difference_diagram_models.png differ diff --git a/paper/workflows/prs_anova/prs_anova.py b/paper/workflows/prs_anova/prs_anova.py new file mode 100644 index 0000000..21f548c --- /dev/null +++ b/paper/workflows/prs_anova/prs_anova.py @@ -0,0 +1,197 @@ +import json +import os +from itertools import product + +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +from tqdm import tqdm +import scipy.stats as stats +import scikit_posthocs as sp + + +if __name__ == '__main__': + + results_dir = '../../output' + pheno_name = 'phenotype' + + sim_names = [ + 'mult_pheno_1', # Multiplicative 1 + 'mult_pheno_2', # Multiplicative 2 + 'compound_het', # Compound het close pair of SNPs + 'compound_het_2', # Compound het far pair of SNPs + 'xor_pheno_2', # XOR close + 'xor_pheno_1', # XOR far + 'gepsi_lin_add_h0625_h0625',# CITRUS lin-add h^2 = 0.0625 + 'gepsi_lin_add_h0625', # GEPSi lin-add h^2 = 0.0625 + 'gepsi_lin_add_h0625_gcta', # GCTA lin-add h^2 = 0.0625 + 'gepsi_lin_add_h125_h125', # CITRUS lin-add h^2 = 0.125 + 'gepsi_lin_add_h125', # GEPSi lin-add h^2 = 0.125 + 'gepsi_lin_add_h125_gcta', # GCTA lin-add h^2 = 0.125 + 'gepsi_lin_add_h25_h25', # CITRUS lin-add h^2 = 0.25 + 'gepsi_lin_add_h25', # GEPSi lin-add h^2 = 0.25 + 'gepsi_lin_add_h25_gcta', # GCTA lin-add h^2 = 0.25 + 'gepsi_lin_add_h5_h5', # CITRUS lin-add h^2 = 0.5 + 'gepsi_lin_add_h5', # GEPSi lin-add h^2 = 0.5 + 'gepsi_lin_add_h5_gcta', # GCTA lin-add h^2 = 0.5 + 'gepsi_lin_add_hFULL_hFULL',# CITRUS lin-add h^2 = 1.0 + 'gepsi_lin_add_hFULL', # GEPSi lin-add h^2 = 1.0 + 'gepsi_lin_add_hFULL_gcta', # GCTA lin-add h^2 = 1.0 + ] + + model_names = [ + 'PRSice-2', + 'BASIL', + 'LGBM-Threshold', + 'LGBM-PRSice', + 'LGBM-BASIL', + ] + + feat_sel_dict = { + 'PRSice-2': 'PRSice', + 'BASIL': 'BASIL', + 'LGBM-PRSice': 'PRSice', + 'LGBM-BASIL': 'BASIL', + 'LGBM-Threshold': 'p-val + window' + } + + ci = "95" + herit_data_dir = '../../pheno_sim/citrus_output' + + # Load all results + res_list = [] + + n_total = len(sim_names) * len(model_names) + + # Desc to model output dir + desc_to_outdir = { + 'PRSice-2': 'prsice', + 'BASIL': 'basil', + 'LGBM-PRSice': 'aml_prsice_vars', + 'LGBM-BASIL': 'aml_basil_vars', + 'LGBM-Threshold': 'aml_thresh_vars', + } + + for sim_name, model_name in tqdm( + product(sim_names, model_names), + desc='Loading performance data', + total=n_total, + ncols=100 + ): + res_fname = 'scores.json' + + res_path = os.path.join( + results_dir, desc_to_outdir[model_name], sim_name, res_fname + ) + + # Check that results exist + if not os.path.isfile(res_path): + print(f"CANNOT FIND: {res_path}") + continue + + # Load JSON of results + with open(res_path, 'r') as f: + res_data = json.load(f) + + res_data['Simulation Name'] = sim_name + res_data['Model'] = model_name + res_data['Feature Selection'] = feat_sel_dict[model_name] + + res_list.append(res_data) + + # Create dataframe of results + res_df = pd.DataFrame(res_list, ) + + # Add R^2 column based on test scores + res_df['R^2'] = res_df['test'].apply(lambda x: float(x['r2'])) + + # Perform Kruskal-Wallis test for Models + kruskal_model = stats.kruskal( + *[res_df[res_df['Model'] == model]['R^2'] for model in model_names] + ) + print('Kruskal-Wallis test result for models:', kruskal_model) + + # Perform Kruskal-Wallis test for Datasets + kruskal_dataset = stats.kruskal( + *[res_df[res_df['Simulation Name'] == sim]['R^2'] for sim in sim_names] + ) + print('Kruskal-Wallis test result for datasets:', kruskal_dataset) + + # Perform post-hoc analysis for models using Dunn's test + posthoc_model = sp.posthoc_conover(res_df, val_col='R^2', group_col='Model', p_adjust='bonferroni') + # print(posthoc_model) + + # Perform post-hoc analysis for datasets using Dunn's test + posthoc_dataset = sp.posthoc_conover(res_df, val_col='R^2', group_col='Simulation Name', p_adjust='bonferroni') + # print(posthoc_dataset) + + # Visualize with a boxplot and save the plots + plt.figure(figsize=(10, 6)) + sns.boxplot(x='Simulation Name', y='R^2', hue='Model', data=res_df) + plt.title('R^2 Scores by Model and Simulation') + plt.xlabel('Simulation Name') + plt.ylabel('R^2') + plt.legend(title='Model') + plt.xticks(rotation=90) + plt.tight_layout() + plt.savefig('r2_scores_by_model_and_simulation.png') + plt.close() + + plt.figure(figsize=(10, 6)) + sns.boxplot(x='Model', y='R^2', hue='Simulation Name', data=res_df) + plt.title('R^2 Scores by Simulation and Model') + plt.xlabel('Model') + plt.ylabel('R^2') + plt.legend(title='Simulation Name') + plt.xticks(rotation=90) + plt.tight_layout() + plt.savefig('r2_scores_by_simulation_and_model.png') + plt.close() + + # Generate and save significance plot for models + plt.figure(figsize=(10, 6)) + sp.sign_plot(posthoc_model, alpha=0.05) + plt.title('Significance Plot for Models') + plt.xlabel('Model') + plt.ylabel('Significance') + plt.tight_layout() + plt.savefig('significance_plot_models.png') + plt.close() + + # Generate and save significance plot for datasets + plt.figure(figsize=(10, 6)) + sp.sign_plot(posthoc_dataset, alpha=0.05) + plt.title('Significance Plot for Datasets') + plt.xlabel('Simulation Name') + plt.ylabel('Significance') + plt.tight_layout() + plt.savefig('significance_plot_datasets.png') + plt.close() + + # Generate and save critical difference diagram for models + + # Calculate average ranks for models + res_df['Rank_Model'] = res_df.groupby('Simulation Name')['R^2'].rank(pct=True) + avg_rank_model = res_df.groupby('Model')['Rank_Model'].mean() + print('Average ranks for models:', avg_rank_model) + + # Plot critical difference diagram for models + plt.figure(figsize=(10, 2), dpi=100) + plt.title('Critical difference diagram of average score ranks for models') + sp.critical_difference_diagram(avg_rank_model, posthoc_model) + plt.savefig('critical_difference_diagram_models.png') + plt.close() + + # Generate and save critical difference diagram for datasets + + # Calculate average ranks for datasets + res_df['Rank_Sim'] = res_df.groupby('Model')['R^2'].rank(pct=True) + avg_rank_sim = res_df.groupby('Simulation Name')['Rank_Sim'].mean() + print('Average ranks for datasets:', avg_rank_sim) + + # Plot critical difference diagram for datasets + plt.figure(figsize=(10, 2), dpi=100) + plt.title('Critical difference diagram of average score ranks for datasets') + sp.critical_difference_diagram(avg_rank_sim, posthoc_dataset) + plt.savefig('critical_difference_diagram_datasets.png') + plt.close() \ No newline at end of file diff --git a/paper/workflows/prs_anova/run_anova.sh b/paper/workflows/prs_anova/run_anova.sh new file mode 100755 index 0000000..463b3a2 --- /dev/null +++ b/paper/workflows/prs_anova/run_anova.sh @@ -0,0 +1,16 @@ +# Score PRS predictions + +PHENO_DIR=pheno_sim/citrus_phenos +SPLITS_DIR=data/splits +TEMP_DIR=/tmp + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_score_preds:latest + +# Start docker env +module load singularitypro + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows/prs_anova && \ + python3 prs_anova.py " \ No newline at end of file diff --git a/paper/workflows/prs_anova/significance_plot_datasets.png b/paper/workflows/prs_anova/significance_plot_datasets.png new file mode 100644 index 0000000..a8cc099 Binary files /dev/null and b/paper/workflows/prs_anova/significance_plot_datasets.png differ diff --git a/paper/workflows/prs_anova/significance_plot_models.png b/paper/workflows/prs_anova/significance_plot_models.png new file mode 100644 index 0000000..1c15ea6 Binary files /dev/null and b/paper/workflows/prs_anova/significance_plot_models.png differ diff --git a/paper/workflows/prsice2/run_prsice.sh b/paper/workflows/prsice2/run_prsice.sh new file mode 100755 index 0000000..7869c7c --- /dev/null +++ b/paper/workflows/prsice2/run_prsice.sh @@ -0,0 +1,118 @@ +# Run PRSice2 PRS on CITRUS simulated phenotype + + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +# PHENO=gepsi_lin_add_h0625 +# PHENO=gepsi_lin_add_h125 +# PHENO=gepsi_lin_add_h25 +# PHENO=gepsi_lin_add_h5 +# PHENO=gepsi_lin_add_hFULL + +# PHENO=gepsi_lin_add_h0625_gcta +# PHENO=gepsi_lin_add_h125_gcta +# PHENO=gepsi_lin_add_h25_gcta +# PHENO=gepsi_lin_add_h5_gcta +PHENO=gepsi_lin_add_hFULL_gcta + +# PHENO_DIR=pheno_sim/citrus_phenos +# PHENO_DIR=output/gepsi_phenos +PHENO_DIR=output/gcta_phenos + +echo ${PHENO} +echo ${PHENO_DIR} + +TEMP_DIR=/tmp +OUT_DIR=output/prsice/${PHENO} +GWAS_OUT_DIR=output/GWAS + +SPLITS_DIR=data/splits +VAL_SPLIT_FNAME=val_samples.txt +TEST_SPLIT_FNAME=test_samples.txt +GENO_DIR=data/geno_data/bed +BED_FNAME=imputed_chr19 +PC_DIR=data/geno_data/PCs + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_prsice2:latest +N_THREADS=$(lscpu | grep "^CPU(s):" | awk '{print $2}') + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + mkdir -p ${OUT_DIR} && \ + echo 'made out dir' && \ + PRSice_linux \ + --base ${GWAS_OUT_DIR}/${PHENO}.phenotype.glm.linear \ + --extract ${GENO_DIR}/prsice_valid_SNPs.valid \ + --A1 A1 \ + --beta \ + --bp POS \ + --chr '#CHROM' \ + --snp ID \ + --pvalue P \ + --target ${GENO_DIR}/${BED_FNAME} \ + --nonfounders \ + --keep ${SPLITS_DIR}/${VAL_SPLIT_FNAME} \ + --ignore-fid \ + --pheno ${PHENO_DIR}/${PHENO}.pheno \ + --cov ${PC_DIR}/pcs.tsv \ + --thread ${N_THREADS} \ + --out ${OUT_DIR}/prs_prsice2_${PHENO} \ + && \ + BEST_P_THRESH=\$(tail -n 1 ${OUT_DIR}/prs_prsice2_${PHENO}.summary | awk '{print \$3}') \ + && \ + echo \${BEST_P_THRESH} \ + && \ + printf 'best_p 0.0 %s\n' \"\$BEST_P_THRESH\" > \"${OUT_DIR}/prsice2_${PHENO}_best_p_thresh.txt\" \ + && \ + head ${OUT_DIR}/prsice2_${PHENO}_best_p_thresh.txt \ + && \ + plink2 \ + --bfile ${GENO_DIR}/${BED_FNAME} \ + --extract ${GENO_DIR}/prsice_valid_SNPs.valid \ + --clump-p1 1 \ + --clump-r2 0.1 \ + --clump-kb 250 \ + --clump ${GWAS_OUT_DIR}/${PHENO}.phenotype.glm.linear \ + --out ${OUT_DIR}/prs_prsice2_clump_${PHENO} \ + && \ + echo MKDIR \ + && \ + mkdir -p ${OUT_DIR} \ + && \ + plink2 \ + --bfile ${GENO_DIR}/${BED_FNAME} \ + --score ${GWAS_OUT_DIR}/${PHENO}.phenotype.glm.linear 3 7 12 header \ + --extract ${OUT_DIR}/prs_prsice2_clump_${PHENO}.clumps \ + --q-score-range ${OUT_DIR}/prsice2_${PHENO}_best_p_thresh.txt \ + ${GWAS_OUT_DIR}/${PHENO}.phenotype.glm.linear 3 15 header \ + --out ${OUT_DIR}/prs_prsice2_score_${PHENO} \ + && \ + awk -v OFS='\t' '{ print \$2, \$3 }' ${PHENO_DIR}/${PHENO}.pheno > \ + ${OUT_DIR}/reformatted_${PHENO}.tsv \ + && \ + python3 /home/fit_wrapper.py \ + --pheno-file ${OUT_DIR}/reformatted_${PHENO}.tsv \ + --covar-file ${PC_DIR}/pcs_just_IID.tsv \ + --score-file ${OUT_DIR}/prs_prsice2_score_${PHENO}.best_p.sscore \ + --val-iids ${SPLITS_DIR}/${VAL_SPLIT_FNAME} \ + --test-iids ${SPLITS_DIR}/${TEST_SPLIT_FNAME} \ + --out-dir ${OUT_DIR} " \ No newline at end of file diff --git a/paper/workflows/score_preds/run_score_preds.sh b/paper/workflows/score_preds/run_score_preds.sh new file mode 100755 index 0000000..5df8ee2 --- /dev/null +++ b/paper/workflows/score_preds/run_score_preds.sh @@ -0,0 +1,77 @@ +# Score PRS predictions + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +# PHENO=gepsi_lin_add_h0625 +# PHENO=gepsi_lin_add_h125 +# PHENO=gepsi_lin_add_h25 +# PHENO=gepsi_lin_add_h5 +# PHENO=gepsi_lin_add_hFULL + +# PHENO=gepsi_lin_add_h0625_gcta +# PHENO=gepsi_lin_add_h125_gcta +# PHENO=gepsi_lin_add_h25_gcta +# PHENO=gepsi_lin_add_h5_gcta +PHENO=gepsi_lin_add_hFULL_gcta + +# PHENO_DIR=pheno_sim/citrus_phenos +# PHENO_DIR=output/gepsi_phenos +PHENO_DIR=output/gcta_phenos + +echo ${PHENO} +echo ${PHENO_DIR} + +# MODEL_TYPE=prsice +# MODEL_TYPE=basil +# MODEL_TYPE=lgbm_thresh +# MODEL_TYPE=lgbm_prsice +MODEL_TYPE=lgbm_basil + +echo ${MODEL_TYPE} + +if [ "$MODEL_TYPE" = prsice ]; then + MODEL_OUT_DIR=output/prsice/${PHENO} +elif [ "$MODEL_TYPE" = basil ]; then + MODEL_OUT_DIR=output/basil/${PHENO} +elif [ "$MODEL_TYPE" = lgbm_basil ]; then + MODEL_OUT_DIR=output/aml_basil_vars/${PHENO} +elif [ "$MODEL_TYPE" = lgbm_prsice ]; then + MODEL_OUT_DIR=output/aml_prsice_vars/${PHENO} +elif [ "$MODEL_TYPE" = lgbm_thresh ]; then + MODEL_OUT_DIR=output/aml_thresh_vars/${PHENO} +else + echo "Invalid model type" + exit 1 +fi + +SPLITS_DIR=data/splits +TEMP_DIR=/tmp + +DOCKER_URL=gcr.io/ucsd-medicine-cast/nonlin_prs_prs_score_preds:latest + +# Start docker env +module load singularitypro + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper && \ + awk -v OFS='\t' '{ print \$2, \$3 }' ${PHENO_DIR}/${PHENO}.pheno > \ + ${TEMP_DIR}/reformatted_${PHENO}.tsv \ + && \ + python3 /mnt/citrus/CITRUS/paper/workflows/score_preds/score_preds.py \ + --val-preds ${MODEL_OUT_DIR}/val_preds.csv \ + --test-preds ${MODEL_OUT_DIR}/test_preds.csv \ + --pheno-file ${TEMP_DIR}/reformatted_${PHENO}.tsv \ + --wb ${SPLITS_DIR}/wb_samples.txt \ + --out-dir ${MODEL_OUT_DIR}" \ No newline at end of file diff --git a/paper/workflows/score_preds/score_preds.py b/paper/workflows/score_preds/score_preds.py new file mode 100644 index 0000000..cdacf4e --- /dev/null +++ b/paper/workflows/score_preds/score_preds.py @@ -0,0 +1,204 @@ +"""Score and plot PRS predictions. + +Outputs scores.json and plots. + +Args: + +* -v, --val-preds: Path to validation set predictions CSV file. +* -t, --test-preds: Path to test set predictions CSV file. +* -p, --pheno-file: Path to ground true phenotype file. +* --wb: Split file for white British samples. +* -o, --out-dir: Path to output directory. +""" + +import argparse +import json +import os + +import numpy as np +import pandas as pd +import matplotlib.pyplot as plt +import seaborn as sns +from scipy import stats +from sklearn import metrics + + +def parse_args(): + parser = argparse.ArgumentParser() + + parser.add_argument("-v", "--val-preds", required=True) + parser.add_argument("-t", "--test-preds", required=True) + parser.add_argument("-p", "--pheno-file", required=True) + parser.add_argument("--wb", required=True) + parser.add_argument("-o", "--out-dir", required=True) + + return parser.parse_args() + + +def score_and_plot_preds( + y_true, + y_pred, + out_dir, + desc=None, + plot_prefix='' +): + """Score regression predictions and plot pred v true jointplot. + + Returns dict with the following keys: + - mse: Mean squared error + - r2: R-squared + - mae: Mean absolute error + - mape: Mean absolute percentage error + - pearson_r: Pearson correlation coefficient + - spearman_r: Spearman correlation coefficient + + Args: + y_true: Ground truth. + y_pred: Predictions. + desc: Description for plot title. If None, no title. + plot_prefix: Prefix for plot filename. + """ + + # Score predictions + mse = metrics.mean_squared_error(y_true, y_pred) + r2 = metrics.r2_score(y_true, y_pred) + mae = metrics.mean_absolute_error(y_true, y_pred) + mape = metrics.mean_absolute_percentage_error(y_true, y_pred) + pearson_r = stats.pearsonr(y_true, y_pred).statistic # type: ignore + spearman_r = stats.spearmanr(y_true, y_pred).statistic # type: ignore + + # Plot predictions vs ground truth + g = sns.jointplot( + x=y_true, + y=y_pred, + kind='scatter', + joint_kws={'marker': '.', 'alpha': 0.3} + ) + g.ax_joint.set_aspect('equal', adjustable='box') + + # Get the overall min/max of the data + min_val = min(y_true.min(), y_pred.min()) + max_val = max(y_true.max(), y_pred.max()) + + # Set the limits + g.ax_joint.set_xlim(min_val, max_val) + g.ax_joint.set_ylim(min_val, max_val) + + # Add dashed line for x=y + g.ax_joint.plot([min_val, max_val], [min_val, max_val], 'k--') + + g.ax_joint.set_xlabel('True') + g.ax_joint.set_ylabel('Predicted') + if desc is not None: + g.ax_joint.set_title(desc) + else: + g.ax_joint.set_title('Pred v True Phenotype') + plt.tight_layout() + + # Save plot + if plot_prefix != '': + plot_prefix = plot_prefix + '_' + plt.savefig( + os.path.join(out_dir, f'{plot_prefix}jointplot.png'), + dpi=300 + ) + plt.close() + + return { + 'mse': mse, + 'r2': r2, + 'mae': mae, + 'mape': mape, + 'pearson_r': pearson_r, + 'spearman_r': spearman_r + } + + +if __name__ == '__main__': + + args = parse_args() + + # Load predictions + val_preds = pd.read_csv(args.val_preds) + test_preds = pd.read_csv(args.test_preds) + + # Load phenotype + pheno = pd.read_csv(args.pheno_file, sep='\s+') + pheno_col = pheno.columns.difference(['IID']).values[0] + pheno = pheno.rename(columns={pheno_col: 'true'}) + + # Format pheno IID + if '_' not in str(pheno['IID'].values[0]): + pheno['IID'] = pheno['IID'].apply(lambda x: str(x) + '_' + str(x)) + if '_' not in str(val_preds['IID'].values[0]): + val_preds['IID'] = val_preds['IID'].apply(lambda x: str(x) + '_' + str(x)) + if '_' not in str(test_preds['IID'].values[0]): + test_preds['IID'] = test_preds['IID'].apply(lambda x: str(x) + '_' + str(x)) + + # print('pheno') + # print(pheno) + # print('val_preds') + # print(val_preds) + # print('test_preds') + # print(test_preds) + + # Join predictions with ground truth + val_preds = val_preds.merge(pheno, on='IID', how='inner') + test_preds = test_preds.merge(pheno, on='IID', how='inner') + + # Load white British split + wb_iids = pd.read_csv(args.wb, sep='\s+', header=None).values.flatten() + wb_iids = [str(i) + '_' + str(i) for i in wb_iids] + + # print('wb_iids') + # print(wb_iids) + + # Get pred-true sets for WB and non-WB for test set + test_wb = test_preds[test_preds['IID'].isin(wb_iids)] + test_nwb = test_preds[~test_preds['IID'].isin(wb_iids)] + + # Score and plot + val_scores = score_and_plot_preds( + val_preds['true'], + val_preds['pred'], + args.out_dir, + desc='Validation', + plot_prefix='val' + ) + + test_all_scores = score_and_plot_preds( + test_preds['true'], + test_preds['pred'], + args.out_dir, + desc='Test', + plot_prefix='test' + ) + + test_wb_scores = score_and_plot_preds( + test_wb['true'], + test_wb['pred'], + args.out_dir, + desc='Test White British', + plot_prefix='test_wb' + ) + + test_nwb_scores = score_and_plot_preds( + test_nwb['true'], + test_nwb['pred'], + args.out_dir, + desc='Test not White British', + plot_prefix='test_nwb' + ) + + # Save scores + scores = { + 'val': val_scores, + 'test': test_all_scores, + 'test_wb': test_wb_scores, + 'test_nwb': test_nwb_scores + } + + with open(os.path.join(args.out_dir, 'scores.json'), 'w') as f: + json.dump(scores, f, indent=4) + + diff --git a/paper/workflows/shap_by_loci/run_shap_by_loci.sh b/paper/workflows/shap_by_loci/run_shap_by_loci.sh new file mode 100755 index 0000000..e5163fd --- /dev/null +++ b/paper/workflows/shap_by_loci/run_shap_by_loci.sh @@ -0,0 +1,41 @@ +# Get local SHAP values + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +echo ${PHENO} + +OUT_DIR=../output/shap_per_loci/${PHENO} +TEMP_DIR=/tmp + +SHAP_OUT_DIR=../output/shap/${PHENO} +SIM_OUT_DIR=../pheno_sim/citrus_output +SIM_CONFIG=${SIM_OUT_DIR}/${PHENO}_config.json + +DOCKER_URL=gcr.io/ucsd-medicine-cast/citrus:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows && \ + mkdir -p ${OUT_DIR} && \ + python3 shap_by_loci/shap_by_loci.py \ + -s ${SHAP_OUT_DIR}/shap_vals.csv \ + -c ${SIM_CONFIG} \ + -o ${OUT_DIR} " diff --git a/paper/workflows/shap_by_loci/shap_by_loci.py b/paper/workflows/shap_by_loci/shap_by_loci.py new file mode 100644 index 0000000..0025a99 --- /dev/null +++ b/paper/workflows/shap_by_loci/shap_by_loci.py @@ -0,0 +1,101 @@ +"""Compute Shapley fraction per loci so can be used to evaluate +feature selection. + +Args: + -s, --shapley: CSV with Shapley values per loci per haplotype + -c, --simulation-config: JSON with simulation configuration + -o, --output-dir: Directory to save output files. +""" + +import argparse +import os +import json + +import numpy as np +import pandas as pd + + +s='../output/shap/mult_pheno_1/shap_vals.csv' +c='../pheno_sim/citrus_output/mult_pheno_1_config.json' + + +def parse_args(): + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument('-s', '--shapley', required=True) + parser.add_argument('-c', '--simulation-config', required=True) + parser.add_argument('-o', '--output-dir', required=True) + return parser.parse_args() + + +if __name__ == '__main__': + args = parse_args() + + # Load Shapley values + shap_vals = pd.read_csv( + args.shapley + ).set_index('sample_id') + + # Load simulation configuration + with open(args.simulation_config, 'r') as f: + input_config = json.load(f)['input'] + + # Get absolute Shapley values, then combine loci on both haplotypes + shap_vals = shap_vals.abs() + + comb_shap_vals = pd.DataFrame( + index=shap_vals.index + ) + + # Find which columns correspond to the same loci + loci_to_cols = dict() + for col_name in shap_vals.columns: + split_name = col_name.split('*-*') + if len(split_name) == 3: + input, strand, idx = split_name + elif len(split_name) == 2: + input, strand = split_name + idx = '0' + + if input not in loci_to_cols: + loci_to_cols[input] = dict() + + if idx not in loci_to_cols[input]: + loci_to_cols[input][idx] = [] + + loci_to_cols[input][idx].append(col_name) + + # Combine loci + for input in loci_to_cols: + for idx in loci_to_cols[input]: + # Assert two columns per loci + assert len(loci_to_cols[input][idx]) == 2 + + # Combine Shapley values + comb_shap_vals[input + '*-*' + idx] = shap_vals[ + loci_to_cols[input][idx] + ].sum(axis=1) + + # Rename loci to position + col_to_pos_map = dict() + input_node_map = dict() + + for input_node in input_config[0]['input_nodes']: + input_node_map[input_node['alias']] = input_node['pos'] + + for col in comb_shap_vals.columns: + input, idx = col.split('*-*') + col_to_pos_map[col] = input_node_map[input][int(idx)] + + comb_shap_vals = comb_shap_vals.rename(columns=col_to_pos_map) + + # Scale so each row sums to 1 + comb_shap_vals = comb_shap_vals.div( + comb_shap_vals.sum(axis=1), + axis=0 + ) + + # Save combined Shapley values + comb_shap_vals.reset_index().to_csv( + os.path.join(args.output_dir, 'comb_shap_vals.csv'), + index=False + ) diff --git a/paper/workflows/shap_feat_sel/run_shap_feat_sel.sh b/paper/workflows/shap_feat_sel/run_shap_feat_sel.sh new file mode 100755 index 0000000..4125c4b --- /dev/null +++ b/paper/workflows/shap_feat_sel/run_shap_feat_sel.sh @@ -0,0 +1,50 @@ +# Get local SHAP values + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +echo ${PHENO} + +OUT_DIR=../output/shap_feat_sel/${PHENO} +TEMP_DIR=/tmp + +VAR_INFO=../data/geno_data/chr19_var_info.csv + +BASIL_OUT_DIR=../output/aml_filter_vars_basil/${PHENO} +PRSICE_OUT_DIR=../output/aml_filter_vars_prsice/${PHENO} +THRESH_OUT_DIR=../output/aml_filter_vars/${PHENO} +THRESH_BEST_DIR=../output/aml_thresh_vars/${PHENO} + +SHAP_DIR=../output/shap_per_loci/${PHENO} + +DOCKER_URL=gcr.io/ucsd-medicine-cast/citrus:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows && \ + mkdir -p ${OUT_DIR} && \ + python3 shap_feat_sel/shap_by_feat_sel.py \ + -s ${SHAP_DIR}/comb_shap_vals.csv \ + -b ${BASIL_OUT_DIR}/filtered_vars.json \ + -p ${PRSICE_OUT_DIR}/filtered_vars.json \ + -t ${THRESH_OUT_DIR}/filtered_vars.json \ + -m ${THRESH_BEST_DIR}/best_model_config.json \ + -v ${VAR_INFO} \ + -o ${OUT_DIR} " diff --git a/paper/workflows/shap_feat_sel/shap_by_feat_sel.py b/paper/workflows/shap_feat_sel/shap_by_feat_sel.py new file mode 100644 index 0000000..a9a6fe0 --- /dev/null +++ b/paper/workflows/shap_feat_sel/shap_by_feat_sel.py @@ -0,0 +1,212 @@ +"""Compute Shapley fraction captured by each feature selection method. + +Args: + -s, --shap_vals: Path to the file containing the absolute Shapley value + fraction per loci per sample. + -b, --basil-filtered-vars: Path to the filtered_vars.json file used by + the LightGBM model that used BASIL feature selection. + -p, --prsice-filtered-vars: Path to the filtered_vars.json file used by + the LightGBM model that used PRSice feature selection. + -t, --threshold-filtered-vars: Path to the filtered_vars.json file used by + the LightGBM model that used threshold feature selection. + -m, --best-model-config: Output best model config for LightGBM model that + used threshold feature selection. + -v, --variant-info: Path to the CSV file with variant information. Mainly + an 'rsid' column and a 'loci' column (chr:bp). Used to map from + filtered_vars.json to the Shapley value column names. + -o, --output-dir: Path to the directory where the output files will be + written. +""" + +import argparse +import json +import os + +import pandas as pd +import numpy as np + + +s='../output/shap_per_loci/mult_pheno_1/comb_shap_vals.csv' +b='../output/aml_filter_vars_basil/mult_pheno_1/filtered_vars.json' +p='../output/aml_filter_vars_prsice/mult_pheno_1/filtered_vars.json' +t='../output/aml_filter_vars/mult_pheno_1/filtered_vars.json' +m='../output/aml_thresh_vars/mult_pheno_1/best_model_config.json' +v='../data/geno_data/chr19_var_info.csv' + +def parse_args(): + + parser = argparse.ArgumentParser(description="Compute Shapley fraction captured by each feature selection method.") + + parser.add_argument("-s", "--shap-vals", required=True, type=str, + help="Path to the file containing the absolute Shapley value fraction per loci per sample.") + parser.add_argument("-b", "--basil-filtered-vars", required=True, type=str, + help="Path to the filtered_vars.json file used by the LightGBM model that uses BASIL feature selection.") + parser.add_argument("-p", "--prsice-filtered-vars", required=True, type=str, + help="Path to the filtered_vars.json file used by the LightGBM model that uses PRSice feature selection.") + parser.add_argument("-t", "--threshold-filtered-vars", required=True, type=str, + help="Path to the filtered_vars.json file used by the LightGBM model that uses threshold feature selection.") + parser.add_argument("-m", "--best-model-config", required=True, type=str, + help="Output best model config for LightGBM model that uses threshold feature selection.") + parser.add_argument("-v", "--variant-info", required=True, type=str, + help="Path to the CSV file with variant information. Mainly an 'rsid' column and a 'loci' column (chr:bp). Used to map from filtered_vars.json to the Shapley value column names.") + parser.add_argument("-o", "--output-dir", required=True, type=str, + help="Path to the directory where the output files will be written.") + + return parser.parse_args() + + +if __name__ == '__main__': + + args = parse_args() + + # Load Shapley values + shap_vals = pd.read_csv(args.shap_vals, index_col=0) + + # Load variant information + variant_info = pd.read_csv( + args.variant_info, + usecols=["rsid", "locus"], + ) + variant_info['pos'] = variant_info['locus'].apply(lambda x: x.split(':')[1]) + + # Load filtered_vars.json files + with open(args.basil_filtered_vars, "r") as f: + basil_filtered_vars = json.load(f) + with open(args.prsice_filtered_vars, "r") as f: + prsice_filtered_vars = json.load(f) + with open(args.threshold_filtered_vars, "r") as f: + threshold_filtered_vars = json.load(f) + + # Get threshold feature selection threshold + with open(args.best_model_config, "r") as f: + best_thresh_config = json.load(f) + + # Get positions included for each feature selection method + # BASIL + basil_raw = basil_filtered_vars['basil']['included'] + basil_rsid = [] + basil_pos = [] + + for loci in basil_raw: + if 'rs' in loci: + loci = loci.split('_')[0] + basil_rsid.append(loci) + else: + assert ':' in loci + loci = loci.split('_')[0] + basil_pos.append(loci.split(':')[1]) + + basil_pos.extend( + variant_info[variant_info['rsid'].isin(basil_rsid)].pos.values.tolist() + ) + + # PRSice + prsice_raw = prsice_filtered_vars['clump']['lead'] + prsice_rsid = [] + prsice_pos = [] + + for loci in prsice_raw: + if 'rs' in loci: + loci = loci.split('_')[0] + prsice_rsid.append(loci) + else: + assert ':' in loci + loci = loci.split('_')[0] + prsice_pos.append(loci.split(':')[1]) + + prsice_pos.extend( + variant_info[variant_info['rsid'].isin(prsice_rsid)].pos.values.tolist() + ) + + # Threshold + thresh_str = best_thresh_config['best_config']['filter_threshold'] + thresh_pval = thresh_str.split(',')[0].split(':')[1] + thresh_window = thresh_str.split(',')[1].split(':')[1] + + thresh_raw = threshold_filtered_vars[thresh_pval][thresh_window] + thresh_rsid = [] + thresh_pos = [] + + for loci in thresh_raw: + if 'rs' in loci: + loci = loci.split('_')[0] + thresh_rsid.append(loci) + else: + assert ':' in loci + loci = loci.split('_')[0] + thresh_pos.append(loci.split(':')[1]) + + thresh_pos.extend( + variant_info[variant_info['rsid'].isin(thresh_rsid)].pos.values.tolist() + ) + + # Compute feature selection recall, precision, and F1 score + true_pos = shap_vals.columns.tolist() + + basil_tp = len(set(basil_pos).intersection(true_pos)) + basil_fp = len(set(basil_pos).difference(true_pos)) + basil_fn = len(set(true_pos).difference(basil_pos)) + + prsice_tp = len(set(prsice_pos).intersection(true_pos)) + prsice_fp = len(set(prsice_pos).difference(true_pos)) + prsice_fn = len(set(true_pos).difference(prsice_pos)) + + thresh_tp = len(set(thresh_pos).intersection(true_pos)) + thresh_fp = len(set(thresh_pos).difference(true_pos)) + thresh_fn = len(set(true_pos).difference(thresh_pos)) + + scores = { + 'basil': dict(), + 'prsice': dict(), + 'thresholds': dict(), + } + + # Recall + scores['basil']['recall'] = basil_tp / (basil_tp + basil_fn) + scores['prsice']['recall'] = prsice_tp / (prsice_tp + prsice_fn) + scores['thresholds']['recall'] = thresh_tp / (thresh_tp + thresh_fn) + + # Precision + scores['basil']['precision'] = basil_tp / (basil_tp + basil_fp) + scores['prsice']['precision'] = prsice_tp / (prsice_tp + prsice_fp) + scores['thresholds']['precision'] = thresh_tp / (thresh_tp + thresh_fp) + + # F1 score + if scores['basil']['recall'] + scores['basil']['precision'] == 0: + scores['basil']['f1'] = 0 + else: + scores['basil']['f1'] = 2 * ( + (scores['basil']['recall'] * scores['basil']['precision']) / + (scores['basil']['recall'] + scores['basil']['precision']) + ) + + if scores['prsice']['recall'] + scores['prsice']['precision'] == 0: + scores['prsice']['f1'] = 0 + else: + scores['prsice']['f1'] = 2 * ( + (scores['prsice']['recall'] * scores['prsice']['precision']) / + (scores['prsice']['recall'] + scores['prsice']['precision']) + ) + + if scores['thresholds']['recall'] + scores['thresholds']['precision'] == 0: + scores['thresholds']['f1'] = 0 + else: + scores['thresholds']['f1'] = 2 * ( + (scores['thresholds']['recall'] * scores['thresholds']['precision']) / + (scores['thresholds']['recall'] + scores['thresholds']['precision']) + ) + + # Get average fraction of Shapley value captured + scores['basil']['shapley_fraction'] = shap_vals[ + list(set(basil_pos).intersection(true_pos)) + ].sum(axis=1).mean() + scores['prsice']['shapley_fraction'] = shap_vals[ + list(set(prsice_pos).intersection(true_pos)) + ].sum(axis=1).mean() + scores['thresholds']['shapley_fraction'] = shap_vals[ + list(set(thresh_pos).intersection(true_pos)) + ].sum(axis=1).mean() + + # Save scores as JSON + with open(os.path.join(args.output_dir, "feature_selection_scores.json"), "w") as f: + json.dump(scores, f, indent=4) \ No newline at end of file diff --git a/paper/workflows/shap_gen/run_shap.sh b/paper/workflows/shap_gen/run_shap.sh new file mode 100755 index 0000000..e99e2fd --- /dev/null +++ b/paper/workflows/shap_gen/run_shap.sh @@ -0,0 +1,50 @@ +# Get local SHAP values + +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +# PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +PHENO=gepsi_lin_add_hFULL_hFULL + +echo ${PHENO} + +TEMP_DIR=/tmp +OUT_DIR=../output/shap/${PHENO} + +SIM_OUT_DIR=../pheno_sim/citrus_output +SIM_CONFIG=${SIM_OUT_DIR}/${PHENO}_config.json + +SPLITS_DIR=../data/splits +TEST_SPLIT_FNAME=test_samples.txt + +DOCKER_URL=gcr.io/ucsd-medicine-cast/citrus:latest + +# Start docker env +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://${DOCKER_URL} + +singularity exec --containall \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/workflows && \ + export SPARK_DRIVER_MEMORY=100G && \ + export SPARK_EXECUTOR_MEMORY=100G && \ + export SPARK_LOCAL_DIRS=/mnt/tmp && \ + export SPARK_WORKER_DIR=/mnt/tmp && \ + awk -v OFS='\t' '{ print \$1\"_\"\$1 }' ${SPLITS_DIR}/${TEST_SPLIT_FNAME} > \ + ${TEMP_DIR}/${TEST_SPLIT_FNAME} \ + && \ + mkdir -p ${OUT_DIR} && \ + citrus shap \ + -c ${SIM_CONFIG} \ + -i ${TEMP_DIR}/${TEST_SPLIT_FNAME} \ + -s ${OUT_DIR}/shap_vals.csv " diff --git a/paper/workflows/shap_gen/shap.py b/paper/workflows/shap_gen/shap.py new file mode 100644 index 0000000..dc5e54c --- /dev/null +++ b/paper/workflows/shap_gen/shap.py @@ -0,0 +1,37 @@ +"""Get SHAP values with CITRUS. + +Args: + + -c, --config_path: Path to JSON simulation config. + -o, --output_path: Path to output directory. +""" + +import argparse +import json + +from pheno_sim.shap import run_SHAP + + +def parse_args(): + parser = argparse.ArgumentParser() + parser.add_argument( + "-c", "--config_path", type=str, help="Path to JSON simulation config." + ) + parser.add_argument( + "-o", "--output_path", type=str, help="Path to output directory." + ) + return parser.parse_args() + + +if __name__ == "__main__": + args = parse_args() + + # Load config + with open(args.config_path, "r") as f: + config = json.load(f) + + # Run SHAP + run_SHAP( + config, + save_path=args.output_path + ) diff --git a/paper/workflows/sim_pheno/simulate_pheno.sh b/paper/workflows/sim_pheno/simulate_pheno.sh new file mode 100755 index 0000000..86f1c47 --- /dev/null +++ b/paper/workflows/sim_pheno/simulate_pheno.sh @@ -0,0 +1,51 @@ +# Simulate phenotypes using CITRUS + +# PHENO=complex_pheno_1 +# PHENO=complex_pheno_1_noise +# PHENO=complex_pheno_2 +# PHENO=complex_pheno_3 +# PHENO=complex_pheno_4 +# PHENO=mult_pheno_1_more_noise +# PHENO=mult_pheno_1 +# PHENO=mult_pheno_2 +PHENO=xor_pheno_1 +# PHENO=xor_pheno_2 +# PHENO=lin_add_nvar1000_h2_v1 +# PHENO=lin_add_nvar1000_h4_v1 +# PHENO=compound_het +# PHENO=compound_het_2 +# PHENO=gepsi_lin_add_h0625_h0625 +# PHENO=gepsi_lin_add_h125_h125 +# PHENO=gepsi_lin_add_h25_h25 +# PHENO=gepsi_lin_add_h5_h5 +# PHENO=gepsi_lin_add_hFULL_hFULL + +echo ${PHENO} + +OUTPUT_DIR=citrus_output +SIM_CONFIG_FILE=sim_configs/${PHENO}.json + +TEMP_DIR=/tmp +DOCKER_URL=gcr.io/ucsd-medicine-cast/citrus:latest + +# Run citrus simulate in docker +module load singularitypro + +# singularity shell --containall \ +# --bind /expanse/projects/gymreklab/rdevito:/mnt \ +# docker://gcr.io/ucsd-medicine-cast/citrus:latest + +singularity exec --containall --cleanenv --writable-tmpfs \ + --bind /expanse/projects/gymreklab/rdevito:/mnt \ + docker://${DOCKER_URL} \ + bash -c "cd /mnt/citrus/CITRUS/paper/pheno_sim/ && \ + JAVA_HOME=/usr/lib/jvm/java-11-openjdk-amd64 && \ + export SPARK_DRIVER_MEMORY=50G && \ + export SPARK_EXECUTOR_MEMORY=50G && \ + citrus simulate \ + -c ${SIM_CONFIG_FILE} \ + -o ${OUTPUT_DIR} \ + -f ${PHENO}.csv \ + --output_config_filename ${PHENO}_config.json \ + && \ + python3 citrus_out_to_plink_fmt.py ${PHENO} "