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snakefile.benchmark
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#----------------------------------------------------
# Setup
#----------------------------------------------------
configfile: workflow.basedir + "/Config/config.default.yml"
# import libraries
import os
import sys
import pandas as pd
from datetime import datetime
# import custom functions
sys.path.insert(0, Path(workflow.basedir).parent.as_posix())
import Scripts.Functions.functions as func
n_folds = list(range(1, config['benchmark']['n_folds']+1))
now = datetime.now()
dt_string = now.strftime("%Y-%m-%d_%H-%M-%S")
# set defult parameters
func.set_reference_batch_parameters(config)
func.set_downsampling_parameters(config)
func.set_benchmark_directory(config,mode = 'benchmarking')
func.set_ontology_parameters(config,mode = 'benchmarking')
# get consensus methods to run
consensus_run = func.get_consensus_methods(config, mode = 'benchmarking')
# get tools to run
tools_to_run = func.get_tools_to_run(config)
consensus_tools = func.get_consensus_tools(config)
#----------------------------------------------------
# Final rule all
#----------------------------------------------------
def all_function():
return [
f"{output_dir_benchmark}/{reference}/report/{dt_string}.benchmark_report_{ontology}.html"
for reference in config['references'].keys()
for output_dir_benchmark in [config["references"][reference]["output_dir_benchmark"]]
for ontology in config["references"][reference]["ontology"]["ontology_column"]
]
def output_all_function():
return [
f"{output_dir_benchmark}/{reference}/benchmark.finished.succesfully.txt"
for reference in config['references'].keys()
for output_dir_benchmark in [config["references"][reference]["output_dir_benchmark"]]
]
rule benchmark_all:
input:
all_function()
output:
finish_file = output_all_function()
shell:
"""
# Your string
input_string="{output.finish_file}"
# Convert the string into an array of files
IFS=' ' read -r -a file_output <<< "$input_string"
# touch a finish file for each path
for file in "${{file_output[@]}}"; do
touch "$file"
done
"""
#----------------------------------------------------
# Subset Folds
#----------------------------------------------------
rule subset_folds:
input:
reference = lambda wildcards:config['references'][wildcards.reference]['expression']
output:
training_data = expand("{{output_dir_benchmark}}/{{reference}}/fold{folds_index}/train.csv", folds_index = n_folds),
labfile = expand("{{output_dir_benchmark}}/{{reference}}/fold{folds_index}/train_labels.csv", folds_index = n_folds),
test_data = expand("{{output_dir_benchmark}}/{{reference}}/fold{folds_index}/test.csv", folds_index = n_folds)
log:
"{output_dir_benchmark}/{reference}/subset_folds.log"
params:
basedir = {workflow.basedir},
labfile = lambda wildcards:config['references'][wildcards.reference]['labels'],
batch_path = lambda wildcards:config['references'][wildcards.reference]['batch'],
outdir = "{output_dir_benchmark}/{reference}",
reference_name = "{reference}",
n_folds = config['benchmark']['n_folds'],
min_cells_per_cluster = lambda wildcards:config["references"][wildcards.reference]['min_cells_per_cluster'],
downsample_value = lambda wildcards:config["references"][wildcards.reference]['downsample']['value'],
downsample_stratified = lambda wildcards:config["references"][wildcards.reference]['downsample']['stratified'],
ontology_path = lambda wildcards:config["references"][wildcards.reference]["ontology"]["ontology_path"],
ontology_column = lambda wildcards:config["references"][wildcards.reference]["ontology"]["ontology_column"],
random_seed = config['seed']
threads: 1
resources:
shell:
"""
Rscript {params.basedir}/Scripts/benchmark/subset_folds.R \
{input.reference} \
{params.labfile} \
{params.outdir} \
{threads} \
{params.n_folds} \
{params.min_cells_per_cluster} \
{params.downsample_value} \
{params.downsample_stratified} \
"{params.ontology_path}" \
"{params.ontology_column}" \
{params.batch_path} \
"{params.random_seed}" \
&> {log}
"""
#----------------------------------------------------
# Knit Report
#----------------------------------------------------
rule knit_report:
input:
pred = expand("{{output_dir_benchmark}}/{{reference}}/fold{folds_index}/{tool}/{tool}_pred.csv",
folds_index = n_folds,
tool = tools_to_run),
consensus = expand("{{output_dir_benchmark}}/{{reference}}/fold{folds_index}/Prediction_Summary_{{ontology}}.tsv",
folds_index = n_folds)
output:
report_path = "{output_dir_benchmark}/{reference}/report/" + dt_string + '.benchmark_report_{ontology}.html'
log:
"{output_dir_benchmark}/{reference}/report/report_{ontology}.log"
params:
basedir = {workflow.basedir},
pred_path = "{output_dir_benchmark}/{reference}",
n_folds = config['benchmark']['n_folds'],
tools = tools_to_run,
min_agree = config['consensus']['type']['majority']['min_agree'],
ref_name = "{reference}",
accuracy_metric = "F1",
ontology_path = lambda wildcards:config["references"][wildcards.reference]["ontology"]["ontology_path"],
ontology_columns = "{ontology}",
resources:
shell:
"""
Rscript -e "rmarkdown::render(
'{params.basedir}/Notebooks/benchmark_report.Rmd',
params = list(tools = '{params.tools}',
ref_name = '{params.ref_name}',
pred_path = '{params.pred_path}',
min_agree = '{params.min_agree}',
fold = '{params.n_folds}',
accuracy_metric = '{params.accuracy_metric}',
ontology_path = '{params.ontology_path}',
ontology_columns = '{params.ontology_columns}'),
output_file = '{output.report_path}')" \
&> {log}
"""
#----------------------------------------------------
# Consensus
#----------------------------------------------------
rule consensus:
input:
results = expand("{{output_dir_benchmark}}/{{reference}}/fold{{folds_index}}/{tool}/{tool}_pred.csv",
tool=tools_to_run)
output:
prediction_summary = "{output_dir_benchmark}/{reference}/fold{folds_index}/Prediction_Summary_{ontology}.tsv"
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/Gatherpreds.{ontology}.log"
params:
basedir = {workflow.basedir},
tools = tools_to_run,
consensus_tools = consensus_tools,
consensus_type = 'majority',
sample = "{output_dir_benchmark}/{reference}/fold{folds_index}",
min_agree = config["consensus"]["type"]["majority"]["min_agree"],
ontology_path = lambda wildcards:config["references"][wildcards.reference]["ontology"]["ontology_path"],
ontology_label = "{ontology}"
shell:
"""
Rscript {params.basedir}/Scripts/calculate_consensus.R \
{params.sample} \
{output.prediction_summary} \
"{params.tools}" \
"{params.consensus_tools}" \
"{params.consensus_type}" \
"{params.min_agree}" \
"{params.ontology_label}" \
"{params.ontology_path}" \
&> {log}
"""
#----------------------------------------------------
# scPred
#----------------------------------------------------
rule train_scPred:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/scPred_{classifier}/scPred_{classifier}.Rda"
params:
basedir = {workflow.basedir},
classifier = "{classifier}"
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scPred_{classifier}/scPred_{classifier}.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scPred_{classifier}/scPred_{classifier}_train_benchmark.txt"
threads:
config['scPred']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/scPred/train_scPred.R \
{input.reference} \
{input.labfile} \
{output.model} \
{threads} \
{params.classifier} \
&> {log}
"""
rule predict_scPred:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/scPred_{classifier}/scPred_{classifier}.Rda"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/scPred_{classifier}/scPred_{classifier}_pred.csv"
params:
basedir = {workflow.basedir},
classifier = "{classifier}",
threshold = config['scPred']['threshold']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scPred_{classifier}/scPred_{classifier}.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scPred_{classifier}/scPred_{classifier}_predict_benchmark.txt"
threads:
config['scPred']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/scPred/predict_scPred.R \
{input.query} \
{input.model} \
{output.pred} \
{threads} \
{params.classifier} \
{params.threshold} \
&> {log}
"""
rule gather_scPred:
input:
expand("{{output_dir_benchmark}}/{{reference}}/fold{{folds_index}}/scPred_{classifier}/scPred_{classifier}_pred.csv",
classifier = config['scPred']['classifier'])
#----------------------------------------------------
# SingleR
#----------------------------------------------------
rule train_SingleR:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/SingleR/SingleR_model.Rda"
params:
basedir = {workflow.basedir},
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SingleR/SingleR.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SingleR/SingleR_train_benchmark.txt"
threads:
config['SingleR']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/SingleR/train_SingleR.R \
{input.reference} \
{input.labfile} \
{output.model} \
{threads} \
&> {log}
"""
rule predict_SingleR:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/SingleR/SingleR_model.Rda"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/SingleR/SingleR_pred.csv"
params:
basedir = {workflow.basedir}
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SingleR/SingleR.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SingleR/SingleR_predict_benchmark.txt"
threads:
config['SingleR']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/SingleR/predict_SingleR.R \
{input.query} \
{input.model} \
{output.pred} \
{threads} \
&> {log}
"""
#----------------------------------------------------
# scClassify
#----------------------------------------------------
rule train_scClassify:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/scClassify/scClassify_model.Rda"
params:
basedir = {workflow.basedir},
topN = config['scClassify']['topN'],
weightsCal = config['scClassify']['weightsCal'],
hopach_kmax = config['scClassify']['hopach_kmax']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scClassify/scClassify.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scClassify/scClassify_train_benchmark.txt"
threads:
config['scClassify']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/scClassify/train_scClassify.R \
{input.reference} \
{input.labfile} \
{output.model} \
{threads} \
{params.topN} \
{params.weightsCal} \
{params.hopach_kmax} \
&> {log}
"""
rule predict_scClassify:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/scClassify/scClassify_model.Rda"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/scClassify/scClassify_pred.csv"
params:
basedir = {workflow.basedir},
algorithm = config['scClassify']['algorithm'],
similarity = config['scClassify']['similarity'],
prob_threshold = config['scClassify']['prob_threshold'],
cor_threshold_static = config['scClassify']['cor_threshold_static'],
cor_threshold_high = config['scClassify']['cor_threshold_high']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scClassify/scClassify.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scClassify/scClassify_predict_benchmark.txt"
threads:
config['scClassify']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/scClassify/predict_scClassify.R \
{input.query} \
{input.model} \
{output.pred} \
{threads} \
{params.algorithm} \
{params.similarity} \
{params.prob_threshold} \
{params.cor_threshold_static} \
{params.cor_threshold_high} \
&> {log}
"""
#----------------------------------------------------
# SciBet
#----------------------------------------------------
rule train_SciBet:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/SciBet/SciBet_model.Rda"
params:
basedir = {workflow.basedir},
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SciBet/SciBet.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SciBet/SciBet_train_benchmark.txt"
threads:
config['SciBet']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/SciBet/train_SciBet.R \
{input.reference} \
{input.labfile} \
{output.model} \
{threads} \
&> {log}
"""
rule predict_SciBet:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/SciBet/SciBet_model.Rda"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/SciBet/SciBet_pred.csv"
params:
basedir = {workflow.basedir}
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SciBet/SciBet.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SciBet/SciBet_predict_benchmark.txt"
threads:
config['SciBet']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/SciBet/predict_SciBet.R \
{input.query} \
{input.model} \
{output.pred} \
{threads} \
&> {log}
"""
#----------------------------------------------------
# SVM Linear
#----------------------------------------------------
rule train_SVMlinear:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/SVMlinear/SVMlinear_model.Rda"
params:
basedir = {workflow.basedir}
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SVMlinear/SVMlinear.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SVMlinear/SVMlinear_train_benchmark.txt"
threads:
config['SVMlinear']['threads']
resources:
shell:
"""
python {params.basedir}/Scripts/SVC/train_linearSVM.py \
{input.reference} \
{input.labfile} \
{output.model} \
{threads} \
&> {log}
"""
rule predict_SVMlinear:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/SVMlinear/SVMlinear_model.Rda"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/SVMlinear/SVMlinear_pred.csv"
params:
basedir = {workflow.basedir},
threshold = config['SVMlinear']['threshold'],
tool_name = config['SVMlinear']['classifier']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SVMlinear/SVMlinear.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SVMlinear/SVMlinear_predict_benchmark.txt"
threads:
config['SVMlinear']['threads']
resources:
shell:
"""
python {params.basedir}/Scripts/SVC/predict_SVM.py \
{input.query} \
{input.model} \
{output.pred} \
{params.threshold} \
{threads} \
{params.tool_name} \
&> {log}
"""
#----------------------------------------------------
# SVC
#----------------------------------------------------
rule train_SVC:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/SVC/SVC_model.pkl"
params:
basedir = {workflow.basedir}
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SVC/SVC.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SVC/SVC_train_benchmark.txt"
threads:
config['SVC']['threads']
resources:
shell:
"""
python {params.basedir}/Scripts/SVC/train_SVM.py \
{input.reference} \
{input.labfile} \
{output.model} \
{params.classifier} \
{threads} \
&> {log}
"""
rule predict_SVC:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/SVC/SVC_model.pkl"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/SVC/SVC_pred.csv"
params:
basedir = {workflow.basedir},
threshold = config['SVC']['threshold'],
tool_name = config['SVC']['classifier']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SVC/SVC.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/SVC/SVC_predict_benchmark.txt"
threads:
config['SVC']['threads']
resources:
shell:
"""
python {params.basedir}/Scripts/SVC/predict_SVM.py \
{input.query} \
{input.model} \
{output.pred} \
{params.threshold} \
{threads} \
{params.tool_name} \
&> {log}
"""
#----------------------------------------------------
# singleCellNet
#----------------------------------------------------
rule train_singleCellNet:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/singleCellNet/singleCellNet_model.Rda"
params:
basedir = {workflow.basedir},
nTrees = config['singleCellNet']['nTrees']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/singleCellNet/singleCellNet.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/singleCellNet/singleCellNet_train_benchmark.txt"
threads:
config['singleCellNet']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/singleCellNet/train_singleCellNet.R \
{input.reference} \
{input.labfile} \
{output.model} \
{threads} \
{params.nTrees} \
&> {log}
"""
rule predict_singleCellNet:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/singleCellNet/singleCellNet_model.Rda"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/singleCellNet/singleCellNet_pred.csv"
params:
basedir = {workflow.basedir},
threshold = config['singleCellNet']['threshold']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/singleCellNet/singleCellNet.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/singleCellNet/singleCellNet_predict_benchmark.txt"
threads:
config['singleCellNet']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/singleCellNet/predict_singleCellNet.R \
{input.query} \
{input.model} \
{output.pred} \
{threads} \
{params.threshold} \
&> {log}
"""
#----------------------------------------------------
# Correlation
#----------------------------------------------------
rule train_Correlation:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/Correlation/Correlation_model.Rda"
params:
basedir = {workflow.basedir},
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/Correlation/Correlation.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/Correlation/Correlation_train_benchmark.txt"
threads:
config['Correlation']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/Correlation/train_Correlation.R \
{input.reference} \
{input.labfile} \
{output.model} \
{threads} \
&> {log}
"""
rule predict_Correlation:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/Correlation/Correlation_model.Rda"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/Correlation/Correlation_pred.csv"
params:
basedir = {workflow.basedir}
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/Correlation/Correlation.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/Correlation/Correlation_predict_benchmark.txt"
threads:
config['Correlation']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/Correlation/predict_Correlation.R \
{input.query} \
{input.model} \
{output.pred} \
{threads} \
&> {log}
"""
#----------------------------------------------------
# scLearn
#----------------------------------------------------
rule train_scLearn:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/scLearn/scLearn_model.Rda"
params:
basedir = {workflow.basedir},
bootstrap_times = config['scLearn']['bootstrap_times']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scLearn/scLearn.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scLearn/scLearn_train_benchmark.txt"
threads:
config['scLearn']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/scLearn/train_scLearn.R \
{input.reference} \
{input.labfile} \
{output.model} \
{threads} \
{params.bootstrap_times} \
&> {log}
"""
rule predict_scLearn:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/scLearn/scLearn_model.Rda"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/scLearn/scLearn_pred.csv"
params:
basedir = {workflow.basedir}
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scLearn/scLearn.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scLearn/scLearn_predict_benchmark.txt"
threads:
config['scLearn']['threads']
resources:
shell:
"""
Rscript {params.basedir}/Scripts/scLearn/predict_scLearn.R \
{input.query} \
{input.model} \
{output.pred} \
{threads} \
&> {log}
"""
#----------------------------------------------------
# scHPL
#----------------------------------------------------
rule train_scHPL:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/scHPL/scHPL_model.Rda"
params:
basedir = {workflow.basedir},
classifier = config['scHPL']['classifier'],
dimred = config['scHPL']['dimred']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scHPL/scHPL.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scHPL/scHPL_train_benchmark.txt"
threads:
config['scHPL']['threads']
resources:
shell:
"""
python {params.basedir}/Scripts/scHPL/train_scHPL.py \
{input.reference} \
{input.labfile} \
{output.model} \
{params.classifier} \
{params.dimred} \
&> {log}
"""
rule predict_scHPL:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/scHPL/scHPL_model.Rda"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/scHPL/scHPL_pred.csv"
params:
basedir = {workflow.basedir},
threshold = config['scHPL']['threshold']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scHPL/scHPL.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scHPL/scHPL_predict_benchmark.txt"
threads:
config['scHPL']['threads']
resources:
shell:
"""
python {params.basedir}/Scripts/scHPL/predict_scHPL.py \
{input.query} \
{input.model} \
{output.pred} \
{params.threshold} \
&> {log}
"""
#----------------------------------------------------
# ACTINN
#----------------------------------------------------
rule train_ACTINN:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/ACTINN/ACTINN_model.pkl"
params:
basedir = {workflow.basedir}
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/ACTINN/ACTINN.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/ACTINN/ACTINN_predict_benchmark.txt"
threads:
config['ACTINN']['threads']
shell:
"""
python {params.basedir}/Scripts/ACTINN/train_ACTINN.py \
-trs {input.reference} \
-trl {input.labfile} \
-mp {output.model} \
&> {log}
"""
rule predict_ACTINN:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/ACTINN/ACTINN_model.pkl"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/ACTINN/ACTINN_pred.csv"
params:
basedir = {workflow.basedir}
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/ACTINN/ACTINN.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/ACTINN/ACTINN_predict_benchmark.txt"
threads:
config['ACTINN']['threads']
resources:
shell:
"""
python {params.basedir}/Scripts/ACTINN/predict_ACTINN.py \
-ts {input.query} \
-mp {input.model} \
-pp {output.pred} \
&> {log}
"""
#----------------------------------------------------
# scID
#----------------------------------------------------
rule run_scID:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv",
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/scID/scID_pred.csv"
params:
basedir = {workflow.basedir},
estimate_weights_from_target = config['scID']['estimate_weights_from_target'],
logFC = config['scID']['logFC']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scID/scID.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scID/scID_benchmark.txt"
threads:
config['scID']['threads']
shell:
"""
Rscript {params.basedir}/Scripts/scID/run_scID.R \
{input.reference} \
{input.labfile} \
{input.query} \
{output.pred} \
{threads} \
{params.estimate_weights_from_target} \
{params.logFC} \
&> {log}
"""
#----------------------------------------------------
# scAnnotate
#----------------------------------------------------
rule run_scAnnotate:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv",
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/scAnnotate/scAnnotate_pred.csv"
params:
basedir = {workflow.basedir},
threshold = 0.5
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scAnnotate/scAnnotate.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scAnnotate/scAnnotate_benchmark.txt"
threads:
config['scAnnotate']['threads']
shell:
"""
Rscript {params.basedir}/Scripts/scAnnotate/run_scAnnotate.R \
{input.reference} \
{input.labfile} \
{input.query} \
{output.pred} \
{threads} \
{params.threshold} \
&> {log}
"""
#----------------------------------------------------
# scNym
#----------------------------------------------------
rule run_scNym:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv",
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/scNym/scNym_pred.csv"
params:
basedir = {workflow.basedir},
threshold = 0.5
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scNym/scNym.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/scNym/scNym_benchmark.txt"
threads:
config['scNym']['threads']
shell:
"""
python {params.basedir}/Scripts/scNym/run_scNym.py \
{input.reference} \
{input.labfile} \
{input.query} \
{output.pred} \
{params.threshold} \
&> {log}
"""
#----------------------------------------------------
# CellTypist
#----------------------------------------------------
rule train_CellTypist:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",
labfile = "{output_dir_benchmark}/{reference}/fold{folds_index}/train_labels.csv"
output:
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/CellTypist/CellTypist_model.pkl"
params:
basedir = {workflow.basedir},
feature_selection = config['CellTypist']['feature_selection']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/CellTypist/CellTypist.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/CellTypist/CellTypist_train_benchmark.txt"
threads:
config['CellTypist']['threads']
resources:
shell:
"""
python {params.basedir}/Scripts/CellTypist/train_CellTypist.py \
{input.reference} \
{input.labfile} \
{output.model} \
{threads} \
{params.feature_selection} \
&> {log}
"""
rule predict_CellTypist:
input:
query = "{output_dir_benchmark}/{reference}/fold{folds_index}/test.csv",
model = "{output_dir_benchmark}/{reference}/fold{folds_index}/CellTypist/CellTypist_model.pkl"
output:
pred = "{output_dir_benchmark}/{reference}/fold{folds_index}/CellTypist/CellTypist_pred.csv"
params:
basedir = {workflow.basedir},
threshold = config['CellTypist']['threshold'],
majority_voting = config['CellTypist']['majority_voting']
log:
"{output_dir_benchmark}/{reference}/fold{folds_index}/CellTypist/CellTypist.log"
benchmark:
"{output_dir_benchmark}/{reference}/fold{folds_index}/CellTypist/CellTypist_predict_benchmark.txt"
threads:
config['CellTypist']['threads']
resources:
shell:
"""
export CELLTYPIST_FOLDER='/root/.celltypist' && \
python {params.basedir}/Scripts/CellTypist/predict_CellTypist.py \
{input.query} \
{input.model} \
{output.pred} \
{params.threshold} \
{threads} \
{params.majority_voting} \
&> {log}
"""
#----------------------------------------------------
# Seurat
#----------------------------------------------------
rule train_Seurat:
input:
reference = "{output_dir_benchmark}/{reference}/fold{folds_index}/train.csv",