From fc8afb67b86208263ed1c94a3df7b7e72f6e39e5 Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Sun, 13 Dec 2020 12:35:43 +0100 Subject: [PATCH 01/26] deleted dockerfile-dev and travis testing --- .github/python-publish.yml | 31 ++++++++++++++++++++++++ .travis.yml | 39 ------------------------------ .vscode/settings.json | 6 ----- Dockerfile-dev | 49 -------------------------------------- hooks/build | 41 ------------------------------- setup.py | 2 +- 6 files changed, 32 insertions(+), 136 deletions(-) create mode 100644 .github/python-publish.yml delete mode 100644 .travis.yml delete mode 100644 .vscode/settings.json delete mode 100644 Dockerfile-dev delete mode 100755 hooks/build diff --git a/.github/python-publish.yml b/.github/python-publish.yml new file mode 100644 index 0000000..4e1ef42 --- /dev/null +++ b/.github/python-publish.yml @@ -0,0 +1,31 @@ +# This workflows will upload a Python Package using Twine when a release is created +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries + +name: Upload Python Package + +on: + release: + types: [created] + +jobs: + deploy: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.x' + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine + - name: Build and publish + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: | + python setup.py sdist bdist_wheel + twine upload dist/* diff --git a/.travis.yml b/.travis.yml deleted file mode 100644 index 9d3ee2f..0000000 --- a/.travis.yml +++ /dev/null @@ -1,39 +0,0 @@ -# Don't use language: python; this gives us an unnecessary virtualenv -language: c - - -env: - - BUILD_TARGET=3.6 - -install: - - wget https://repo.continuum.io/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda.sh - - bash miniconda.sh -b -p $HOME/miniconda - - source "$HOME/miniconda/etc/profile.d/conda.sh" - - hash -r - - conda config --set always_yes yes --set changeps1 no - - conda update -q conda - - conda info -a - - export LANG=en_US.UTF-8 - - export COVERAGE_DIR=":$HOME/htmlcov" - - printenv | sort - # Install conda-build and conda-verify - - conda install -q -c defaults -c conda-forge python=$BUILD_TARGET conda-build conda-verify codecov flake8 - -script: - - source "$HOME/miniconda/etc/profile.d/conda.sh" - - > - conda build \ - --override-channels -c defaults -c conda-forge -c r -c bioconda \ - --python $BUILD_TARGET \ - ./conda.recipe/ - - > - conda create \ - -n install-environment \ - --override-channels -c defaults -c conda-forge -c r -c local -c bioconda \ - python=$BUILD_TARGET \ - scaden conda codecov coverage - - conda activate install-environment && scaden --help - -after_success: - - codecov - diff --git a/.vscode/settings.json b/.vscode/settings.json deleted file mode 100644 index 338af49..0000000 --- a/.vscode/settings.json +++ /dev/null @@ -1,6 +0,0 @@ -{ - "python.pythonPath": "/home/kevin/anaconda3/envs/scaden/bin/python", - "python.linting.pylintEnabled": false, - "python.linting.enabled": true, - "python.linting.flake8Enabled": true -} \ No newline at end of file diff --git a/Dockerfile-dev b/Dockerfile-dev deleted file mode 100644 index 8ed6b79..0000000 --- a/Dockerfile-dev +++ /dev/null @@ -1,49 +0,0 @@ -# BASE_IMAGE is either "nvidia/cuda:10.1-runtime-ubuntu18.04" or "library/ubuntu:18.04" -ARG BASE_IMAGE -FROM ${BASE_IMAGE} - -# -# Install Miniconda in /opt/conda -# - -ENV LANG=C.UTF-8 LC_ALL=C.UTF-8 -ENV PATH /opt/conda/bin:$PATH - -RUN apt-get update --fix-missing && \ - apt-get install -y wget bzip2 ca-certificates curl git && \ - apt-get clean && \ - rm -rf /var/lib/apt/lists/* - -RUN wget --quiet https://repo.anaconda.com/miniconda/Miniconda3-4.7.12.1-Linux-x86_64.sh -O ~/miniconda.sh && \ - /bin/bash ~/miniconda.sh -b -p /opt/conda && \ - rm ~/miniconda.sh && \ - /opt/conda/bin/conda clean -tipsy && \ - ln -s /opt/conda/etc/profile.d/conda.sh /etc/profile.d/conda.sh && \ - echo ". /opt/conda/etc/profile.d/conda.sh" >> ~/.bashrc && \ - echo "conda activate base" >> ~/.bashrc - -ENV LD_LIBRARY_PATH /usr/local/cuda-10.1/lib64:/usr/local/cuda-10.1/extras/CUPTI/lib64:$LD_LIBRARY_PATH - -COPY . /src -WORKDIR /src - -ARG CPU_OR_GPU - -RUN conda update -n base -c defaults conda \ - && conda install conda-build conda-verify \ - && conda build --override-channels -c defaults -c conda-forge -c bioconda conda.recipe/ \ - && cd / \ - && rm -rf /src \ - && conda install --override-channels -c local -c defaults -c conda-forge -c bioconda scaden \ - && conda remove conda-build conda-verify \ - && conda clean -a - -# Needed for when the docker container uses a non-root user id -RUN mkdir /tmp/numba_cache & chmod 777 /tmp/numba_cache & NUMBA_CACHE_DIR=/tmp/numba_cache - -RUN if [ "x$CPU_OR_GPU" = "xgpu" ]; then \ - conda install tensorflow-gpu && \ - conda clean -a; \ - fi - -WORKDIR / diff --git a/hooks/build b/hooks/build deleted file mode 100755 index c9e6800..0000000 --- a/hooks/build +++ /dev/null @@ -1,41 +0,0 @@ -#!/bin/bash - - -# DOCKER_TAG and IMAGE_NAME are set by the docker hub autobuild system -# https://docs.docker.com/docker-hub/builds/advanced/ - -# Determine if building GPU or CPU image: -## First from DOCKER_TAG: -if [[ "${DOCKER_TAG}" == *gpu ]]; then - CPU_OR_GPU="gpu" -fi - -if [[ "${DOCKER_TAG}" == *cpu ]]; then - CPU_OR_GPU="cpu" -fi - -## If it is a local build, build cpu -if [ "x${CPU_OR_GPU}" = "x" ]; then - CPU_OR_GPU="cpu" -fi - -# If it is a local build, give an image name: -if [ "x${IMAGE_NAME}" = "x" ]; then - IMAGE_NAME="scaden-${CPU_OR_GPU}:dev" -fi - -# Choose base image for cpu or gpu: -if [ "${CPU_OR_GPU}" = "gpu" ]; then - BASE_IMAGE="nvidia/cuda:10.1-runtime-ubuntu18.04" -else - BASE_IMAGE="ubuntu:18.04" -fi - -# Build: -echo "Building ${IMAGE_NAME}" -docker build \ - -t "${IMAGE_NAME}" \ - --build-arg BASE_IMAGE="${BASE_IMAGE}" \ - --build-arg CPU_OR_GPU="${CPU_OR_GPU}" \ - -f Dockerfile-dev \ - . diff --git a/setup.py b/setup.py index 03cd660..b2a448a 100644 --- a/setup.py +++ b/setup.py @@ -2,7 +2,7 @@ from setuptools import setup, find_packages -version = '0.9.5' +version = '0.9.6' with open("README.md", "r", encoding="UTF-8") as fh: From 1d7a92706f3c2c48cc6cfb234902c284170b2a7b Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Sun, 13 Dec 2020 17:30:22 +0100 Subject: [PATCH 02/26] remove environment and docker file as on biocontainers --- Dockerfile | 5 ----- docs/index.md | 16 ++++++---------- docs/installation.md | 8 ++++---- environment.yml | 10 ---------- 4 files changed, 10 insertions(+), 29 deletions(-) delete mode 100644 Dockerfile delete mode 100644 environment.yml diff --git a/Dockerfile b/Dockerfile deleted file mode 100644 index 212a600..0000000 --- a/Dockerfile +++ /dev/null @@ -1,5 +0,0 @@ -FROM continuumio/miniconda3 - -COPY environment.yml / -RUN conda env create -f /environment.yml && conda clean -a -ENV PATH /opt/conda/envs/scaden/bin:$PATH \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index be88f3c..0ecdf5a 100644 --- a/docs/index.md +++ b/docs/index.md @@ -10,32 +10,28 @@ A pre-print describing the method is available on Biorxiv: [Deep-learning-based cell composition analysis from tissue expression profiles](https://www.biorxiv.org/content/10.1101/659227v1) - - - ## Changelog -### Version 0.9.5 +#### Version 0.9.5 + added `scaden simulate` command to perform bulk simulation and training file creation + added `--seed` parameter to allow reproducible Scaden runs -### Version 0.9.4 +#### Version 0.9.4 + fixed dependencies (added python>=3.6 requirement) -### Version 0.9.3 +#### Version 0.9.3 + upgrade to Tensorflow 2 + cleaned up dependencies -### Version 0.9.2 +#### Version 0.9.2 + RAM usage improvement -### Version 0.9.1 +#### Version 0.9.1 + Added automatic removal of duplicate genes in Mixture file + Changed name of final prediction file + Added Scaden logo to main script - -### Version 0.9.0 +#### Version 0.9.0 This is the initial release version of Scaden. While this version contains full functionality for pre-processing, training and prediction, it does not contain thorough error messages, plotting functionality and a solid helper function for generation training data. These are all features planned for the release of v.1.0.0. diff --git a/docs/installation.md b/docs/installation.md index c656db0..35f9bd3 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -18,10 +18,10 @@ If you don't want to use conda, you can also install Scaden using pip: ## Docker -If you don't want to install Scaden at all, but rather use a Docker container, we provide that as well. -You can pull the [Scaden docker container](https://hub.docker.com/r/kevinmenden/scaden) with the following command (from Dockerhub): - -`docker pull kevinmenden/scaden` +A docker container with Scaden installed is also available from [biocontainers](https://biocontainers.pro/#/). +You can download containers for all Scaden version here: +[https://biocontainers.pro/#/tools/scaden](https://biocontainers.pro/#/tools/scaden) +If you will also find Singularity images there. ## Webtool (beta) We now also provide a webtool for you: diff --git a/environment.yml b/environment.yml deleted file mode 100644 index 515d843..0000000 --- a/environment.yml +++ /dev/null @@ -1,10 +0,0 @@ -name: scaden -channels: - - bioconda - - r - - defaults - - conda-forge -dependencies: - - scaden=0.9.4=py_0 -prefix: /home/kevin/anaconda3/envs/scaden - From 3d9d2e5ba6a930400f2f9b085e3609737f4a88d1 Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Sun, 13 Dec 2020 19:09:06 +0100 Subject: [PATCH 03/26] starting converting to TF2-keras --- scaden/model/scaden_tf2.py | 308 ++++++++++++++++++++++++++++++++++++ scaden/scaden/prediction.py | 60 +++---- scaden/scaden/training.py | 75 ++++----- 3 files changed, 367 insertions(+), 76 deletions(-) create mode 100644 scaden/model/scaden_tf2.py diff --git a/scaden/model/scaden_tf2.py b/scaden/model/scaden_tf2.py new file mode 100644 index 0000000..60b9eca --- /dev/null +++ b/scaden/model/scaden_tf2.py @@ -0,0 +1,308 @@ +""" +Cell Deconvolutional Network (scaden) class +""" +import os +import logging +import sys +import gc +import tensorflow as tf +import numpy as np +import pandas as pd +from anndata import read_h5ad +import collections +from .functions import dummy_labels, sample_scaling +from tqdm import tqdm + +logger = logging.getLogger(__name__) + +class Scaden(object): + """ + scaden class + """ + + def __init__(self, model_dir, + model_name, batch_size=128, + learning_rate=0.0001, num_steps=1000, + seed=0, hidden_units=[256, 128, 64, 32], + do_rates=[0,0,0,0]): + + self.model_dir=model_dir + self.batch_size=batch_size + self.model_name=model_name + self.beta1=0.9 + self.beta2=0.999 + self.learning_rate=learning_rate + self.data=None + self.n_classes=None + self.labels=None + self.x=None + self.y=None + self.num_steps=num_steps + self.scaling="log_min_max" + self.sig_genes=None + self.sample_names=None + self.hidden_units = hidden_units + self.do_rates = do_rates + + # Set seeds for reproducibility + tf.random.set_seed(seed) + os.environ['TF_DETERMINISTIC_OPS'] = '1' + np.random.seed(seed) + + def scaden_model(self, n_classes): + """Create the Scaden model""" + + model = tf.keras.Sequential() + model.add(tf.keras.layers.Dense(self.hidden_units[0], activation=tf.nn.relu)) + model.add(tf.keras.layers.Dropout(self.do_rates[0])) + model.add(tf.keras.layers.Dense(self.hidden_units[1], activation=tf.nn.relu)) + model.add(tf.keras.layers.Dropout(self.do_rates[1])) + model.add(tf.keras.layers.Dense(self.hidden_units[2], activation=tf.nn.relu)) + model.add(tf.keras.layers.Dropout(self.do_rates[2])) + model.add(tf.keras.layers.Dense(self.hidden_units[3], activation=tf.nn.relu)) + model.add(tf.keras.layers.Dropout(self.do_rates[3])) + model.add(tf.keras.layers.Dense(n_classes, activation=tf.nn.softmax)) + + return model + + def compute_loss(self, logits, targets): + """ + Compute L1 loss + :param logits: + :param targets: + :return: L1 loss + """ + loss = tf.reduce_mean(input_tensor=tf.math.square(logits-targets)) + return loss + + def compute_accuracy(self, logits, targets, pct_cut=0.05): + """ + Compute prediction accuracy + :param targets: + :param pct_cut: + :return: + """ + equality = tf.less_equal(tf.math.abs(tf.math.subtract(logits, targets)), pct_cut) + accuracy = tf.reduce_mean(input_tensor=tf.cast(equality, tf.float32)) + return accuracy + + def correlation_coefficient(self, logits, targets): + """ + Calculate the pearson correlation coefficient + :param logits: + :param targets: + :return: + """ + mx = tf.reduce_mean(input_tensor=logits) + my = tf.reduce_mean(input_tensor=targets) + xm, ym = logits-mx, targets-my + r_num = tf.reduce_sum(input_tensor=tf.multiply(xm, ym)) + r_den = tf.sqrt(tf.multiply(tf.reduce_sum(input_tensor=tf.square(xm)), tf.reduce_sum(input_tensor=tf.square(ym)))) + r = tf.divide(r_num, r_den) + r = tf.maximum(tf.minimum(r, 1.0), -1.0) + return r + + def visualization(self, logits, targets, classes): + """ + Create evaluation metrics + :param targets: + :param classes: + :return: + """ + # add evaluation metrics + rmse = tf.compat.v1.metrics.root_mean_squared_error(logits, targets)[1] + pcor = self.correlation_coefficient(logits, targets) + eval_metrics = {"rmse": rmse, "pcor": pcor} + + for i in range(logits.shape[1]): + eval_metrics["mre_" + str(classes[i])] = tf.compat.v1.metrics.mean_relative_error( + targets[:, i], + logits[:, i], + targets[:, i])[0] + eval_metrics["mae_" + str(classes[i])] = tf.compat.v1.metrics.mean_absolute_error( + targets[:, i], + logits[:, i], + targets[:, i])[0] + eval_metrics["pcor_" + str(classes[i])] = self.correlation_coefficient(targets[:, i],logits[:, i]) + + + eval_metrics["mre_total"] = tf.compat.v1.metrics.mean_relative_error(targets, + logits, + targets)[1] + + eval_metrics["mae_total"] = tf.compat.v1.metrics.mean_relative_error(targets, + logits, + targets)[1] + + eval_metrics["accuracy01"] = self.compute_accuracy(logits, targets, pct_cut=0.01) + eval_metrics["accuracy05"] = self.compute_accuracy(logits, targets, pct_cut=0.05) + eval_metrics["accuracy1"] = self.compute_accuracy(logits, targets, pct_cut=0.1) + + # Create summary scalars + for key, value in eval_metrics.items(): + tf.compat.v1.summary.scalar(key, value) + + tf.compat.v1.summary.scalar('loss', self.loss) + + merged_summary_op = tf.compat.v1.summary.merge_all() + + return merged_summary_op + + def load_h5ad_file(self, input_path, batch_size, datasets=[]): + """ + Load input data from a h5ad file and divide into training and test set + :param input_path: path to h5ad file + :param batch_size: batch size to use for training + :param datasets: a list of datasets to extract from the file + :return: Dataset object + """ + try: + raw_input = read_h5ad(input_path) + except: + logger.error("Could not load training data file! Is it a .h5ad file generated with `scaden process`?") + sys.exit() + + # Subset dataset + if len(datasets) > 0: + all_ds = collections.Counter(raw_input.obs['ds']) + for ds in all_ds: + if ds not in datasets: + raw_input = raw_input[raw_input.obs['ds'] != ds].copy() + + + # Create training dataset + ratios = [raw_input.obs[ctype] for ctype in raw_input.uns['cell_types']] + self.x_data = raw_input.X.astype(np.float32) + self.y_data = np.array(ratios, dtype=np.float32).transpose() + self.data = tf.data.Dataset.from_tensor_slices((self.x_data, self.y_data)) + self.data = self.data.shuffle(1000).repeat().batch(batch_size=batch_size) + self.data_iter = iter(self.data) + + # Extract celltype and feature info + self.labels = raw_input.uns['cell_types'] + self.sig_genes = list(raw_input.var_names) + + def load_prediction_file(self, input_path, sig_genes, labels, scaling=None): + """ + Load a file to perform prediction on it + :param input_path: path to input file + :param sig_genes: the signature genes to use + :param scaling: which scaling to perform + :return: Dataset object + """ + # Load data + data = pd.read_table(input_path, sep="\t", index_col=0) + sample_names = list(data.columns) + + # check for duplicates + data_index = list(data.index) + if not (len(data_index) == len(set(data_index))): + print("Scaden Warning: Your mixture file conatins duplicate genes! The first occuring gene will be used for every duplicate.") + data = data.loc[~data.index.duplicated(keep='first')] + + data = data.loc[sig_genes] + data = data.T + + # Scaling + if scaling: + data = sample_scaling(data, scaling_option=scaling) + + self.data = data + + return sample_names + + def build_model(self, input_path, train_datasets, mode="train"): + """ + Build the model graph + :param reuse: + :return: + """ + self.global_step = tf.Variable(0, name='global_step', trainable=False) + + # Load training data + if mode=="train": + self.load_h5ad_file( + input_path=input_path, + batch_size=self.batch_size, + datasets=train_datasets + ) + + # Load prediction data + if mode=="predict": + self.sample_names = self.load_prediction_file( + input_path=input_path, + sig_genes=self.sig_genes, + labels=self.labels, + scaling=self.scaling + ) + + # Build the model or load if available + self.n_classes = len(self.labels) + + try: + self.model = tf.keras.models.load_model(self.model_dir, compile=False) + logger.info("Loaded pre-trained model") + except: + self.model = self.scaden_model(n_classes=self.n_classes) + + def train(self, input_path, train_datasets): + """ + Train the model + :param num_steps: + :return: + """ + + # Define the optimizer + optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate) + + # Build model graph + self.build_model(input_path=input_path, train_datasets=train_datasets, mode="train") + + # Training loop + pbar = tqdm(range(self.num_steps)) + for step,_ in enumerate(pbar): + + x, y = self.data_iter.get_next() + + with tf.GradientTape() as tape: + self.logits = self.model(x, training = True) + loss = self.compute_loss(self.logits, y) + + grads = tape.gradient(loss, self.model.trainable_weights) + + optimizer.apply_gradients(zip(grads, self.model.trainable_weights)) + + desc = (f"Step: {step}, Loss: {loss:.4f}") + pbar.set_description(desc=desc) + + # Collect garbage after 100 steps - otherwise runs out of memory + if step % 100 == 0: + gc.collect() + + # Save the trained model + self.model.save(self.model_dir) + pd.DataFrame(self.labels).to_csv(os.path.join(self.model_dir, "celltypes.txt"), sep="\t") + pd.DataFrame(self.sig_genes).to_csv(os.path.join(self.model_dir, "genes.txt"), sep="\t") + + + def predict(self, input_path, out_name="scaden_predictions.txt"): + """ + Perform prediction with a pre-trained model + :param out_dir: path to store results in + :param training_data: the dataset used for training + :return: + """ + # Load signature genes and celltype labels + sig_genes = pd.read_table(self.model_dir + "/genes.txt", index_col=0) + self.sig_genes = list(sig_genes['0']) + labels = pd.read_table(self.model_dir + "/celltypes.txt", index_col=0) + self.labels = list(labels['0']) + + # Build model graph + self.build_model(input_path=input_path, train_datasets=[], mode="predict") + + print(self.data) + predictions = self.model.predict(self.data) + + pred_df = pd.DataFrame(predictions[0], columns=self.labels, index=self.sample_names) + return pred_df \ No newline at end of file diff --git a/scaden/scaden/prediction.py b/scaden/scaden/prediction.py index 8dc33e5..d6e0815 100644 --- a/scaden/scaden/prediction.py +++ b/scaden/scaden/prediction.py @@ -12,7 +12,7 @@ import tensorflow as tf from anndata import read_h5ad from scaden.model.architectures import architectures -from scaden.model.scaden import Scaden +from scaden.model.scaden_tf2 import Scaden """ PARAMETERS @@ -41,44 +41,34 @@ def prediction(model_dir, data_path, out_name, seed=0): """ # Small model predictions - tf.compat.v1.reset_default_graph() - with tf.compat.v1.Session() as sess: - cdn256 = Scaden(sess=sess, - model_dir=model_dir + "/m256", - model_name='m256', - seed=seed) - cdn256.hidden_units = M256_HIDDEN_UNITS - cdn256.do_rates = M256_DO_RATES - - # Predict ratios - preds_256 = cdn256.predict(input_path=data_path, out_name='scaden_predictions_m256.txt') - + cdn256 = Scaden( + model_dir=model_dir + "/m256", + model_name='m256', + seed=seed, + hidden_units=M256_HIDDEN_UNITS, + do_rates=M256_DO_RATES) + # Predict ratios + preds_256 = cdn256.predict(input_path=data_path, out_name='scaden_predictions_m256.txt') # Mid model predictions - tf.compat.v1.reset_default_graph() - with tf.compat.v1.Session() as sess: - cdn512 = Scaden(sess=sess, - model_dir=model_dir+"/m512", - model_name='m512', - seed=seed) - cdn512.hidden_units = M512_HIDDEN_UNITS - cdn512.do_rates = M512_DO_RATES - - # Predict ratios - preds_512 = cdn512.predict(input_path=data_path, out_name='scaden_predictions_m512.txt') + cdn512 = Scaden( + model_dir=model_dir+"/m512", + model_name='m512', + seed=seed, + hidden_units=M512_HIDDEN_UNITS, + do_rates=M512_DO_RATES) + # Predict ratios + preds_512 = cdn512.predict(input_path=data_path, out_name='scaden_predictions_m512.txt') # Large model predictions - tf.compat.v1.reset_default_graph() - with tf.compat.v1.Session() as sess: - cdn1024 = Scaden(sess=sess, - model_dir=model_dir+"/m1024", - model_name='m1024', - seed=seed) - cdn1024.hidden_units = M1024_HIDDEN_UNITS - cdn1024.do_rates = M1024_DO_RATES - - # Predict ratios - preds_1024 = cdn1024.predict(input_path=data_path, out_name='scaden_predictions_m1024.txt') + cdn1024 = Scaden( + model_dir=model_dir+"/m1024", + model_name='m1024', + seed=seed, + hidden_units=M1024_HIDDEN_UNITS, + do_rates=M256_DO_RATES) + # Predict ratios + preds_1024 = cdn1024.predict(input_path=data_path, out_name='scaden_predictions_m1024.txt') # Average predictions preds = (preds_256 + preds_512 + preds_1024) / 3 diff --git a/scaden/scaden/training.py b/scaden/scaden/training.py index 3d84157..f59066c 100644 --- a/scaden/scaden/training.py +++ b/scaden/scaden/training.py @@ -12,7 +12,7 @@ import tensorflow as tf from anndata import read_h5ad from scaden.model.architectures import architectures -from scaden.model.scaden import Scaden +from scaden.model.scaden_tf2 import Scaden """ PARAMETERS @@ -46,53 +46,46 @@ def training(data_path, train_datasets, model_dir, batch_size, learning_rate, nu train_datasets = train_datasets.split() print(f"Training on: {train_datasets}") - - # M256 model training + # Training of M256 model print("Training M256 Model ...") - tf.compat.v1.reset_default_graph() - with tf.compat.v1.Session() as sess: - cdn256 = Scaden(sess=sess, - model_dir=model_dir+"/m256", - model_name='m256', - batch_size=batch_size, - learning_rate=learning_rate, - num_steps=num_steps, - seed=seed) - cdn256.hidden_units = M256_HIDDEN_UNITS - cdn256.do_rates = M256_DO_RATES - cdn256.train(input_path=data_path, train_datasets=train_datasets) + cdn256 = Scaden( + model_dir=model_dir+"/m256", + model_name='m256', + batch_size=batch_size, + learning_rate=learning_rate, + num_steps=num_steps, + seed=seed, + hidden_units=M256_HIDDEN_UNITS, + do_rates=M512_DO_RATES) + cdn256.train(input_path=data_path, train_datasets=train_datasets) del cdn256 - # Training of mid model + # Training of M512 model print("Training M512 Model ...") - tf.compat.v1.reset_default_graph() - with tf.compat.v1.Session() as sess: - cdn512 = Scaden(sess=sess, - model_dir=model_dir+"/m512", - model_name='m512', - batch_size=batch_size, - learning_rate=learning_rate, - num_steps=num_steps, - seed=seed) - cdn512.hidden_units = M512_HIDDEN_UNITS - cdn512.do_rates = M512_DO_RATES - cdn512.train(input_path=data_path, train_datasets=train_datasets) + cdn512 = Scaden( + model_dir=model_dir+"/m512", + model_name='m512', + batch_size=batch_size, + learning_rate=learning_rate, + num_steps=num_steps, + seed=seed, + hidden_units=M512_HIDDEN_UNITS, + do_rates=M512_DO_RATES) + cdn512.train(input_path=data_path, train_datasets=train_datasets) del cdn512 - # Training of large model + # Training of M1024 model print("Training M1024 Model ...") - tf.compat.v1.reset_default_graph() - with tf.compat.v1.Session() as sess: - cdn1024 = Scaden(sess=sess, - model_dir=model_dir+"/m1024", - model_name='m1024', - batch_size=batch_size, - learning_rate=learning_rate, - num_steps=num_steps, - seed=seed) - cdn1024.hidden_units = M1024_HIDDEN_UNITS - cdn1024.do_rates = M1024_DO_RATES - cdn1024.train(input_path=data_path, train_datasets=train_datasets) + cdn1024 = Scaden( + model_dir=model_dir+"/m1024", + model_name='m1024', + batch_size=batch_size, + learning_rate=learning_rate, + num_steps=num_steps, + seed=seed, + hidden_units=M1024_HIDDEN_UNITS, + do_rates=M1024_DO_RATES) + cdn1024.train(input_path=data_path, train_datasets=train_datasets) del cdn1024 print("Training finished.") \ No newline at end of file From d95fec9a58e26d2507fa4178131e9c1b0b3a1081 Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Fri, 18 Dec 2020 06:57:58 +0100 Subject: [PATCH 04/26] updated setup, docs --- README.md | 60 ++++++++-------- docs/docs/changelog.md | 31 +++++++++ docs/docs/datasets.md | 27 ++++++++ docs/docs/img/figure1.png | Bin 0 -> 547442 bytes docs/docs/img/scaden_logo.png | Bin 0 -> 16389 bytes docs/docs/index.md | 10 +++ docs/docs/installation.md | 34 +++++++++ docs/docs/usage.md | 127 ++++++++++++++++++++++++++++++++++ setup.py | 53 ++++++-------- 9 files changed, 282 insertions(+), 60 deletions(-) create mode 100644 docs/docs/changelog.md create mode 100644 docs/docs/datasets.md create mode 100644 docs/docs/img/figure1.png create mode 100644 docs/docs/img/scaden_logo.png create mode 100644 docs/docs/index.md create mode 100644 docs/docs/installation.md create mode 100644 docs/docs/usage.md diff --git a/README.md b/README.md index 38151de..63d9cce 100644 --- a/README.md +++ b/README.md @@ -1,7 +1,12 @@ ![Scaden](docs/img/scaden_logo.png) -![MIT](https://anaconda.org/bioconda/scaden/badges/license.svg) -![Install with Bioconda](https://anaconda.org/bioconda/scaden/badges/installer/conda.svg) + +![Scaden version](https://img.shields.io/badge/Scaden-v0.9.6-cyan) +![MIT](https://img.shields.io/badge/License-MIT-black) +![Install with pip](https://img.shields.io/badge/Install%20with-pip-blue) +![Install with Bioconda](https://img.shields.io/badge/Install%20with-conda-green) +![Downloads](https://static.pepy.tech/personalized-badge/scaden?period=total&units=international_system&left_color=blue&right_color=green&left_text=Downloads) + ## Single-cell assisted deconvolutional network Scaden is a deep-learning based algorithm for cell type deconvolution of bulk RNA-seq samples. It was developed @@ -26,44 +31,41 @@ hardware (e.g. GPU), however we recommend to have at least 16 GB of memory. Scaden requires Python 3. All package dependencies should be handled automatically when installing with pip or conda. ### 2. Installation guide +Scaden can be easily installed on a Linux system, and should also work on Mac. +There are currently two options for installing Scaden, either using [Bioconda](https://bioconda.github.io/) or via [pip](https://pypi.org/). + +## pip +To install Scaden via pip, simply run the following command: + +`pip install scaden` + -#### Stable version +## Bioconda +You can also install Scaden via bioconda, using: -The recommended way to install Scaden is using conda and the Bioconda channel. +`conda install -c bioconda scaden` -You can create a conda environment for your scaden installation to minimize package conflicts: +## GPU +If you want to make use of your GPU, you will have to additionally install `tensorflow-gpu`. -```bash -conda create -n scaden_env --override-channels -c defaults -c conda-forge -c bioconda scaden -``` +For pip: -`scaden` depends on `tensorflow`. On conda `tensorflow` uses the CPU to train the networks. - If you have a `cuda` capable GPU, you may want to install the `tensorflow-gpu` conda package as well -so scaden uses the gpu instead: +`pip install tensorflow-gpu` -```bash -conda activate scaden_env -conda install tensorflow-gpu -``` +For conda: -Installation with `conda` takes only a few minutes (2-5), depending on the internet connection. +`conda install tensorflow-gpu` -Alternatively Scaden can be installed with `pip`: +## Docker +If you don't want to install Scaden at all, but rather use a Docker container, we provide that as well. +For every release, we provide two version - one for CPU and one for GPU usage. +To pull the CPU container, use this command: -```bash -# Create a virtual environment: -python3 -m venv ./venv -# Activate the virtual environment with: -source ./venv/bin/activate -# Upgrade pip and wheel: -python3 -m pip install -U pip wheel -# Install scaden: -python3 -m pip install -U scaden -``` +`docker pull ghcr.io/kevinmenden/scaden/scaden` -We also provide a docker image with Scaden installed: -[https://hub.docker.com/r/kevinmenden/scaden](https://hub.docker.com/r/kevinmenden/scaden) +For the GPU container: +`docker pull ghcr.io/kevinmenden/scaden/scaden-gpu` ### Webtool (beta) Additionally, we now proivde a web tool: diff --git a/docs/docs/changelog.md b/docs/docs/changelog.md new file mode 100644 index 0000000..2f0e14a --- /dev/null +++ b/docs/docs/changelog.md @@ -0,0 +1,31 @@ +# Changelog + +### Version 0.9.6 ++ fixed Dockerfile (switched to pip installation) ++ added better error messages to `simulate` command ++ cleaned up dependencies + +### Version 0.9.5 ++ added `scaden simulate` command to perform bulk simulation and training file creation ++ added `--seed` parameter to allow reproducible Scaden runs + +### Version 0.9.4 ++ fixed dependencies (added python>=3.6 requirement) + +### Version 0.9.3 ++ upgrade to Tensorflow 2 ++ cleaned up dependencies + +### Version 0.9.2 ++ RAM usage improvement + +### Version 0.9.1 ++ Added automatic removal of duplicate genes in Mixture file ++ Changed name of final prediction file ++ Added Scaden logo to main script + +### Version 0.9.0 +This is the initial release version of Scaden. While this version contains full functionality for pre-processing, training and prediction, it does not +contain thorough error messages, plotting functionality and a solid helper function for generation training data. These are all features +planned for the release of v.1.0.0. +The core functionality of Scaden is, however, implemented and fully operational. Please check the [Usage](usage) section to learn how to use Scaden. \ No newline at end of file diff --git a/docs/docs/datasets.md b/docs/docs/datasets.md new file mode 100644 index 0000000..c865598 --- /dev/null +++ b/docs/docs/datasets.md @@ -0,0 +1,27 @@ +# Datasets +This section lists all training datasets currently available for Scaden. + + +#### Mouse Brain +* Link: [Mouse brain training data](https://figshare.com/s/2fec97de0665a4414b03) +* Sample size: 30,000 +* No. Datasets: 5 +* Identifier: MGI Symbol + +#### Human PBMC +* Link: [PBMC training data](https://figshare.com/s/e59a03885ec4c4d8153f) +* Sample size: 32,000 +* No. Datasets: 4 +* Identifier: HGNC Symbol + +#### Human Pancreas +* Link: [Pancreas training data](https://figshare.com/s/b3ec41496a5c35a94165) +* Sample size: 12,000 +* No. Datasets: 2 +* Identifier: HGNC Symbol + +#### Human Ascites +* Link: [Ascites training data](https://figshare.com/s/01df6b6975036f942d1f) +* Sample size: 6,000 +* No. Datasets: 1 +* 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z{%64G28-dq_D+7^dKiJTgWtCRSe|43Z!i$B8@9RWyFnwGrQAiXs6;+BW5DQ^o5u<9RdzG# zbh_(nbtMk@&pSkmjT=B|s;3cQb@0RILzit+fhvqMFKD@cTF9F0zw>>e+OXYrItX4{ z<$4#b9XWUR;I0&w+w}I0jH~f`SoBU1Z#>KRS0a0D!1WO)^jJxZ~N+&7b;d z0F8?{#FzSirB~Gk%wM+3x<@-4*ZK7BAHp-aj|&!6*-VwK4b;8_So)6)>2%fqnfcx= z{M*zenUU=8I!UoM6i+Eq+BFs=;(<*n@TGshq8-NU&A5L2yvlLk>9p6xQ9)eIT)pTv z4=WI?3(cAS>*kR>5L1r7PkzJj6cAR9UK8GY6q|oiMK3O(#JT?s^Yg4br$t-$&H2#& zWTS1CsDAguIDsmg6Qeo^dMB6_x|QqCIhT{W-M7HNcI2p$@CXk6{xm7S3FMdr qknr1Cv$d|rkuZqt>}{4`s6A#h?i|XVtzndXIQjP~GSyO_L;fFr2aC)A literal 0 HcmV?d00001 diff --git a/docs/docs/index.md b/docs/docs/index.md new file mode 100644 index 0000000..f1d7083 --- /dev/null +++ b/docs/docs/index.md @@ -0,0 +1,10 @@ +![Scaden](img/scaden_logo.png) + +## Single-cell Assisted Deconvolutional Network + +Scaden is a tool for bulk RNA-seq cell type deconvolutional that uses a deep neural network ensemble trained +on artificial bulk data simulated with scRNA-seq datasets. This method was developed in the [Genome Biology of Neurodegenerativ Diseases group](https://www.dzne.de/en/research/research-areas/fundamental-research/research-groups/heutink/research-areasfocus/) +at the [DZNE Tübingen](https://www.dzne.de/en/about-us/sites/tuebingen/) and the [Medical Systems Biology](https://www.uke.de/english/departments-institutes/institutes/medical-systems-biology/team/index.html) group at the [ZMNH](https://www.uke.de/english/departments-institutes/centers/center-for-molecular-neurobiology-hamburg-(zmnh)/index.html). The main author is [Kevin Menden](https://github.com/KevinMenden). + +A paper describing Scaden has been published in Science Advances: +[Deep-learning based cell composition analysis from tissue expression profiles](https://advances.sciencemag.org/content/6/30/eaba2619) diff --git a/docs/docs/installation.md b/docs/docs/installation.md new file mode 100644 index 0000000..e7f6c21 --- /dev/null +++ b/docs/docs/installation.md @@ -0,0 +1,34 @@ +# Installation +Scaden be easily installed on a Linux system, and should also work on Mac. +There are currently two options for installing Scaden, either using [Bioconda](https://bioconda.github.io/) or via [pip](https://pypi.org/). + + +## pip +To install Scaden via pip, simply run the following command: + +`pip install scaden` + + +## Bioconda +You can also install Scaden via bioconda, using:: + +`conda install -c bioconda scaden` + + +## Docker +If you don't want to install Scaden at all, but rather use a Docker container, we provide that as well. +For every release, we provide two version - one for CPU and one for GPU usage. +To pull the CPU container, use this command: + +`docker pull ghcr.io/kevinmenden/scaden/scaden` + +For the GPU container: + +`docker pull ghcr.io/kevinmenden/scaden/scaden-gpu` + +## Webtool (beta) +We now also provide a webtool for you: + +[https://scaden.ims.bio](https://scaden.ims.bio) + +It comes with pre-generated training datasets for several tissues. You only need to upload the epxression data for prediction. Please not that this is still in beta. diff --git a/docs/docs/usage.md b/docs/docs/usage.md new file mode 100644 index 0000000..a64bfda --- /dev/null +++ b/docs/docs/usage.md @@ -0,0 +1,127 @@ +# Usage + +For a typical deconvolution with Scaden you will have to perform three steps: + +* pre-processing of training data +* training of Scaden model +* prediction + +This assumes that you already have a training dataset. If not, Scaden contains functionality to create a dataset from one or several scRNA-seq datasets. +Please refer to the [data generation](#training-data-generation) section for instructions on how to create training datasets. + +Note that we already provide datasets for certain tissues. All available datasets are listed in the [Datasets](datasets) section. We will +update this section when new datasets are added. + +## Webtool (beta) +If you don't want to install Scaden on your local machine, we now provide a webtool for you: + +[https://scaden.ims.bio](https://scaden.ims.bio) + +It contains pre-generated training datasets for several tissues, and all you need to do is to upload your expression data. Please note that this is still in preview. + +## Pre-processing +The first step is to pre-process your training data. For this you need your training data and the dataset you want to perform deconvolution on. +In this step, Scaden will create a new file for training which only contains the intersection of genes between the training and the prediction data. +Furthermore, the training data will be log2-transformed and scaled to the range [0,1]. Use the following command for pre-processing: + +```console +scaden process +``` + +## Training +Now that your data is set-up, you can start training a Scaden ensemble model. Scaden consists of three deep neural network models. By default, +each of them will be trained for 20,000 steps. You can train longer if you want, although we got good results with this number for datasets of +around 30,000 samples. Use the following command to just train a model for 20,000 steps: + + +```console +scaden train +``` + +This will save the model parameters in your working directory. If you want to create a specific directory for your trained models instead, +and train for 30,00 steps, you can use this command: + + +```console +scaden train --model_dir --steps 30000 +``` + + +You can also adjust the batch size and the learning rate, although we recommend using the default values. If you want to adjust them anyway, use these flages: + + +```console +--batch_size + +--learning_rate +``` + +## Prediction +Finally, after your model is trained, you can start the prediction. If you haven't specified any model directory and just trained a model +in your current directory, you can use the following command to perform the deconvolution: + +```console +scaden predict +``` + +Scaden will then generate a file called 'cdn_predictions.txt' (this name will change in future releases) in your current directory. If the models were saved elsewhere, +you have to tell Scaden where to look for them: + +```console +scaden predict --model_dir +``` + + +You can also change the path and name of the output predictions file using the `outname` flag: + +```console +--outname _.txt for the count data + +_celltypes.txt for the cell type labels + +Here, the `pattern` should be same among all the different datasets (unless you only want to use a specific dataset). + +As example, you can generate 1000 artificial bulk samples from 100 cells per samples with the following command: +```console +scaden simulate --cells 100 --n_samples 1000 --data --pattern +``` + +An example for a pattern would be `*_counts.txt`. This pattern would find the following dataset: +* `dataset_counts.txt` +* `dataset_celltypes.txt` + +Make sure to include an `*` in your pattern! + +This command will create the artificial samples in the current working directory. You can also specificy an output directory using the `--out` parameter. Scaden will also directly create a .h5ad file in this directory, which is the file you will need for training. By default, this file will be called `data.h5ad`, however you can change the prefix using the `--prefix` flag. + + +If you get any errors with the above process, pleas don't hesitate to open an issue on GitHub. \ No newline at end of file diff --git a/setup.py b/setup.py index b2a448a..9ab0c45 100644 --- a/setup.py +++ b/setup.py @@ -2,8 +2,7 @@ from setuptools import setup, find_packages -version = '0.9.6' - +version = '1.0.0' with open("README.md", "r", encoding="UTF-8") as fh: long_description = fh.read() @@ -11,32 +10,24 @@ with open('LICENSE', encoding="UTF-8") as f: license = f.read() -setup( - name='scaden', - version=version, - description="Cell type deconvolution using single cell data", - long_description=long_description, - long_description_content_type="text/markdown", - keywords=['bioinformatics', 'deep learning', 'machine learning', 'single cell sequencing', 'deconvolution'], - author='Kevin Menden', - author_email='kevin.menden@t-online.de', - url='https://github.com/KevinMenden/scaden', - license="MIT License", - entry_points={"console_scripts": ["scaden=scaden.__main__:main"]}, - packages=find_packages(), - include_package_data=True, - python_requires='>3.6.0', - install_requires = [ - 'pandas', - 'numpy', - 'scikit-learn', - 'scipy', - 'tensorflow>=2.0', - 'anndata', - 'tqdm', - 'click' - ], - extras_require = { - 'scanpy': ["scanpy", "matplotlib", "seaborn"] - } -) +setup(name='scaden', + version=version, + description="Cell type deconvolution using single cell data", + long_description=long_description, + long_description_content_type="text/markdown", + keywords=[ + 'bioinformatics', 'deep learning', 'machine learning', + 'single cell sequencing', 'deconvolution' + ], + author='Kevin Menden', + author_email='kevin.menden@t-online.de', + url='https://github.com/KevinMenden/scaden', + license="MIT License", + entry_points={"console_scripts": ["scaden=scaden.__main__:main"]}, + packages=find_packages(), + include_package_data=True, + python_requires='>3.6.0', + install_requires=[ + 'pandas', 'numpy', 'scikit-learn', 'tensorflow>=2.0', 'anndata', + 'tqdm', 'click', 'h5py~=2.10.0' + ]) From 08523f5c5d726b58bcca59f7588b03a2ea22fe45 Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Fri, 18 Dec 2020 06:58:52 +0100 Subject: [PATCH 05/26] updated dockerfile and actions --- .github/workflows/docker-publish.yml | 117 +++++++++++++++++++++++++++ .github/workflows/python-publish.yml | 31 +++++++ Dockerfile | 13 +++ 3 files changed, 161 insertions(+) create mode 100644 .github/workflows/docker-publish.yml create mode 100644 .github/workflows/python-publish.yml create mode 100644 Dockerfile diff --git a/.github/workflows/docker-publish.yml b/.github/workflows/docker-publish.yml new file mode 100644 index 0000000..5e3eae2 --- /dev/null +++ b/.github/workflows/docker-publish.yml @@ -0,0 +1,117 @@ +name: Docker + +on: + push: + # Publish `master` as Docker `latest` image. + branches: + - master + + # Publish `v1.2.3` tags as releases. + tags: + - v* + + # Run tests for any PRs. + pull_request: + +env: + IMAGE_NAME: scaden + CPU_BASE: ubuntu + GPU_IMAGE_NAME: scaden-gpu + GPU_BASE: nvidia/cuda:11.1-runtime-ubuntu20.04 + +jobs: + # Run tests. + # See also https://docs.docker.com/docker-hub/builds/automated-testing/ + test: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + + - name: Run tests + run: | + if [ -f docker-compose.test.yml ]; then + docker-compose --file docker-compose.test.yml build + docker-compose --file docker-compose.test.yml run sut + else + docker build . --build-arg BASE_IMAGE=$CPU_BASE --build-arg GPU=CPU --file Dockerfile + fi + + # Push image to ghcr.io + # See also https://docs.docker.com/docker-hub/builds/ + push: + # Ensure test job passes before pushing image. + needs: test + + runs-on: ubuntu-latest + if: github.event_name == 'push' + + steps: + - uses: actions/checkout@v2 + + - name: Build image + run: docker build . --build-arg BASE_IMAGE=$CPU_BASE --build-arg GPU=CPU --file Dockerfile --tag $IMAGE_NAME + + - name: Log into registry + run: echo "${{ secrets.CR_PAT }}" | docker login ghcr.io -u ${{ github.actor }} --password-stdin + + - name: Push image + run: | + IMAGE_ID=ghcr.io/${{ github.repository }}/$IMAGE_NAME + + # Change all uppercase to lowercase + IMAGE_ID=$(echo $IMAGE_ID | tr '[A-Z]' '[a-z]') + + # Strip git ref prefix from version + VERSION=$(echo "${{ github.ref }}" | sed -e 's,.*/\(.*\),\1,') + + # Strip "v" prefix from tag name + [[ "${{ github.ref }}" == "refs/tags/"* ]] && VERSION=$(echo $VERSION | sed -e 's/^v//') + + # Use Docker `latest` tag convention + [ "$VERSION" == "master" ] && VERSION=latest + + echo IMAGE_ID=$IMAGE_ID + echo VERSION=$VERSION + + docker tag $IMAGE_NAME $IMAGE_ID:$VERSION + docker push $IMAGE_ID:$VERSION + + # Push GPU image to ghcr.io + push_gpu: + # Ensure test job passes before pushing image. + needs: test + + runs-on: ubuntu-latest + if: github.event_name == 'push' + + steps: + - uses: actions/checkout@v2 + + - name: Build image + run: docker build . --build-arg BASE_IMAGE=$GPU_BASE --build-arg GPU=GPU --file Dockerfile --tag $GPU_IMAGE_NAME + + - name: Log into registry + run: echo "${{ secrets.CR_PAT }}" | docker login ghcr.io -u ${{ github.actor }} --password-stdin + + - name: Push image + run: | + IMAGE_ID=ghcr.io/${{ github.repository }}/$GPU_IMAGE_NAME + + # Change all uppercase to lowercase + IMAGE_ID=$(echo $IMAGE_ID | tr '[A-Z]' '[a-z]') + + # Strip git ref prefix from version + VERSION=$(echo "${{ github.ref }}" | sed -e 's,.*/\(.*\),\1,') + + # Strip "v" prefix from tag name + [[ "${{ github.ref }}" == "refs/tags/"* ]] && VERSION=$(echo $VERSION | sed -e 's/^v//') + + # Use Docker `latest` tag convention + [ "$VERSION" == "master" ] && VERSION=latest + + echo IMAGE_ID=$IMAGE_ID + echo VERSION=$VERSION + + docker tag $GPU_IMAGE_NAME $IMAGE_ID:$VERSION + docker push $IMAGE_ID:$VERSION diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml new file mode 100644 index 0000000..4e1ef42 --- /dev/null +++ b/.github/workflows/python-publish.yml @@ -0,0 +1,31 @@ +# This workflows will upload a Python Package using Twine when a release is created +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries + +name: Upload Python Package + +on: + release: + types: [created] + +jobs: + deploy: + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: '3.x' + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine + - name: Build and publish + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: | + python setup.py sdist bdist_wheel + twine upload dist/* diff --git a/Dockerfile b/Dockerfile new file mode 100644 index 0000000..b8d99a3 --- /dev/null +++ b/Dockerfile @@ -0,0 +1,13 @@ +ARG BASE_IMAGE +FROM ${BASE_IMAGE} + +RUN apt-get update && apt-get upgrade -y +RUN apt-get install python3 -y +RUN apt-get install python3-pip -y +RUN pip3 install scaden + +# Install tensorflow-gpu if GPU container +ARG GPU +RUN if [ "$GPU" = "GPU" ]; then \ + pip3 install tensorflow-gpu; \ + fi \ No newline at end of file From fb3449f1101b0c6429b7a3c992acfe8dd6ab2105 Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Fri, 18 Dec 2020 07:00:53 +0100 Subject: [PATCH 06/26] updated bulk simulation --- scaden/preprocessing/bulk_simulation.py | 25 +++++++++++++++++------- scaden/preprocessing/create_h5ad_file.py | 2 +- 2 files changed, 19 insertions(+), 8 deletions(-) diff --git a/scaden/preprocessing/bulk_simulation.py b/scaden/preprocessing/bulk_simulation.py index 3db2d3c..8cc495c 100644 --- a/scaden/preprocessing/bulk_simulation.py +++ b/scaden/preprocessing/bulk_simulation.py @@ -160,6 +160,22 @@ def filter_matrix_signature(mat, genes): mat = mat[genes] return mat +def load_celltypes(path, name): + """ Load the cell type information """ + try: + y = pd.read_table(path) + # Check if has Celltype column + if not 'Celltype' in y.columns: + logger.error(f"No 'Celltype' column found in {name}_celltypes.txt! Please make sure to include this column.") + sys.exit() + except FileNotFoundError as e: + logger.error(f"No celltypes file found for {name}. It should be called {name}_celltypes.txt.") + sys.exit(e) + + return y + + + def load_dataset(name, dir, pattern): """ @@ -172,12 +188,7 @@ def load_dataset(name, dir, pattern): pattern = pattern.replace("*", "") print("Loading " + name + " dataset ...") - try: - y = pd.read_table(dir + name + "_celltypes.txt") - except FileNotFoundError as e: - logger.error(f"No celltypes file found for {name}. It should be called {name}_celltypes.txt.") - sys.exit() - + y = load_celltypes(dir + name + "_celltypes.txt", name) x = pd.read_table(dir + name + pattern, index_col=0) return (x, y) @@ -285,7 +296,7 @@ def simulate_bulk( datasets = [x.split("_")[0] for x in files] if len(datasets) == 0: - logging.error("No datasetes fround! Have you specified the pattern correctly?") + logging.error("No datasets fround! Have you specified the pattern correctly?") sys.exit() print("Datasets: " + str(datasets)) diff --git a/scaden/preprocessing/create_h5ad_file.py b/scaden/preprocessing/create_h5ad_file.py index 3ef423b..6ce1656 100644 --- a/scaden/preprocessing/create_h5ad_file.py +++ b/scaden/preprocessing/create_h5ad_file.py @@ -27,7 +27,7 @@ def parse_data(x_path, y_path): x = pd.read_table(x_path, sep="\t") y = pd.read_table(y_path, sep="\t") except FileNotFoundError as e: - logging.error(f"Could not find simulated data files: {e}") + logging.error(f" Could not find simulated data files: {e}") sys.exit() labels = list(y.columns) From bbb4395cac3ec94d0300167cbf1c9b0aad93a305 Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Fri, 18 Dec 2020 07:36:15 +0100 Subject: [PATCH 07/26] working TF2 keras version --- CHANGELOG.md | 6 + docs/{docs => }/changelog.md | 0 docs/docs/datasets.md | 27 ----- docs/docs/img/figure1.png | Bin 547442 -> 0 bytes docs/docs/img/scaden_logo.png | Bin 16389 -> 0 bytes docs/docs/index.md | 10 -- docs/docs/installation.md | 34 ------ docs/docs/usage.md | 127 ---------------------- docs/index.md | 32 +----- docs/installation.md | 25 +++-- docs/usage.md | 13 ++- scaden/model/scaden_tf2.py | 199 ++++++++++++++++++++-------------- scaden/scaden/prediction.py | 44 ++++---- scaden/scaden/training.py | 60 +++++----- 14 files changed, 199 insertions(+), 378 deletions(-) rename docs/{docs => }/changelog.md (100%) delete mode 100644 docs/docs/datasets.md delete mode 100644 docs/docs/img/figure1.png delete mode 100644 docs/docs/img/scaden_logo.png delete mode 100644 docs/docs/index.md delete mode 100644 docs/docs/installation.md delete mode 100644 docs/docs/usage.md diff --git a/CHANGELOG.md b/CHANGELOG.md index 752aba2..b904aa7 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,5 +1,11 @@ # Scaden Changelog +### Version 0.9.6 + ++ fixed Dockerfile (switched to pip installation) ++ added better error messages to `simulate` command ++ cleaned up dependencies + ## v0.9.5 * added `--seed` parameter to allow reproducible Scaden runs diff --git a/docs/docs/changelog.md b/docs/changelog.md similarity index 100% rename from docs/docs/changelog.md rename to docs/changelog.md diff --git a/docs/docs/datasets.md b/docs/docs/datasets.md deleted file mode 100644 index c865598..0000000 --- a/docs/docs/datasets.md +++ /dev/null @@ -1,27 +0,0 @@ -# Datasets -This section lists all training datasets currently available for Scaden. - - -#### Mouse Brain -* Link: [Mouse brain training data](https://figshare.com/s/2fec97de0665a4414b03) -* Sample size: 30,000 -* No. Datasets: 5 -* Identifier: MGI Symbol - -#### Human PBMC -* Link: [PBMC training data](https://figshare.com/s/e59a03885ec4c4d8153f) -* Sample size: 32,000 -* No. Datasets: 4 -* Identifier: HGNC Symbol - -#### Human Pancreas -* Link: [Pancreas training data](https://figshare.com/s/b3ec41496a5c35a94165) -* Sample size: 12,000 -* No. Datasets: 2 -* Identifier: HGNC Symbol - -#### Human Ascites -* Link: [Ascites training data](https://figshare.com/s/01df6b6975036f942d1f) -* Sample size: 6,000 -* No. Datasets: 1 -* Identifier: HGNC Symbol diff --git a/docs/docs/img/figure1.png b/docs/docs/img/figure1.png deleted file mode 100644 index 1343f7ebed69d9c53e32fb410d40f8cee657bffa..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 547442 zcmeEugY4WPXN~Q*Ei?bE)Nad0RSGRzkX;w`LbR&7pXiH^*ywltv$S-xmf|c zy}fztob25#pSf7^IJ?WMu1ijfw8d;i*e#Duf(5WFA~BI`F7X z+AX9{sfd)XF7n4QA6(_Q%Lp!nlWh3>d_yb@F2P>l%!~o!5nt z@8eY1!@o|eIj{zJiWVd z#lOe3vEza$9pI7ws}4*j0S)W6&%Y~Zy3gVTRQ)dL#EmESH^J;l##OlgLHZxf3jpr_ z*~A~m!rA}X`X9Fn*KeEtla||KbsNCTKidDJmdXo>|76r3#Wa6Vh5QF+{%AR45P<&2 z=^wQcM?Em_{>_&^FaC23D-irY(*A=j|2J#@Ek*w)kbkP@U&k3`HB%V>tx<-)>X{;r zv&HsK2cPpvMCU(vFP-)TI?`EUeI4J)$DJl`+aDt4m38eijLMPP%&;#rnB6jD`G?u1 z)Bg(B09I}Cq0FfFMK`XFqSr#v&PxEi(0B_m3^jF3UyZQFv0It zewk$-+j6nfl!~WQdP{hCTp|V2?NI+E9i^tnv+g$(IR|#@#I;=YMI__OQhu_J@id~B 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z{%64G28-dq_D+7^dKiJTgWtCRSe|43Z!i$B8@9RWyFnwGrQAiXs6;+BW5DQ^o5u<9RdzG# zbh_(nbtMk@&pSkmjT=B|s;3cQb@0RILzit+fhvqMFKD@cTF9F0zw>>e+OXYrItX4{ z<$4#b9XWUR;I0&w+w}I0jH~f`SoBU1Z#>KRS0a0D!1WO)^jJxZ~N+&7b;d z0F8?{#FzSirB~Gk%wM+3x<@-4*ZK7BAHp-aj|&!6*-VwK4b;8_So)6)>2%fqnfcx= z{M*zenUU=8I!UoM6i+Eq+BFs=;(<*n@TGshq8-NU&A5L2yvlLk>9p6xQ9)eIT)pTv z4=WI?3(cAS>*kR>5L1r7PkzJj6cAR9UK8GY6q|oiMK3O(#JT?s^Yg4br$t-$&H2#& zWTS1CsDAguIDsmg6Qeo^dMB6_x|QqCIhT{W-M7HNcI2p$@CXk6{xm7S3FMdr qknr1Cv$d|rkuZqt>}{4`s6A#h?i|XVtzndXIQjP~GSyO_L;fFr2aC)A diff --git a/docs/docs/index.md b/docs/docs/index.md deleted file mode 100644 index f1d7083..0000000 --- a/docs/docs/index.md +++ /dev/null @@ -1,10 +0,0 @@ -![Scaden](img/scaden_logo.png) - -## Single-cell Assisted Deconvolutional Network - -Scaden is a tool for bulk RNA-seq cell type deconvolutional that uses a deep neural network ensemble trained -on artificial bulk data simulated with scRNA-seq datasets. This method was developed in the [Genome Biology of Neurodegenerativ Diseases group](https://www.dzne.de/en/research/research-areas/fundamental-research/research-groups/heutink/research-areasfocus/) -at the [DZNE Tübingen](https://www.dzne.de/en/about-us/sites/tuebingen/) and the [Medical Systems Biology](https://www.uke.de/english/departments-institutes/institutes/medical-systems-biology/team/index.html) group at the [ZMNH](https://www.uke.de/english/departments-institutes/centers/center-for-molecular-neurobiology-hamburg-(zmnh)/index.html). The main author is [Kevin Menden](https://github.com/KevinMenden). - -A paper describing Scaden has been published in Science Advances: -[Deep-learning based cell composition analysis from tissue expression profiles](https://advances.sciencemag.org/content/6/30/eaba2619) diff --git a/docs/docs/installation.md b/docs/docs/installation.md deleted file mode 100644 index e7f6c21..0000000 --- a/docs/docs/installation.md +++ /dev/null @@ -1,34 +0,0 @@ -# Installation -Scaden be easily installed on a Linux system, and should also work on Mac. -There are currently two options for installing Scaden, either using [Bioconda](https://bioconda.github.io/) or via [pip](https://pypi.org/). - - -## pip -To install Scaden via pip, simply run the following command: - -`pip install scaden` - - -## Bioconda -You can also install Scaden via bioconda, using:: - -`conda install -c bioconda scaden` - - -## Docker -If you don't want to install Scaden at all, but rather use a Docker container, we provide that as well. -For every release, we provide two version - one for CPU and one for GPU usage. -To pull the CPU container, use this command: - -`docker pull ghcr.io/kevinmenden/scaden/scaden` - -For the GPU container: - -`docker pull ghcr.io/kevinmenden/scaden/scaden-gpu` - -## Webtool (beta) -We now also provide a webtool for you: - -[https://scaden.ims.bio](https://scaden.ims.bio) - -It comes with pre-generated training datasets for several tissues. You only need to upload the epxression data for prediction. Please not that this is still in beta. diff --git a/docs/docs/usage.md b/docs/docs/usage.md deleted file mode 100644 index a64bfda..0000000 --- a/docs/docs/usage.md +++ /dev/null @@ -1,127 +0,0 @@ -# Usage - -For a typical deconvolution with Scaden you will have to perform three steps: - -* pre-processing of training data -* training of Scaden model -* prediction - -This assumes that you already have a training dataset. If not, Scaden contains functionality to create a dataset from one or several scRNA-seq datasets. -Please refer to the [data generation](#training-data-generation) section for instructions on how to create training datasets. - -Note that we already provide datasets for certain tissues. All available datasets are listed in the [Datasets](datasets) section. We will -update this section when new datasets are added. - -## Webtool (beta) -If you don't want to install Scaden on your local machine, we now provide a webtool for you: - -[https://scaden.ims.bio](https://scaden.ims.bio) - -It contains pre-generated training datasets for several tissues, and all you need to do is to upload your expression data. Please note that this is still in preview. - -## Pre-processing -The first step is to pre-process your training data. For this you need your training data and the dataset you want to perform deconvolution on. -In this step, Scaden will create a new file for training which only contains the intersection of genes between the training and the prediction data. -Furthermore, the training data will be log2-transformed and scaled to the range [0,1]. Use the following command for pre-processing: - -```console -scaden process -``` - -## Training -Now that your data is set-up, you can start training a Scaden ensemble model. Scaden consists of three deep neural network models. By default, -each of them will be trained for 20,000 steps. You can train longer if you want, although we got good results with this number for datasets of -around 30,000 samples. Use the following command to just train a model for 20,000 steps: - - -```console -scaden train -``` - -This will save the model parameters in your working directory. If you want to create a specific directory for your trained models instead, -and train for 30,00 steps, you can use this command: - - -```console -scaden train --model_dir --steps 30000 -``` - - -You can also adjust the batch size and the learning rate, although we recommend using the default values. If you want to adjust them anyway, use these flages: - - -```console ---batch_size - ---learning_rate -``` - -## Prediction -Finally, after your model is trained, you can start the prediction. If you haven't specified any model directory and just trained a model -in your current directory, you can use the following command to perform the deconvolution: - -```console -scaden predict -``` - -Scaden will then generate a file called 'cdn_predictions.txt' (this name will change in future releases) in your current directory. If the models were saved elsewhere, -you have to tell Scaden where to look for them: - -```console -scaden predict --model_dir -``` - - -You can also change the path and name of the output predictions file using the `outname` flag: - -```console ---outname _.txt for the count data - -_celltypes.txt for the cell type labels - -Here, the `pattern` should be same among all the different datasets (unless you only want to use a specific dataset). - -As example, you can generate 1000 artificial bulk samples from 100 cells per samples with the following command: -```console -scaden simulate --cells 100 --n_samples 1000 --data --pattern -``` - -An example for a pattern would be `*_counts.txt`. This pattern would find the following dataset: -* `dataset_counts.txt` -* `dataset_celltypes.txt` - -Make sure to include an `*` in your pattern! - -This command will create the artificial samples in the current working directory. You can also specificy an output directory using the `--out` parameter. Scaden will also directly create a .h5ad file in this directory, which is the file you will need for training. By default, this file will be called `data.h5ad`, however you can change the prefix using the `--prefix` flag. - - -If you get any errors with the above process, pleas don't hesitate to open an issue on GitHub. \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 0ecdf5a..f1d7083 100644 --- a/docs/index.md +++ b/docs/index.md @@ -6,33 +6,5 @@ Scaden is a tool for bulk RNA-seq cell type deconvolutional that uses a deep neu on artificial bulk data simulated with scRNA-seq datasets. This method was developed in the [Genome Biology of Neurodegenerativ Diseases group](https://www.dzne.de/en/research/research-areas/fundamental-research/research-groups/heutink/research-areasfocus/) at the [DZNE Tübingen](https://www.dzne.de/en/about-us/sites/tuebingen/) and the [Medical Systems Biology](https://www.uke.de/english/departments-institutes/institutes/medical-systems-biology/team/index.html) group at the [ZMNH](https://www.uke.de/english/departments-institutes/centers/center-for-molecular-neurobiology-hamburg-(zmnh)/index.html). The main author is [Kevin Menden](https://github.com/KevinMenden). -A pre-print describing the method is available on Biorxiv: -[Deep-learning-based cell composition analysis from tissue expression profiles](https://www.biorxiv.org/content/10.1101/659227v1) - - -## Changelog - -#### Version 0.9.5 -+ added `scaden simulate` command to perform bulk simulation and training file creation -+ added `--seed` parameter to allow reproducible Scaden runs - -#### Version 0.9.4 -+ fixed dependencies (added python>=3.6 requirement) - -#### Version 0.9.3 -+ upgrade to Tensorflow 2 -+ cleaned up dependencies - -#### Version 0.9.2 -+ RAM usage improvement - -#### Version 0.9.1 -+ Added automatic removal of duplicate genes in Mixture file -+ Changed name of final prediction file -+ Added Scaden logo to main script - -#### Version 0.9.0 -This is the initial release version of Scaden. While this version contains full functionality for pre-processing, training and prediction, it does not -contain thorough error messages, plotting functionality and a solid helper function for generation training data. These are all features -planned for the release of v.1.0.0. -The core functionality of Scaden is, however, implemented and fully operational. Please check the [Usage](usage) section to learn how to use Scaden. \ No newline at end of file +A paper describing Scaden has been published in Science Advances: +[Deep-learning based cell composition analysis from tissue expression profiles](https://advances.sciencemag.org/content/6/30/eaba2619) diff --git a/docs/installation.md b/docs/installation.md index 35f9bd3..e7f6c21 100644 --- a/docs/installation.md +++ b/docs/installation.md @@ -3,25 +3,28 @@ Scaden be easily installed on a Linux system, and should also work on Mac. There are currently two options for installing Scaden, either using [Bioconda](https://bioconda.github.io/) or via [pip](https://pypi.org/). +## pip +To install Scaden via pip, simply run the following command: + +`pip install scaden` + + ## Bioconda -Installation via Bioconda is the preferred route of installation, and we highly recommend using conda. To install Scaden, use: +You can also install Scaden via bioconda, using:: `conda install -c bioconda scaden` -It is always recommended to create a separate conda environment for installation. +## Docker +If you don't want to install Scaden at all, but rather use a Docker container, we provide that as well. +For every release, we provide two version - one for CPU and one for GPU usage. +To pull the CPU container, use this command: -## pip -If you don't want to use conda, you can also install Scaden using pip: +`docker pull ghcr.io/kevinmenden/scaden/scaden` -`pip install scaden` +For the GPU container: - -## Docker -A docker container with Scaden installed is also available from [biocontainers](https://biocontainers.pro/#/). -You can download containers for all Scaden version here: -[https://biocontainers.pro/#/tools/scaden](https://biocontainers.pro/#/tools/scaden) -If you will also find Singularity images there. +`docker pull ghcr.io/kevinmenden/scaden/scaden-gpu` ## Webtool (beta) We now also provide a webtool for you: diff --git a/docs/usage.md b/docs/usage.md index ab71666..a64bfda 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -96,10 +96,9 @@ Once you have done this, you can use Scaden's command `scaden simulate` to gener The first step is to process your scRNA-seq dataset(s) you want to use for training. I used Scanpy for this, and would therefore recommend to do the same, but you can of course use other software for this purpose. I've uploaded the scripts I used to preprocess the data used for the Scaden paper [here](https://doi.org/10.6084/m9.figshare.8234030.v1). Mainly you have to normalize your count data -and create a file containing the cell type labels. The file for the cell type labels should be of size (n x 2), where n is the number of cells -you have in your data. The two columns correspond to a label for your cells, and a 'Celltype' column. In fact, the only necessary column is the 'Celltype' -column, which Scaden uses to extract the information. The count data should be of size (n x g), where g is the number of genes and n is the number of samples. -The order must be the same as for the cell type labels. +and create a file containing the cell type labels. +The file for the cell type labels should be of size (n x 1), where n is the number of cells +you have in your data. The single column in this file should be labeled 'Celltype'. You can have extra columns if you like, as long as you have a 'Celltype' column which specifies the cell type label in the correct order. The count data should be of size (n x g), where g is the number of genes and n is the number of samples. The order must be the same as for the cell type labels. #### Bulk simulation Once the data is processed, you can use the command `scaden simulate` to generate your artificial bulk samples for training. @@ -116,6 +115,12 @@ As example, you can generate 1000 artificial bulk samples from 100 cells per sam scaden simulate --cells 100 --n_samples 1000 --data --pattern ``` +An example for a pattern would be `*_counts.txt`. This pattern would find the following dataset: +* `dataset_counts.txt` +* `dataset_celltypes.txt` + +Make sure to include an `*` in your pattern! + This command will create the artificial samples in the current working directory. You can also specificy an output directory using the `--out` parameter. Scaden will also directly create a .h5ad file in this directory, which is the file you will need for training. By default, this file will be called `data.h5ad`, however you can change the prefix using the `--prefix` flag. diff --git a/scaden/model/scaden_tf2.py b/scaden/model/scaden_tf2.py index 60b9eca..4d3706d 100644 --- a/scaden/model/scaden_tf2.py +++ b/scaden/model/scaden_tf2.py @@ -15,32 +15,36 @@ logger = logging.getLogger(__name__) + class Scaden(object): """ scaden class """ - - def __init__(self, model_dir, - model_name, batch_size=128, - learning_rate=0.0001, num_steps=1000, - seed=0, hidden_units=[256, 128, 64, 32], - do_rates=[0,0,0,0]): - - self.model_dir=model_dir - self.batch_size=batch_size - self.model_name=model_name - self.beta1=0.9 - self.beta2=0.999 - self.learning_rate=learning_rate - self.data=None - self.n_classes=None - self.labels=None - self.x=None - self.y=None - self.num_steps=num_steps - self.scaling="log_min_max" - self.sig_genes=None - self.sample_names=None + def __init__(self, + model_dir, + model_name, + batch_size=128, + learning_rate=0.0001, + num_steps=1000, + seed=0, + hidden_units=[256, 128, 64, 32], + do_rates=[0, 0, 0, 0]): + + self.model_dir = model_dir + self.batch_size = batch_size + self.model_name = model_name + self.beta1 = 0.9 + self.beta2 = 0.999 + self.learning_rate = learning_rate + self.data = None + self.n_classes = None + self.labels = None + self.x = None + self.y = None + self.num_steps = num_steps + self.scaling = "log_min_max" + self.sig_genes = None + self.sample_names = None self.hidden_units = hidden_units self.do_rates = do_rates @@ -53,13 +57,17 @@ def scaden_model(self, n_classes): """Create the Scaden model""" model = tf.keras.Sequential() - model.add(tf.keras.layers.Dense(self.hidden_units[0], activation=tf.nn.relu)) + model.add( + tf.keras.layers.Dense(self.hidden_units[0], activation=tf.nn.relu)) model.add(tf.keras.layers.Dropout(self.do_rates[0])) - model.add(tf.keras.layers.Dense(self.hidden_units[1], activation=tf.nn.relu)) + model.add( + tf.keras.layers.Dense(self.hidden_units[1], activation=tf.nn.relu)) model.add(tf.keras.layers.Dropout(self.do_rates[1])) - model.add(tf.keras.layers.Dense(self.hidden_units[2], activation=tf.nn.relu)) + model.add( + tf.keras.layers.Dense(self.hidden_units[2], activation=tf.nn.relu)) model.add(tf.keras.layers.Dropout(self.do_rates[2])) - model.add(tf.keras.layers.Dense(self.hidden_units[3], activation=tf.nn.relu)) + model.add( + tf.keras.layers.Dense(self.hidden_units[3], activation=tf.nn.relu)) model.add(tf.keras.layers.Dropout(self.do_rates[3])) model.add(tf.keras.layers.Dense(n_classes, activation=tf.nn.softmax)) @@ -72,7 +80,7 @@ def compute_loss(self, logits, targets): :param targets: :return: L1 loss """ - loss = tf.reduce_mean(input_tensor=tf.math.square(logits-targets)) + loss = tf.reduce_mean(input_tensor=tf.math.square(logits - targets)) return loss def compute_accuracy(self, logits, targets, pct_cut=0.05): @@ -82,7 +90,8 @@ def compute_accuracy(self, logits, targets, pct_cut=0.05): :param pct_cut: :return: """ - equality = tf.less_equal(tf.math.abs(tf.math.subtract(logits, targets)), pct_cut) + equality = tf.less_equal( + tf.math.abs(tf.math.subtract(logits, targets)), pct_cut) accuracy = tf.reduce_mean(input_tensor=tf.cast(equality, tf.float32)) return accuracy @@ -95,9 +104,11 @@ def correlation_coefficient(self, logits, targets): """ mx = tf.reduce_mean(input_tensor=logits) my = tf.reduce_mean(input_tensor=targets) - xm, ym = logits-mx, targets-my + xm, ym = logits - mx, targets - my r_num = tf.reduce_sum(input_tensor=tf.multiply(xm, ym)) - r_den = tf.sqrt(tf.multiply(tf.reduce_sum(input_tensor=tf.square(xm)), tf.reduce_sum(input_tensor=tf.square(ym)))) + r_den = tf.sqrt( + tf.multiply(tf.reduce_sum(input_tensor=tf.square(xm)), + tf.reduce_sum(input_tensor=tf.square(ym)))) r = tf.divide(r_num, r_den) r = tf.maximum(tf.minimum(r, 1.0), -1.0) return r @@ -115,28 +126,33 @@ def visualization(self, logits, targets, classes): eval_metrics = {"rmse": rmse, "pcor": pcor} for i in range(logits.shape[1]): - eval_metrics["mre_" + str(classes[i])] = tf.compat.v1.metrics.mean_relative_error( - targets[:, i], - logits[:, i], - targets[:, i])[0] - eval_metrics["mae_" + str(classes[i])] = tf.compat.v1.metrics.mean_absolute_error( - targets[:, i], - logits[:, i], - targets[:, i])[0] - eval_metrics["pcor_" + str(classes[i])] = self.correlation_coefficient(targets[:, i],logits[:, i]) - - - eval_metrics["mre_total"] = tf.compat.v1.metrics.mean_relative_error(targets, - logits, - targets)[1] - - eval_metrics["mae_total"] = tf.compat.v1.metrics.mean_relative_error(targets, - logits, - targets)[1] - - eval_metrics["accuracy01"] = self.compute_accuracy(logits, targets, pct_cut=0.01) - eval_metrics["accuracy05"] = self.compute_accuracy(logits, targets, pct_cut=0.05) - eval_metrics["accuracy1"] = self.compute_accuracy(logits, targets, pct_cut=0.1) + eval_metrics[ + "mre_" + + str(classes[i])] = tf.compat.v1.metrics.mean_relative_error( + targets[:, i], logits[:, i], targets[:, i])[0] + eval_metrics[ + "mae_" + + str(classes[i])] = tf.compat.v1.metrics.mean_absolute_error( + targets[:, i], logits[:, i], targets[:, i])[0] + eval_metrics["pcor_" + + str(classes[i])] = self.correlation_coefficient( + targets[:, i], logits[:, i]) + + eval_metrics["mre_total"] = tf.compat.v1.metrics.mean_relative_error( + targets, logits, targets)[1] + + eval_metrics["mae_total"] = tf.compat.v1.metrics.mean_relative_error( + targets, logits, targets)[1] + + eval_metrics["accuracy01"] = self.compute_accuracy(logits, + targets, + pct_cut=0.01) + eval_metrics["accuracy05"] = self.compute_accuracy(logits, + targets, + pct_cut=0.05) + eval_metrics["accuracy1"] = self.compute_accuracy(logits, + targets, + pct_cut=0.1) # Create summary scalars for key, value in eval_metrics.items(): @@ -159,7 +175,9 @@ def load_h5ad_file(self, input_path, batch_size, datasets=[]): try: raw_input = read_h5ad(input_path) except: - logger.error("Could not load training data file! Is it a .h5ad file generated with `scaden process`?") + logger.error( + "Could not load training data file! Is it a .h5ad file generated with `scaden process`?" + ) sys.exit() # Subset dataset @@ -169,20 +187,27 @@ def load_h5ad_file(self, input_path, batch_size, datasets=[]): if ds not in datasets: raw_input = raw_input[raw_input.obs['ds'] != ds].copy() - # Create training dataset - ratios = [raw_input.obs[ctype] for ctype in raw_input.uns['cell_types']] + ratios = [ + raw_input.obs[ctype] for ctype in raw_input.uns['cell_types'] + ] self.x_data = raw_input.X.astype(np.float32) self.y_data = np.array(ratios, dtype=np.float32).transpose() - self.data = tf.data.Dataset.from_tensor_slices((self.x_data, self.y_data)) - self.data = self.data.shuffle(1000).repeat().batch(batch_size=batch_size) + self.data = tf.data.Dataset.from_tensor_slices( + (self.x_data, self.y_data)) + self.data = self.data.shuffle(1000).repeat().batch( + batch_size=batch_size) self.data_iter = iter(self.data) # Extract celltype and feature info self.labels = raw_input.uns['cell_types'] self.sig_genes = list(raw_input.var_names) - def load_prediction_file(self, input_path, sig_genes, labels, scaling=None): + def load_prediction_file(self, + input_path, + sig_genes, + labels, + scaling=None): """ Load a file to perform prediction on it :param input_path: path to input file @@ -197,7 +222,9 @@ def load_prediction_file(self, input_path, sig_genes, labels, scaling=None): # check for duplicates data_index = list(data.index) if not (len(data_index) == len(set(data_index))): - print("Scaden Warning: Your mixture file conatins duplicate genes! The first occuring gene will be used for every duplicate.") + print( + "Scaden Warning: Your mixture file conatins duplicate genes! The first occuring gene will be used for every duplicate." + ) data = data.loc[~data.index.duplicated(keep='first')] data = data.loc[sig_genes] @@ -220,27 +247,25 @@ def build_model(self, input_path, train_datasets, mode="train"): self.global_step = tf.Variable(0, name='global_step', trainable=False) # Load training data - if mode=="train": - self.load_h5ad_file( - input_path=input_path, - batch_size=self.batch_size, - datasets=train_datasets - ) + if mode == "train": + self.load_h5ad_file(input_path=input_path, + batch_size=self.batch_size, + datasets=train_datasets) # Load prediction data - if mode=="predict": + if mode == "predict": self.sample_names = self.load_prediction_file( - input_path=input_path, - sig_genes=self.sig_genes, - labels=self.labels, - scaling=self.scaling - ) + input_path=input_path, + sig_genes=self.sig_genes, + labels=self.labels, + scaling=self.scaling) # Build the model or load if available self.n_classes = len(self.labels) try: - self.model = tf.keras.models.load_model(self.model_dir, compile=False) + self.model = tf.keras.models.load_model(self.model_dir, + compile=False) logger.info("Loaded pre-trained model") except: self.model = self.scaden_model(n_classes=self.n_classes) @@ -256,18 +281,20 @@ def train(self, input_path, train_datasets): optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate) # Build model graph - self.build_model(input_path=input_path, train_datasets=train_datasets, mode="train") + self.build_model(input_path=input_path, + train_datasets=train_datasets, + mode="train") # Training loop pbar = tqdm(range(self.num_steps)) - for step,_ in enumerate(pbar): + for step, _ in enumerate(pbar): x, y = self.data_iter.get_next() with tf.GradientTape() as tape: - self.logits = self.model(x, training = True) - loss = self.compute_loss(self.logits, y) - + self.logits = self.model(x, training=True) + loss = self.compute_loss(self.logits, y) + grads = tape.gradient(loss, self.model.trainable_weights) optimizer.apply_gradients(zip(grads, self.model.trainable_weights)) @@ -281,9 +308,12 @@ def train(self, input_path, train_datasets): # Save the trained model self.model.save(self.model_dir) - pd.DataFrame(self.labels).to_csv(os.path.join(self.model_dir, "celltypes.txt"), sep="\t") - pd.DataFrame(self.sig_genes).to_csv(os.path.join(self.model_dir, "genes.txt"), sep="\t") - + pd.DataFrame(self.labels).to_csv(os.path.join(self.model_dir, + "celltypes.txt"), + sep="\t") + pd.DataFrame(self.sig_genes).to_csv(os.path.join( + self.model_dir, "genes.txt"), + sep="\t") def predict(self, input_path, out_name="scaden_predictions.txt"): """ @@ -299,10 +329,13 @@ def predict(self, input_path, out_name="scaden_predictions.txt"): self.labels = list(labels['0']) # Build model graph - self.build_model(input_path=input_path, train_datasets=[], mode="predict") + self.build_model(input_path=input_path, + train_datasets=[], + mode="predict") - print(self.data) predictions = self.model.predict(self.data) - pred_df = pd.DataFrame(predictions[0], columns=self.labels, index=self.sample_names) + pred_df = pd.DataFrame(predictions, + columns=self.labels, + index=self.sample_names) return pred_df \ No newline at end of file diff --git a/scaden/scaden/prediction.py b/scaden/scaden/prediction.py index d6e0815..c3f75d8 100644 --- a/scaden/scaden/prediction.py +++ b/scaden/scaden/prediction.py @@ -13,7 +13,6 @@ from anndata import read_h5ad from scaden.model.architectures import architectures from scaden.model.scaden_tf2 import Scaden - """ PARAMETERS """ @@ -27,7 +26,6 @@ M512_DO_RATES = architectures['m512'][1] M1024_DO_RATES = architectures['m1024'][1] - # ==========================================# @@ -41,34 +39,34 @@ def prediction(model_dir, data_path, out_name, seed=0): """ # Small model predictions - cdn256 = Scaden( - model_dir=model_dir + "/m256", - model_name='m256', - seed=seed, - hidden_units=M256_HIDDEN_UNITS, - do_rates=M256_DO_RATES) + cdn256 = Scaden(model_dir=model_dir + "/m256", + model_name='m256', + seed=seed, + hidden_units=M256_HIDDEN_UNITS, + do_rates=M256_DO_RATES) # Predict ratios - preds_256 = cdn256.predict(input_path=data_path, out_name='scaden_predictions_m256.txt') + preds_256 = cdn256.predict(input_path=data_path, + out_name='scaden_predictions_m256.txt') # Mid model predictions - cdn512 = Scaden( - model_dir=model_dir+"/m512", - model_name='m512', - seed=seed, - hidden_units=M512_HIDDEN_UNITS, - do_rates=M512_DO_RATES) + cdn512 = Scaden(model_dir=model_dir + "/m512", + model_name='m512', + seed=seed, + hidden_units=M512_HIDDEN_UNITS, + do_rates=M512_DO_RATES) # Predict ratios - preds_512 = cdn512.predict(input_path=data_path, out_name='scaden_predictions_m512.txt') + preds_512 = cdn512.predict(input_path=data_path, + out_name='scaden_predictions_m512.txt') # Large model predictions - cdn1024 = Scaden( - model_dir=model_dir+"/m1024", - model_name='m1024', - seed=seed, - hidden_units=M1024_HIDDEN_UNITS, - do_rates=M256_DO_RATES) + cdn1024 = Scaden(model_dir=model_dir + "/m1024", + model_name='m1024', + seed=seed, + hidden_units=M1024_HIDDEN_UNITS, + do_rates=M256_DO_RATES) # Predict ratios - preds_1024 = cdn1024.predict(input_path=data_path, out_name='scaden_predictions_m1024.txt') + preds_1024 = cdn1024.predict(input_path=data_path, + out_name='scaden_predictions_m1024.txt') # Average predictions preds = (preds_256 + preds_512 + preds_1024) / 3 diff --git a/scaden/scaden/training.py b/scaden/scaden/training.py index f59066c..34c0bc7 100644 --- a/scaden/scaden/training.py +++ b/scaden/scaden/training.py @@ -13,7 +13,6 @@ from anndata import read_h5ad from scaden.model.architectures import architectures from scaden.model.scaden_tf2 import Scaden - """ PARAMETERS """ @@ -30,7 +29,13 @@ # ==========================================# -def training(data_path, train_datasets, model_dir, batch_size, learning_rate, num_steps, seed=0): +def training(data_path, + train_datasets, + model_dir, + batch_size, + learning_rate, + num_steps, + seed=0): """ Perform training of three a scaden model ensemble consisting of three different models :param model_dir: @@ -48,43 +53,40 @@ def training(data_path, train_datasets, model_dir, batch_size, learning_rate, nu # Training of M256 model print("Training M256 Model ...") - cdn256 = Scaden( - model_dir=model_dir+"/m256", - model_name='m256', - batch_size=batch_size, - learning_rate=learning_rate, - num_steps=num_steps, - seed=seed, - hidden_units=M256_HIDDEN_UNITS, - do_rates=M512_DO_RATES) + cdn256 = Scaden(model_dir=model_dir + "/m256", + model_name='m256', + batch_size=batch_size, + learning_rate=learning_rate, + num_steps=num_steps, + seed=seed, + hidden_units=M256_HIDDEN_UNITS, + do_rates=M512_DO_RATES) cdn256.train(input_path=data_path, train_datasets=train_datasets) del cdn256 # Training of M512 model print("Training M512 Model ...") - cdn512 = Scaden( - model_dir=model_dir+"/m512", - model_name='m512', - batch_size=batch_size, - learning_rate=learning_rate, - num_steps=num_steps, - seed=seed, - hidden_units=M512_HIDDEN_UNITS, - do_rates=M512_DO_RATES) + cdn512 = Scaden(model_dir=model_dir + "/m512", + model_name='m512', + batch_size=batch_size, + learning_rate=learning_rate, + num_steps=num_steps, + seed=seed, + hidden_units=M512_HIDDEN_UNITS, + do_rates=M512_DO_RATES) cdn512.train(input_path=data_path, train_datasets=train_datasets) del cdn512 # Training of M1024 model print("Training M1024 Model ...") - cdn1024 = Scaden( - model_dir=model_dir+"/m1024", - model_name='m1024', - batch_size=batch_size, - learning_rate=learning_rate, - num_steps=num_steps, - seed=seed, - hidden_units=M1024_HIDDEN_UNITS, - do_rates=M1024_DO_RATES) + cdn1024 = Scaden(model_dir=model_dir + "/m1024", + model_name='m1024', + batch_size=batch_size, + learning_rate=learning_rate, + num_steps=num_steps, + seed=seed, + hidden_units=M1024_HIDDEN_UNITS, + do_rates=M1024_DO_RATES) cdn1024.train(input_path=data_path, train_datasets=train_datasets) del cdn1024 From b6a90055bae2f313464440f06d9b187e48a4fd2b Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Fri, 18 Dec 2020 07:43:28 +0100 Subject: [PATCH 08/26] refactoring --- scaden/__main__.py | 249 +++++++++------------ scaden/model/scaden.py | 350 +++++++++++++++--------------- scaden/model/scaden_tf2.py | 341 ----------------------------- scaden/{scaden => }/prediction.py | 2 +- scaden/{scaden => }/processing.py | 0 scaden/scaden/__init__.py | 0 scaden/{scaden => }/training.py | 2 +- 7 files changed, 280 insertions(+), 664 deletions(-) delete mode 100644 scaden/model/scaden_tf2.py rename scaden/{scaden => }/prediction.py (98%) rename scaden/{scaden => }/processing.py (100%) delete mode 100644 scaden/scaden/__init__.py rename scaden/{scaden => }/training.py (98%) diff --git a/scaden/__main__.py b/scaden/__main__.py index dd56871..82c0426 100644 --- a/scaden/__main__.py +++ b/scaden/__main__.py @@ -2,9 +2,9 @@ import scaden import logging import os -from scaden.scaden.training import training -from scaden.scaden.prediction import prediction -from scaden.scaden.processing import processing +from scaden.training import training +from scaden.prediction import prediction +from scaden.processing import processing from scaden.preprocessing.simulate import simulation """ @@ -15,7 +15,7 @@ # Logging logger = logging.getLogger() -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' def main(): @@ -33,8 +33,6 @@ def main(): if __name__ == '__main__': main() - - """ Set up the command line client with different commands to execute """ @@ -45,175 +43,136 @@ def main(): def cli(): pass + """ Training mode """ + + @cli.command() -@click.argument( - 'data_path', - type = click.Path(exists=True), - required = True, - metavar = '' -) -@click.option( - '--train_datasets', - default = '', - help = 'Datasets used for training. Uses all by default.' -) -@click.option( - '--model_dir', - default = "./", - help = 'Path to store the model in' -) -@click.option( - '--batch_size', - default = 128, - help = 'Batch size to use for training. Default: 128' -) -@click.option( - '--learning_rate', - default = 0.0001, - help = 'Learning rate used for training. Default: 0.0001' -) -@click.option( - '--steps', - default = 5000, - help = 'Number of training steps' -) -@click.option( - '--seed', - default = 0, - help = "Set random seed" -) -def train(data_path, train_datasets, model_dir, batch_size, learning_rate, steps, seed): +@click.argument('data_path', + type=click.Path(exists=True), + required=True, + metavar='') +@click.option('--train_datasets', + default='', + help='Datasets used for training. Uses all by default.') +@click.option('--model_dir', default="./", help='Path to store the model in') +@click.option('--batch_size', + default=128, + help='Batch size to use for training. Default: 128') +@click.option('--learning_rate', + default=0.0001, + help='Learning rate used for training. Default: 0.0001') +@click.option('--steps', default=5000, help='Number of training steps') +@click.option('--seed', default=0, help="Set random seed") +def train(data_path, train_datasets, model_dir, batch_size, learning_rate, + steps, seed): """ Train a Scaden model """ training(data_path=data_path, - train_datasets=train_datasets, - model_dir=model_dir, - batch_size=batch_size, - learning_rate=learning_rate, - num_steps=steps, - seed=seed) + train_datasets=train_datasets, + model_dir=model_dir, + batch_size=batch_size, + learning_rate=learning_rate, + num_steps=steps, + seed=seed) """ Prediction mode """ + + @cli.command() -@click.argument( - 'data_path', - type = click.Path(exists=True), - required = True, - metavar = '' -) -@click.option( - '--model_dir', - default = "./", - help = 'Path to trained model' -) -@click.option( - '--outname', - default = "scaden_predictions.txt", - help = 'Name of predictions file.' -) -@click.option( - '--seed', - default = 0, - help = "Set random seed" -) +@click.argument('data_path', + type=click.Path(exists=True), + required=True, + metavar='') +@click.option('--model_dir', default="./", help='Path to trained model') +@click.option('--outname', + default="scaden_predictions.txt", + help='Name of predictions file.') +@click.option('--seed', default=0, help="Set random seed") def predict(data_path, model_dir, outname, seed): """ Predict cell type composition using a trained Scaden model""" prediction(model_dir=model_dir, - data_path=data_path, - out_name=outname, - seed=seed) - + data_path=data_path, + out_name=outname, + seed=seed) """ Processing mode """ + + @cli.command() -@click.argument( - 'data_path', - type = click.Path(exists=True), - required = True, - metavar = '' -) -@click.argument( - 'prediction_data', - type = click.Path(exists=True), - required = True, - metavar = '' -) -@click.option( - '--processed_path', - default = "processed.h5ad", - help = 'Path of processed file. Must end with .h5ad' -) +@click.argument('data_path', + type=click.Path(exists=True), + required=True, + metavar='') +@click.argument('prediction_data', + type=click.Path(exists=True), + required=True, + metavar='') +@click.option('--processed_path', + default="processed.h5ad", + help='Path of processed file. Must end with .h5ad') @click.option( '--var_cutoff', - default = 0.1, - help = 'Filter out genes with a variance less than the specified cutoff. A low cutoff is recommended,' - 'this should only remove genes that are obviously uninformative.' -) + default=0.1, + help= + 'Filter out genes with a variance less than the specified cutoff. A low cutoff is recommended,' + 'this should only remove genes that are obviously uninformative.') def process(data_path, prediction_data, processed_path, var_cutoff): """ Process a dataset for training """ processing(data_path=prediction_data, - training_data=data_path, - processed_path=processed_path, - var_cutoff=var_cutoff - ) + training_data=data_path, + processed_path=processed_path, + var_cutoff=var_cutoff) + """ Simulate dataset """ -@cli.command() -@click.option( - '--out', '-o', - default = './', - help = "Directory to store output files in" -) -@click.option( - '--data', '-d', - default = '.', - help = "Path to scRNA-seq dataset(s)" -) -@click.option( - '--cells', '-c', - default = 100, - help = "Number of cells per sample [default: 100]" -) -@click.option( - '--n_samples', '-n', - default = 1000, - help = "Number of samples to simulate [default: 1000]" -) -@click.option( - '--pattern', - default = "*_norm_counts_all.txt", - help = "File pattern to recognize your processed scRNA-seq count files" -) -@click.option( - '--unknown', '-u', - multiple = True, - default = ['unknown'], - help = "Specifiy cell types to merge into the unknown category. Specify this flag for every cell type you want to merge in unknown. [default: unknown]" -) -@click.option( - '--prefix', '-p', - default = "data", - help = "Prefix to append to training .h5ad file [default: data]" -) + +@cli.command() +@click.option('--out', + '-o', + default='./', + help="Directory to store output files in") +@click.option('--data', '-d', default='.', help="Path to scRNA-seq dataset(s)") +@click.option('--cells', + '-c', + default=100, + help="Number of cells per sample [default: 100]") +@click.option('--n_samples', + '-n', + default=1000, + help="Number of samples to simulate [default: 1000]") +@click.option( + '--pattern', + default="*_norm_counts_all.txt", + help="File pattern to recognize your processed scRNA-seq count files") +@click.option( + '--unknown', + '-u', + multiple=True, + default=['unknown'], + help= + "Specifiy cell types to merge into the unknown category. Specify this flag for every cell type you want to merge in unknown. [default: unknown]" +) +@click.option('--prefix', + '-p', + default="data", + help="Prefix to append to training .h5ad file [default: data]") def simulate(out, data, cells, n_samples, pattern, unknown, prefix): """ Create artificial bulk RNA-seq data from scRNA-seq dataset(s)""" - simulation( - simulate_dir=out, - data_dir=data, - sample_size=cells, - num_samples=n_samples, - pattern=pattern, - unknown_celltypes=unknown, - out_prefix=prefix - ) + simulation(simulate_dir=out, + data_dir=data, + sample_size=cells, + num_samples=n_samples, + pattern=pattern, + unknown_celltypes=unknown, + out_prefix=prefix) diff --git a/scaden/model/scaden.py b/scaden/model/scaden.py index ab252a8..4d3706d 100644 --- a/scaden/model/scaden.py +++ b/scaden/model/scaden.py @@ -2,6 +2,9 @@ Cell Deconvolutional Network (scaden) class """ import os +import logging +import sys +import gc import tensorflow as tf import numpy as np import pandas as pd @@ -10,57 +13,65 @@ from .functions import dummy_labels, sample_scaling from tqdm import tqdm +logger = logging.getLogger(__name__) + + class Scaden(object): """ scaden class """ - - def __init__(self, sess, model_dir, model_name, batch_size=128, learning_rate=0.0001, num_steps=1000, seed=0): - self.sess=sess - self.model_dir=model_dir - self.batch_size=batch_size - self.model_name=model_name - self.beta1=0.9 - self.beta2=0.999 - self.learning_rate=learning_rate - self.data=None - self.n_classes=None - self.labels=None - self.x=None - self.y=None - self.num_steps=num_steps - self.scaling="log_min_max" - self.sig_genes=None - self.sample_names=None - self.hidden_units = [256, 128, 64, 32] - self.do_rates = [0, 0, 0, 0] + def __init__(self, + model_dir, + model_name, + batch_size=128, + learning_rate=0.0001, + num_steps=1000, + seed=0, + hidden_units=[256, 128, 64, 32], + do_rates=[0, 0, 0, 0]): + + self.model_dir = model_dir + self.batch_size = batch_size + self.model_name = model_name + self.beta1 = 0.9 + self.beta2 = 0.999 + self.learning_rate = learning_rate + self.data = None + self.n_classes = None + self.labels = None + self.x = None + self.y = None + self.num_steps = num_steps + self.scaling = "log_min_max" + self.sig_genes = None + self.sample_names = None + self.hidden_units = hidden_units + self.do_rates = do_rates # Set seeds for reproducibility tf.random.set_seed(seed) os.environ['TF_DETERMINISTIC_OPS'] = '1' np.random.seed(seed) - - def model_fn(self, X, n_classes, reuse=False): - """ - Model function - :param params: - :param mode: - :return: - """ - activation = tf.nn.relu - with tf.compat.v1.variable_scope("scaden_model", reuse=reuse): - layer1 = tf.compat.v1.layers.dense(X, units=self.hidden_units[0], activation=activation , name="dense1") - do1 = tf.compat.v1.layers.dropout(layer1, rate=self.do_rates[0], training=self.training_mode) - layer2 = tf.compat.v1.layers.dense(do1, units=self.hidden_units[1], activation=activation , name="dense2") - do2 = tf.compat.v1.layers.dropout(layer2, rate=self.do_rates[1], training=self.training_mode) - layer3 = tf.compat.v1.layers.dense(do2, units=self.hidden_units[2], activation=activation , name="dense3") - do3 = tf.compat.v1.layers.dropout(layer3, rate=self.do_rates[2], training=self.training_mode) - layer4 = tf.compat.v1.layers.dense(do3, units=self.hidden_units[3], activation=activation , name="dense4") - do4 = tf.compat.v1.layers.dropout(layer4, rate=self.do_rates[3], training=self.training_mode) - logits = tf.compat.v1.layers.dense(do4, units=n_classes, activation=tf.nn.softmax, name="logits_layer") - - return logits + def scaden_model(self, n_classes): + """Create the Scaden model""" + + model = tf.keras.Sequential() + model.add( + tf.keras.layers.Dense(self.hidden_units[0], activation=tf.nn.relu)) + model.add(tf.keras.layers.Dropout(self.do_rates[0])) + model.add( + tf.keras.layers.Dense(self.hidden_units[1], activation=tf.nn.relu)) + model.add(tf.keras.layers.Dropout(self.do_rates[1])) + model.add( + tf.keras.layers.Dense(self.hidden_units[2], activation=tf.nn.relu)) + model.add(tf.keras.layers.Dropout(self.do_rates[2])) + model.add( + tf.keras.layers.Dense(self.hidden_units[3], activation=tf.nn.relu)) + model.add(tf.keras.layers.Dropout(self.do_rates[3])) + model.add(tf.keras.layers.Dense(n_classes, activation=tf.nn.softmax)) + + return model def compute_loss(self, logits, targets): """ @@ -69,7 +80,7 @@ def compute_loss(self, logits, targets): :param targets: :return: L1 loss """ - loss = tf.reduce_mean(input_tensor=tf.math.square(logits-targets)) + loss = tf.reduce_mean(input_tensor=tf.math.square(logits - targets)) return loss def compute_accuracy(self, logits, targets, pct_cut=0.05): @@ -79,7 +90,8 @@ def compute_accuracy(self, logits, targets, pct_cut=0.05): :param pct_cut: :return: """ - equality = tf.less_equal(tf.math.abs(tf.math.subtract(logits, targets)), pct_cut) + equality = tf.less_equal( + tf.math.abs(tf.math.subtract(logits, targets)), pct_cut) accuracy = tf.reduce_mean(input_tensor=tf.cast(equality, tf.float32)) return accuracy @@ -92,9 +104,11 @@ def correlation_coefficient(self, logits, targets): """ mx = tf.reduce_mean(input_tensor=logits) my = tf.reduce_mean(input_tensor=targets) - xm, ym = logits-mx, targets-my + xm, ym = logits - mx, targets - my r_num = tf.reduce_sum(input_tensor=tf.multiply(xm, ym)) - r_den = tf.sqrt(tf.multiply(tf.reduce_sum(input_tensor=tf.square(xm)), tf.reduce_sum(input_tensor=tf.square(ym)))) + r_den = tf.sqrt( + tf.multiply(tf.reduce_sum(input_tensor=tf.square(xm)), + tf.reduce_sum(input_tensor=tf.square(ym)))) r = tf.divide(r_num, r_den) r = tf.maximum(tf.minimum(r, 1.0), -1.0) return r @@ -112,28 +126,33 @@ def visualization(self, logits, targets, classes): eval_metrics = {"rmse": rmse, "pcor": pcor} for i in range(logits.shape[1]): - eval_metrics["mre_" + str(classes[i])] = tf.compat.v1.metrics.mean_relative_error( - targets[:, i], - logits[:, i], - targets[:, i])[0] - eval_metrics["mae_" + str(classes[i])] = tf.compat.v1.metrics.mean_absolute_error( - targets[:, i], - logits[:, i], - targets[:, i])[0] - eval_metrics["pcor_" + str(classes[i])] = self.correlation_coefficient(targets[:, i],logits[:, i]) - - - eval_metrics["mre_total"] = tf.compat.v1.metrics.mean_relative_error(targets, - logits, - targets)[1] - - eval_metrics["mae_total"] = tf.compat.v1.metrics.mean_relative_error(targets, - logits, - targets)[1] - - eval_metrics["accuracy01"] = self.compute_accuracy(logits, targets, pct_cut=0.01) - eval_metrics["accuracy05"] = self.compute_accuracy(logits, targets, pct_cut=0.05) - eval_metrics["accuracy1"] = self.compute_accuracy(logits, targets, pct_cut=0.1) + eval_metrics[ + "mre_" + + str(classes[i])] = tf.compat.v1.metrics.mean_relative_error( + targets[:, i], logits[:, i], targets[:, i])[0] + eval_metrics[ + "mae_" + + str(classes[i])] = tf.compat.v1.metrics.mean_absolute_error( + targets[:, i], logits[:, i], targets[:, i])[0] + eval_metrics["pcor_" + + str(classes[i])] = self.correlation_coefficient( + targets[:, i], logits[:, i]) + + eval_metrics["mre_total"] = tf.compat.v1.metrics.mean_relative_error( + targets, logits, targets)[1] + + eval_metrics["mae_total"] = tf.compat.v1.metrics.mean_relative_error( + targets, logits, targets)[1] + + eval_metrics["accuracy01"] = self.compute_accuracy(logits, + targets, + pct_cut=0.01) + eval_metrics["accuracy05"] = self.compute_accuracy(logits, + targets, + pct_cut=0.05) + eval_metrics["accuracy1"] = self.compute_accuracy(logits, + targets, + pct_cut=0.1) # Create summary scalars for key, value in eval_metrics.items(): @@ -153,7 +172,13 @@ def load_h5ad_file(self, input_path, batch_size, datasets=[]): :param datasets: a list of datasets to extract from the file :return: Dataset object """ - raw_input = read_h5ad(input_path) + try: + raw_input = read_h5ad(input_path) + except: + logger.error( + "Could not load training data file! Is it a .h5ad file generated with `scaden process`?" + ) + sys.exit() # Subset dataset if len(datasets) > 0: @@ -162,22 +187,27 @@ def load_h5ad_file(self, input_path, batch_size, datasets=[]): if ds not in datasets: raw_input = raw_input[raw_input.obs['ds'] != ds].copy() - # Create training dataset - ratios = [raw_input.obs[ctype] for ctype in raw_input.uns['cell_types']] + ratios = [ + raw_input.obs[ctype] for ctype in raw_input.uns['cell_types'] + ] self.x_data = raw_input.X.astype(np.float32) self.y_data = np.array(ratios, dtype=np.float32).transpose() - # create placeholders - self.x_data_ph = tf.compat.v1.placeholder(self.x_data.dtype, self.x_data.shape, name="x_data_ph") - self.y_data_ph = tf.compat.v1.placeholder(self.y_data.dtype, self.y_data.shape, name="y_data_ph") - self.data = tf.data.Dataset.from_tensor_slices((self.x_data_ph, self.y_data_ph)) - self.data = self.data.shuffle(1000).repeat().batch(batch_size=batch_size) + self.data = tf.data.Dataset.from_tensor_slices( + (self.x_data, self.y_data)) + self.data = self.data.shuffle(1000).repeat().batch( + batch_size=batch_size) + self.data_iter = iter(self.data) # Extract celltype and feature info self.labels = raw_input.uns['cell_types'] self.sig_genes = list(raw_input.var_names) - def load_prediction_file(self, input_path, sig_genes, labels, scaling=None): + def load_prediction_file(self, + input_path, + sig_genes, + labels, + scaling=None): """ Load a file to perform prediction on it :param input_path: path to input file @@ -192,24 +222,19 @@ def load_prediction_file(self, input_path, sig_genes, labels, scaling=None): # check for duplicates data_index = list(data.index) if not (len(data_index) == len(set(data_index))): - print("Scaden Warning: Your mixture file conatins duplicate genes! The firs occuring gene will be used for every duplicate.") + print( + "Scaden Warning: Your mixture file conatins duplicate genes! The first occuring gene will be used for every duplicate." + ) data = data.loc[~data.index.duplicated(keep='first')] data = data.loc[sig_genes] + data = data.T - self.x_data = data.T - self.x_data = self.x_data.astype(np.float32) - m = self.x_data.shape[0] - self.y_dummy = dummy_labels(m, labels) # Scaling if scaling: - self.x_data = sample_scaling(self.x_data, scaling_option=scaling) + data = sample_scaling(data, scaling_option=scaling) - # Create Dataset object from placeholders - self.x_data_ph = tf.compat.v1.placeholder(self.x_data.dtype, self.x_data.shape, name="x_data_ph") - self.y_data_ph = tf.compat.v1.placeholder(self.y_dummy.dtype, self.y_dummy.shape, name="y_data_ph") - self.data = tf.data.Dataset.from_tensor_slices((self.x_data_ph, self.y_data_ph)) - self.data = self.data.batch(batch_size=m) + self.data = data return sample_names @@ -221,39 +246,29 @@ def build_model(self, input_path, train_datasets, mode="train"): """ self.global_step = tf.Variable(0, name='global_step', trainable=False) - # Load data - if mode=="train": - self.load_h5ad_file(input_path=input_path, batch_size=self.batch_size, datasets=train_datasets) - - if mode=="predict": - self.sample_names = self.load_prediction_file(input_path=input_path, sig_genes=self.sig_genes, - labels=self.labels, scaling=self.scaling) - - # Make iterator - iter = tf.compat.v1.data.Iterator.from_structure(tf.compat.v1.data.get_output_types(self.data), tf.compat.v1.data.get_output_shapes(self.data)) - next_element = iter.get_next() - self.data_init_op = iter.make_initializer(self.data) - self.x, self.y = next_element - self.x = tf.cast(self.x, tf.float32) - - self.n_classes = len(self.labels) - - # Placeholder for training mode - self.training_mode = tf.compat.v1.placeholder_with_default(True, shape=()) - - # Model - self.logits = self.model_fn(X=self.x, n_classes=self.n_classes) - - + # Load training data if mode == "train": - # Loss - self.loss = self.compute_loss(self.logits, self.y) - # Summary scalars - self.merged_summary_op = self.visualization(tf.cast(self.logits, tf.float32), targets=tf.cast(self.y, tf.float32), classes=self.labels) - learning_rate = self.learning_rate - # Optimizer - self.optimizer = tf.compat.v1.train.AdamOptimizer(learning_rate=learning_rate).minimize(self.loss, global_step=self.global_step) + self.load_h5ad_file(input_path=input_path, + batch_size=self.batch_size, + datasets=train_datasets) + + # Load prediction data + if mode == "predict": + self.sample_names = self.load_prediction_file( + input_path=input_path, + sig_genes=self.sig_genes, + labels=self.labels, + scaling=self.scaling) + + # Build the model or load if available + self.n_classes = len(self.labels) + try: + self.model = tf.keras.models.load_model(self.model_dir, + compile=False) + logger.info("Loaded pre-trained model") + except: + self.model = self.scaden_model(n_classes=self.n_classes) def train(self, input_path, train_datasets): """ @@ -261,41 +276,46 @@ def train(self, input_path, train_datasets): :param num_steps: :return: """ + + # Define the optimizer + optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate) + # Build model graph - self.build_model(input_path=input_path, train_datasets=train_datasets, mode="train") + self.build_model(input_path=input_path, + train_datasets=train_datasets, + mode="train") - # Init variables - self.sess.run(tf.compat.v1.global_variables_initializer()) - self.sess.run(tf.compat.v1.local_variables_initializer()) - self.saver = tf.compat.v1.train.Saver() - model = os.path.join(self.model_dir, self.model_name) - self.writer = tf.compat.v1.summary.FileWriter(model, self.sess.graph) - self.eval_writer = tf.compat.v1.summary.FileWriter(os.path.join(self.model_dir, "eval"), self.sess.graph) + # Training loop + pbar = tqdm(range(self.num_steps)) + for step, _ in enumerate(pbar): - # Initialize datasets - self.sess.run(self.data_init_op, feed_dict={self.x_data_ph: self.x_data, self.y_data_ph: self.y_data}) + x, y = self.data_iter.get_next() + with tf.GradientTape() as tape: + self.logits = self.model(x, training=True) + loss = self.compute_loss(self.logits, y) - # Load pre-trained weights if avaialble - self.load_weights(self.model_dir) + grads = tape.gradient(loss, self.model.trainable_weights) - # Training loop - pbar = tqdm(range(self.num_steps)) - for _ in pbar: - _, loss, summary = self.sess.run([self.optimizer, self.loss, self.merged_summary_op]) - self.writer.add_summary(summary, tf.compat.v1.train.global_step(self.sess, self.global_step)) - description = "Step: " + str(tf.compat.v1.train.global_step(self.sess, self.global_step)) + ", Loss: {:4.3f}".format( - loss) - pbar.set_description(desc=description) + optimizer.apply_gradients(zip(grads, self.model.trainable_weights)) - # Save the trained model - self.saver.save(self.sess, model, global_step=self.global_step) - # Save features and celltypes - pd.DataFrame(self.labels).to_csv(self.model_dir + "/celltypes.txt", sep="\t") - pd.DataFrame(self.sig_genes).to_csv(self.model_dir + "/genes.txt", sep="\t") + desc = (f"Step: {step}, Loss: {loss:.4f}") + pbar.set_description(desc=desc) + # Collect garbage after 100 steps - otherwise runs out of memory + if step % 100 == 0: + gc.collect() - def predict(self, input_path, out_name="cdn_predictions.txt"): + # Save the trained model + self.model.save(self.model_dir) + pd.DataFrame(self.labels).to_csv(os.path.join(self.model_dir, + "celltypes.txt"), + sep="\t") + pd.DataFrame(self.sig_genes).to_csv(os.path.join( + self.model_dir, "genes.txt"), + sep="\t") + + def predict(self, input_path, out_name="scaden_predictions.txt"): """ Perform prediction with a pre-trained model :param out_dir: path to store results in @@ -309,35 +329,13 @@ def predict(self, input_path, out_name="cdn_predictions.txt"): self.labels = list(labels['0']) # Build model graph - self.build_model(input_path=input_path, train_datasets=[], mode="predict") - - # Initialize variables - self.sess.run(tf.compat.v1.global_variables_initializer()) - self.sess.run(tf.compat.v1.local_variables_initializer()) - - self.saver = tf.compat.v1.train.Saver() - - model = os.path.join(self.model_dir, self.model_name) - self.writer = tf.compat.v1.summary.FileWriter(model, self.sess.graph) - - # Initialize datasets - self.sess.run(self.data_init_op, feed_dict={self.x_data_ph: self.x_data, self.y_data_ph: self.y_dummy}) - - # Load pre-trained weights if avaialble - self.load_weights(self.model_dir) - - predictions = self.sess.run([self.logits], feed_dict={self.training_mode: False}) - pred_df = pd.DataFrame(predictions[0], columns=self.labels, index=self.sample_names) - #pred_df.to_csv(out_name, sep="\t") - return pred_df - - def load_weights(self, model_dir): - """ - Load pre-trained weights if available - :param model_dir: - :return: - """ - ckpt = tf.train.get_checkpoint_state(model_dir) - if ckpt: - self.saver.restore(self.sess, ckpt.model_checkpoint_path) - print("Model parameters restored successfully") + self.build_model(input_path=input_path, + train_datasets=[], + mode="predict") + + predictions = self.model.predict(self.data) + + pred_df = pd.DataFrame(predictions, + columns=self.labels, + index=self.sample_names) + return pred_df \ No newline at end of file diff --git a/scaden/model/scaden_tf2.py b/scaden/model/scaden_tf2.py deleted file mode 100644 index 4d3706d..0000000 --- a/scaden/model/scaden_tf2.py +++ /dev/null @@ -1,341 +0,0 @@ -""" -Cell Deconvolutional Network (scaden) class -""" -import os -import logging -import sys -import gc -import tensorflow as tf -import numpy as np -import pandas as pd -from anndata import read_h5ad -import collections -from .functions import dummy_labels, sample_scaling -from tqdm import tqdm - -logger = logging.getLogger(__name__) - - -class Scaden(object): - """ - scaden class - """ - def __init__(self, - model_dir, - model_name, - batch_size=128, - learning_rate=0.0001, - num_steps=1000, - seed=0, - hidden_units=[256, 128, 64, 32], - do_rates=[0, 0, 0, 0]): - - self.model_dir = model_dir - self.batch_size = batch_size - self.model_name = model_name - self.beta1 = 0.9 - self.beta2 = 0.999 - self.learning_rate = learning_rate - self.data = None - self.n_classes = None - self.labels = None - self.x = None - self.y = None - self.num_steps = num_steps - self.scaling = "log_min_max" - self.sig_genes = None - self.sample_names = None - self.hidden_units = hidden_units - self.do_rates = do_rates - - # Set seeds for reproducibility - tf.random.set_seed(seed) - os.environ['TF_DETERMINISTIC_OPS'] = '1' - np.random.seed(seed) - - def scaden_model(self, n_classes): - """Create the Scaden model""" - - model = tf.keras.Sequential() - model.add( - tf.keras.layers.Dense(self.hidden_units[0], activation=tf.nn.relu)) - model.add(tf.keras.layers.Dropout(self.do_rates[0])) - model.add( - tf.keras.layers.Dense(self.hidden_units[1], activation=tf.nn.relu)) - model.add(tf.keras.layers.Dropout(self.do_rates[1])) - model.add( - tf.keras.layers.Dense(self.hidden_units[2], activation=tf.nn.relu)) - model.add(tf.keras.layers.Dropout(self.do_rates[2])) - model.add( - tf.keras.layers.Dense(self.hidden_units[3], activation=tf.nn.relu)) - model.add(tf.keras.layers.Dropout(self.do_rates[3])) - model.add(tf.keras.layers.Dense(n_classes, activation=tf.nn.softmax)) - - return model - - def compute_loss(self, logits, targets): - """ - Compute L1 loss - :param logits: - :param targets: - :return: L1 loss - """ - loss = tf.reduce_mean(input_tensor=tf.math.square(logits - targets)) - return loss - - def compute_accuracy(self, logits, targets, pct_cut=0.05): - """ - Compute prediction accuracy - :param targets: - :param pct_cut: - :return: - """ - equality = tf.less_equal( - tf.math.abs(tf.math.subtract(logits, targets)), pct_cut) - accuracy = tf.reduce_mean(input_tensor=tf.cast(equality, tf.float32)) - return accuracy - - def correlation_coefficient(self, logits, targets): - """ - Calculate the pearson correlation coefficient - :param logits: - :param targets: - :return: - """ - mx = tf.reduce_mean(input_tensor=logits) - my = tf.reduce_mean(input_tensor=targets) - xm, ym = logits - mx, targets - my - r_num = tf.reduce_sum(input_tensor=tf.multiply(xm, ym)) - r_den = tf.sqrt( - tf.multiply(tf.reduce_sum(input_tensor=tf.square(xm)), - tf.reduce_sum(input_tensor=tf.square(ym)))) - r = tf.divide(r_num, r_den) - r = tf.maximum(tf.minimum(r, 1.0), -1.0) - return r - - def visualization(self, logits, targets, classes): - """ - Create evaluation metrics - :param targets: - :param classes: - :return: - """ - # add evaluation metrics - rmse = tf.compat.v1.metrics.root_mean_squared_error(logits, targets)[1] - pcor = self.correlation_coefficient(logits, targets) - eval_metrics = {"rmse": rmse, "pcor": pcor} - - for i in range(logits.shape[1]): - eval_metrics[ - "mre_" + - str(classes[i])] = tf.compat.v1.metrics.mean_relative_error( - targets[:, i], logits[:, i], targets[:, i])[0] - eval_metrics[ - "mae_" + - str(classes[i])] = tf.compat.v1.metrics.mean_absolute_error( - targets[:, i], logits[:, i], targets[:, i])[0] - eval_metrics["pcor_" + - str(classes[i])] = self.correlation_coefficient( - targets[:, i], logits[:, i]) - - eval_metrics["mre_total"] = tf.compat.v1.metrics.mean_relative_error( - targets, logits, targets)[1] - - eval_metrics["mae_total"] = tf.compat.v1.metrics.mean_relative_error( - targets, logits, targets)[1] - - eval_metrics["accuracy01"] = self.compute_accuracy(logits, - targets, - pct_cut=0.01) - eval_metrics["accuracy05"] = self.compute_accuracy(logits, - targets, - pct_cut=0.05) - eval_metrics["accuracy1"] = self.compute_accuracy(logits, - targets, - pct_cut=0.1) - - # Create summary scalars - for key, value in eval_metrics.items(): - tf.compat.v1.summary.scalar(key, value) - - tf.compat.v1.summary.scalar('loss', self.loss) - - merged_summary_op = tf.compat.v1.summary.merge_all() - - return merged_summary_op - - def load_h5ad_file(self, input_path, batch_size, datasets=[]): - """ - Load input data from a h5ad file and divide into training and test set - :param input_path: path to h5ad file - :param batch_size: batch size to use for training - :param datasets: a list of datasets to extract from the file - :return: Dataset object - """ - try: - raw_input = read_h5ad(input_path) - except: - logger.error( - "Could not load training data file! Is it a .h5ad file generated with `scaden process`?" - ) - sys.exit() - - # Subset dataset - if len(datasets) > 0: - all_ds = collections.Counter(raw_input.obs['ds']) - for ds in all_ds: - if ds not in datasets: - raw_input = raw_input[raw_input.obs['ds'] != ds].copy() - - # Create training dataset - ratios = [ - raw_input.obs[ctype] for ctype in raw_input.uns['cell_types'] - ] - self.x_data = raw_input.X.astype(np.float32) - self.y_data = np.array(ratios, dtype=np.float32).transpose() - self.data = tf.data.Dataset.from_tensor_slices( - (self.x_data, self.y_data)) - self.data = self.data.shuffle(1000).repeat().batch( - batch_size=batch_size) - self.data_iter = iter(self.data) - - # Extract celltype and feature info - self.labels = raw_input.uns['cell_types'] - self.sig_genes = list(raw_input.var_names) - - def load_prediction_file(self, - input_path, - sig_genes, - labels, - scaling=None): - """ - Load a file to perform prediction on it - :param input_path: path to input file - :param sig_genes: the signature genes to use - :param scaling: which scaling to perform - :return: Dataset object - """ - # Load data - data = pd.read_table(input_path, sep="\t", index_col=0) - sample_names = list(data.columns) - - # check for duplicates - data_index = list(data.index) - if not (len(data_index) == len(set(data_index))): - print( - "Scaden Warning: Your mixture file conatins duplicate genes! The first occuring gene will be used for every duplicate." - ) - data = data.loc[~data.index.duplicated(keep='first')] - - data = data.loc[sig_genes] - data = data.T - - # Scaling - if scaling: - data = sample_scaling(data, scaling_option=scaling) - - self.data = data - - return sample_names - - def build_model(self, input_path, train_datasets, mode="train"): - """ - Build the model graph - :param reuse: - :return: - """ - self.global_step = tf.Variable(0, name='global_step', trainable=False) - - # Load training data - if mode == "train": - self.load_h5ad_file(input_path=input_path, - batch_size=self.batch_size, - datasets=train_datasets) - - # Load prediction data - if mode == "predict": - self.sample_names = self.load_prediction_file( - input_path=input_path, - sig_genes=self.sig_genes, - labels=self.labels, - scaling=self.scaling) - - # Build the model or load if available - self.n_classes = len(self.labels) - - try: - self.model = tf.keras.models.load_model(self.model_dir, - compile=False) - logger.info("Loaded pre-trained model") - except: - self.model = self.scaden_model(n_classes=self.n_classes) - - def train(self, input_path, train_datasets): - """ - Train the model - :param num_steps: - :return: - """ - - # Define the optimizer - optimizer = tf.keras.optimizers.Adam(learning_rate=self.learning_rate) - - # Build model graph - self.build_model(input_path=input_path, - train_datasets=train_datasets, - mode="train") - - # Training loop - pbar = tqdm(range(self.num_steps)) - for step, _ in enumerate(pbar): - - x, y = self.data_iter.get_next() - - with tf.GradientTape() as tape: - self.logits = self.model(x, training=True) - loss = self.compute_loss(self.logits, y) - - grads = tape.gradient(loss, self.model.trainable_weights) - - optimizer.apply_gradients(zip(grads, self.model.trainable_weights)) - - desc = (f"Step: {step}, Loss: {loss:.4f}") - pbar.set_description(desc=desc) - - # Collect garbage after 100 steps - otherwise runs out of memory - if step % 100 == 0: - gc.collect() - - # Save the trained model - self.model.save(self.model_dir) - pd.DataFrame(self.labels).to_csv(os.path.join(self.model_dir, - "celltypes.txt"), - sep="\t") - pd.DataFrame(self.sig_genes).to_csv(os.path.join( - self.model_dir, "genes.txt"), - sep="\t") - - def predict(self, input_path, out_name="scaden_predictions.txt"): - """ - Perform prediction with a pre-trained model - :param out_dir: path to store results in - :param training_data: the dataset used for training - :return: - """ - # Load signature genes and celltype labels - sig_genes = pd.read_table(self.model_dir + "/genes.txt", index_col=0) - self.sig_genes = list(sig_genes['0']) - labels = pd.read_table(self.model_dir + "/celltypes.txt", index_col=0) - self.labels = list(labels['0']) - - # Build model graph - self.build_model(input_path=input_path, - train_datasets=[], - mode="predict") - - predictions = self.model.predict(self.data) - - pred_df = pd.DataFrame(predictions, - columns=self.labels, - index=self.sample_names) - return pred_df \ No newline at end of file diff --git a/scaden/scaden/prediction.py b/scaden/prediction.py similarity index 98% rename from scaden/scaden/prediction.py rename to scaden/prediction.py index c3f75d8..c2d26dd 100644 --- a/scaden/scaden/prediction.py +++ b/scaden/prediction.py @@ -12,7 +12,7 @@ import tensorflow as tf from anndata import read_h5ad from scaden.model.architectures import architectures -from scaden.model.scaden_tf2 import Scaden +from scaden.model.scaden import Scaden """ PARAMETERS """ diff --git a/scaden/scaden/processing.py b/scaden/processing.py similarity index 100% rename from scaden/scaden/processing.py rename to scaden/processing.py diff --git a/scaden/scaden/__init__.py b/scaden/scaden/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/scaden/scaden/training.py b/scaden/training.py similarity index 98% rename from scaden/scaden/training.py rename to scaden/training.py index 34c0bc7..1d32d24 100644 --- a/scaden/scaden/training.py +++ b/scaden/training.py @@ -12,7 +12,7 @@ import tensorflow as tf from anndata import read_h5ad from scaden.model.architectures import architectures -from scaden.model.scaden_tf2 import Scaden +from scaden.model.scaden import Scaden """ PARAMETERS """ From 832ea62888f4cbad4bed1e8df60fd40a7cde132f Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Fri, 18 Dec 2020 07:46:12 +0100 Subject: [PATCH 09/26] more refactoring --- scaden/__main__.py | 8 ++++---- scaden/{prediction.py => predict.py} | 0 scaden/{processing.py => process.py} | 0 scaden/{preprocessing => }/simulate.py | 20 ++++++-------------- scaden/{training.py => train.py} | 0 5 files changed, 10 insertions(+), 18 deletions(-) rename scaden/{prediction.py => predict.py} (100%) rename scaden/{processing.py => process.py} (100%) rename scaden/{preprocessing => }/simulate.py (63%) rename scaden/{training.py => train.py} (100%) diff --git a/scaden/__main__.py b/scaden/__main__.py index 82c0426..8f149f1 100644 --- a/scaden/__main__.py +++ b/scaden/__main__.py @@ -2,10 +2,10 @@ import scaden import logging import os -from scaden.training import training -from scaden.prediction import prediction -from scaden.processing import processing -from scaden.preprocessing.simulate import simulation +from scaden.train import training +from scaden.predict import prediction +from scaden.process import processing +from scaden.simulate import simulation """ author: Kevin Menden diff --git a/scaden/prediction.py b/scaden/predict.py similarity index 100% rename from scaden/prediction.py rename to scaden/predict.py diff --git a/scaden/processing.py b/scaden/process.py similarity index 100% rename from scaden/processing.py rename to scaden/process.py diff --git a/scaden/preprocessing/simulate.py b/scaden/simulate.py similarity index 63% rename from scaden/preprocessing/simulate.py rename to scaden/simulate.py index 21c824a..617775f 100644 --- a/scaden/preprocessing/simulate.py +++ b/scaden/simulate.py @@ -6,23 +6,15 @@ and subsequenbt formatting in .h5ad file for training with Scaden """ -def simulation(simulate_dir, data_dir, sample_size, num_samples, pattern, unknown_celltypes, out_prefix): + +def simulation(simulate_dir, data_dir, sample_size, num_samples, pattern, + unknown_celltypes, out_prefix): # Perform the bulk simulation unknown_celltypes = list(unknown_celltypes) - simulate_bulk( - sample_size, - num_samples, - data_dir, - simulate_dir, - pattern, - unknown_celltypes - ) + simulate_bulk(sample_size, num_samples, data_dir, simulate_dir, pattern, + unknown_celltypes) # Create the h5ad training data file out_name = os.path.join(simulate_dir, out_prefix + ".h5ad") - create_h5ad_file( - simulate_dir, - out_name, - unknown_celltypes - ) + create_h5ad_file(simulate_dir, out_name, unknown_celltypes) diff --git a/scaden/training.py b/scaden/train.py similarity index 100% rename from scaden/training.py rename to scaden/train.py From 9844baa7dce5cc9c90619b529c7e9ccf14ae2483 Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Fri, 18 Dec 2020 08:06:06 +0100 Subject: [PATCH 10/26] started with example implementation --- mkdocs.yml | 1 + scaden/__main__.py | 23 +++++++++++++++++++++++ scaden/example.py | 25 +++++++++++++++++++++++++ 3 files changed, 49 insertions(+) create mode 100644 scaden/example.py diff --git a/mkdocs.yml b/mkdocs.yml index 5b64afe..e78c266 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -4,4 +4,5 @@ nav: - Installation: installation.md - Usage: usage.md - Datasets: datasets.md + - Changelog: changelog.md theme: readthedocs diff --git a/scaden/__main__.py b/scaden/__main__.py index 8f149f1..16a59e8 100644 --- a/scaden/__main__.py +++ b/scaden/__main__.py @@ -6,6 +6,7 @@ from scaden.predict import prediction from scaden.process import processing from scaden.simulate import simulation +from scaden.example import exampleCounts """ author: Kevin Menden @@ -176,3 +177,25 @@ def simulate(out, data, cells, n_samples, pattern, unknown, prefix): pattern=pattern, unknown_celltypes=unknown, out_prefix=prefix) + + +""" +Generate example data +""" + + +@cli.command() +@click.option('--out', + '-o', + default='./', + help="Directory to store output files in") +@click.option('--cells', + '-c', + default=10, + help="Number of cells [default: 10]") +@click.option('--genes', + '-g', + default=100, + help="Number of genes [default: 100]") +def example(out, cells, genes): + exampleCounts(n_cells=cells, n_genes=genes) diff --git a/scaden/example.py b/scaden/example.py new file mode 100644 index 0000000..9b4d8e6 --- /dev/null +++ b/scaden/example.py @@ -0,0 +1,25 @@ +""" +Generate random example data which allows for testing and +to give users examples for the input format +""" + +import pandas as pd +import numpy as np +import string +import random + + +def exampleCounts(n_cells=10, n_genes=100): + """ + Generate an example scRNA-seq count file + :param n: number of cells + :param g: number of genes + """ + counts = np.random.randn(n_cells, n_genes) + gene_name_lengths = np.random.choice(list(range(3, 10)), n_genes) + gene_names = [] + for gl in gene_name_lengths: + gene_names.append(''.join(random.choices(string.ascii_letters, k=gl))) + + df = pd.DataFrame(counts, columns=gene_names) + print(df) From 7927c1dd7c32a146cb75c2a704cb5727d43279d2 Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Fri, 18 Dec 2020 16:57:18 +0100 Subject: [PATCH 11/26] Added example data creation --- scaden/__main__.py | 17 +++++++++++++---- scaden/example.py | 46 ++++++++++++++++++++++++++++++++++++---------- 2 files changed, 49 insertions(+), 14 deletions(-) diff --git a/scaden/__main__.py b/scaden/__main__.py index 16a59e8..66cbae6 100644 --- a/scaden/__main__.py +++ b/scaden/__main__.py @@ -6,7 +6,7 @@ from scaden.predict import prediction from scaden.process import processing from scaden.simulate import simulation -from scaden.example import exampleCounts +from scaden.example import exampleData """ author: Kevin Menden @@ -16,7 +16,8 @@ # Logging logger = logging.getLogger() -os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3' +logger.setLevel(logging.INFO) +os.environ['TF_CPP_MIN_LOG_LEVEL'] = '0' def main(): @@ -197,5 +198,13 @@ def simulate(out, data, cells, n_samples, pattern, unknown, prefix): '-g', default=100, help="Number of genes [default: 100]") -def example(out, cells, genes): - exampleCounts(n_cells=cells, n_genes=genes) +@click.option('--out', + '-o', + default="./", + help="Output directory [default: ./]") +@click.option('--samples', + '-n', + default=10, + help="Number of bulk samples [default: 10]") +def example(cells, genes, samples, out): + exampleData(n_cells=cells, n_genes=genes, n_samples=samples, out_dir=out) diff --git a/scaden/example.py b/scaden/example.py index 9b4d8e6..fecfd51 100644 --- a/scaden/example.py +++ b/scaden/example.py @@ -2,24 +2,50 @@ Generate random example data which allows for testing and to give users examples for the input format """ - -import pandas as pd -import numpy as np import string import random +import os +import logging +import pandas as pd +import numpy as np +logger = logging.getLogger(__name__) +logger.setLevel(logging.INFO) -def exampleCounts(n_cells=10, n_genes=100): + +def exampleData(n_cells=10, n_genes=100, n_samples=10, out_dir="./"): """ Generate an example scRNA-seq count file :param n: number of cells :param g: number of genes """ - counts = np.random.randn(n_cells, n_genes) - gene_name_lengths = np.random.choice(list(range(3, 10)), n_genes) - gene_names = [] - for gl in gene_name_lengths: - gene_names.append(''.join(random.choices(string.ascii_letters, k=gl))) + # Generate example scRNA-seq data + counts = np.random.randint(low=0, high=1000, size=(n_cells, n_genes)) + gene_names = ['gene'] * n_genes + for i in range(len(gene_names)): + gene_names[i] = gene_names[i] + str(i) df = pd.DataFrame(counts, columns=gene_names) - print(df) + + # Generate example celltype labels + celltypes = ['celltype'] * np.random.randint(n_cells - 1) + for i in range(len(celltypes)): + celltypes[i] = celltypes[i] + str(i) + celltype_list = random.choices(celltypes, k=n_cells) + ct_df = pd.DataFrame(celltype_list, columns=['Celltype']) + + # Generate example bulk RNA-seq data + bulk = np.random.randint(low=0, high=1000, size=(n_genes, n_samples)) + samples = ['sample'] * n_samples + for i in range(len(samples)): + samples[i] = samples[i] + str(i) + bulk_df = pd.DataFrame(bulk, columns=samples, index=gene_names) + + # Save the data + df.to_csv(os.path.join(out_dir, "example_counts.txt"), sep="\t") + ct_df.to_csv(os.path.join(out_dir, "example_celltypes.txt"), + sep="\t", + index=False) + bulk_df.to_csv(os.path.join(out_dir, "example_bulk_data.txt"), sep="\t") + + logger.warn(f"Example data has been create in {out_dir}") From 3727e3629405523a6a7aed09ec128ccb7b24596e Mon Sep 17 00:00:00 2001 From: Kevin Menden Date: Sat, 19 Dec 2020 13:28:17 +0100 Subject: [PATCH 12/26] added ci workflow --- .github/python-publish.yml | 31 ------------------- .github/workflows/ci-test.yml | 45 ++++++++++++++++++++++++++++ .github/workflows/python-publish.yml | 33 ++++++++++---------- 3 files changed, 61 insertions(+), 48 deletions(-) delete mode 100644 .github/python-publish.yml create mode 100644 .github/workflows/ci-test.yml diff --git a/.github/python-publish.yml b/.github/python-publish.yml deleted file mode 100644 index 4e1ef42..0000000 --- a/.github/python-publish.yml +++ /dev/null @@ -1,31 +0,0 @@ -# This workflows will upload a Python Package using Twine when a release is created -# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries - -name: Upload Python Package - -on: - release: - types: [created] - -jobs: - deploy: - - runs-on: ubuntu-latest - - steps: - - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: '3.x' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install setuptools wheel twine - - name: Build and publish - env: - TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} - TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} - run: | - python setup.py sdist bdist_wheel - twine upload dist/* diff --git a/.github/workflows/ci-test.yml b/.github/workflows/ci-test.yml new file mode 100644 index 0000000..5793457 --- /dev/null +++ b/.github/workflows/ci-test.yml @@ -0,0 +1,45 @@ +name: Scaden CI +# This workflow installs Scaden, creates example data and performs all +# steps of the Scaden workflow: simulation, processing, training and prediction +on: [push, pull_request] + +jobs: + run_scaden: + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: "3.x" + + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine + + - name: Install Scaden + run: | + pip install . + + - name: Create example data + run: | + mkdir example_data + scaden example -out example_data + + - name: Simulate training data + run: | + scaden simulate -d example_data --pattern '*_counts.txt' + + - name: Scaden process + run: | + scaden process data.h5ad example_data/example_bulk_data.txt + + - name: Scaden training + run: | + scaden train processed.h5ad --steps 100 --model_dir model + + - name: Scaden predict + run: | + scaden predict --model_dir model example_data/example_bulk_data.txt diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index 4e1ef42..48c52e1 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -9,23 +9,22 @@ on: jobs: deploy: - runs-on: ubuntu-latest steps: - - uses: actions/checkout@v2 - - name: Set up Python - uses: actions/setup-python@v2 - with: - python-version: '3.x' - - name: Install dependencies - run: | - python -m pip install --upgrade pip - pip install setuptools wheel twine - - name: Build and publish - env: - TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} - TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} - run: | - python setup.py sdist bdist_wheel - twine upload dist/* + - uses: actions/checkout@v2 + - name: Set up Python + uses: actions/setup-python@v2 + with: + python-version: "3.x" + - name: Install dependencies + run: | + python -m pip install --upgrade pip + pip install setuptools wheel twine + - name: Build and publish + env: + TWINE_USERNAME: ${{ secrets.PYPI_USERNAME }} + TWINE_PASSWORD: ${{ secrets.PYPI_PASSWORD }} + run: | + python setup.py sdist bdist_wheel + twine upload dist/* From e4388e5f8d39abb98b2ca97a6deb0fb7155dc738 Mon Sep 17 00:00:00 2001 From: Kevin Menden Date: Sat, 19 Dec 2020 13:33:55 +0100 Subject: [PATCH 13/26] switched to python 3.8 --- .github/workflows/ci-test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci-test.yml b/.github/workflows/ci-test.yml index 5793457..47801d8 100644 --- a/.github/workflows/ci-test.yml +++ b/.github/workflows/ci-test.yml @@ -12,7 +12,7 @@ jobs: - name: Set up Python uses: actions/setup-python@v2 with: - python-version: "3.x" + python-version: "3.8" - name: Install dependencies run: | From 5f335c104d89803db51b75173504db5335cfc3e1 Mon Sep 17 00:00:00 2001 From: Kevin Menden Date: Sat, 19 Dec 2020 13:36:41 +0100 Subject: [PATCH 14/26] bugfix --- .github/workflows/ci-test.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/ci-test.yml b/.github/workflows/ci-test.yml index 47801d8..8b3cae5 100644 --- a/.github/workflows/ci-test.yml +++ b/.github/workflows/ci-test.yml @@ -26,7 +26,7 @@ jobs: - name: Create example data run: | mkdir example_data - scaden example -out example_data + scaden example --out example_data - name: Simulate training data run: | From 10d1ae5e012b0b867135bfa691cefd9be8f431cd Mon Sep 17 00:00:00 2001 From: Kevin Menden Date: Sat, 19 Dec 2020 13:42:30 +0100 Subject: [PATCH 15/26] bugfix --- .github/workflows/ci-test.yml | 9 ++++----- scaden/example.py | 2 +- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/.github/workflows/ci-test.yml b/.github/workflows/ci-test.yml index 8b3cae5..746410c 100644 --- a/.github/workflows/ci-test.yml +++ b/.github/workflows/ci-test.yml @@ -25,16 +25,15 @@ jobs: - name: Create example data run: | - mkdir example_data - scaden example --out example_data + scaden example - name: Simulate training data run: | - scaden simulate -d example_data --pattern '*_counts.txt' + scaden simulate --pattern '*_counts.txt' - name: Scaden process run: | - scaden process data.h5ad example_data/example_bulk_data.txt + scaden process data.h5ad example_bulk_data.txt - name: Scaden training run: | @@ -42,4 +41,4 @@ jobs: - name: Scaden predict run: | - scaden predict --model_dir model example_data/example_bulk_data.txt + scaden predict --model_dir model example_bulk_data.txt diff --git a/scaden/example.py b/scaden/example.py index fecfd51..9d948b2 100644 --- a/scaden/example.py +++ b/scaden/example.py @@ -48,4 +48,4 @@ def exampleData(n_cells=10, n_genes=100, n_samples=10, out_dir="./"): index=False) bulk_df.to_csv(os.path.join(out_dir, "example_bulk_data.txt"), sep="\t") - logger.warn(f"Example data has been create in {out_dir}") + logger.warn(f"Example data has been created in {out_dir}") From e2ce341e1df0023a9ac0a7e3550baed18df86794 Mon Sep 17 00:00:00 2001 From: Kevin Menden Date: Sat, 19 Dec 2020 13:45:08 +0100 Subject: [PATCH 16/26] bugfix --- .github/workflows/ci-test.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/ci-test.yml b/.github/workflows/ci-test.yml index 746410c..a21f3a8 100644 --- a/.github/workflows/ci-test.yml +++ b/.github/workflows/ci-test.yml @@ -26,6 +26,7 @@ jobs: - name: Create example data run: | scaden example + scaden simulate --pattern '*_counts.txt' - name: Simulate training data run: | From 376be0d5ea2c20dc8c7b3fea8a1121eab1e5bdde Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Sat, 19 Dec 2020 13:58:47 +0100 Subject: [PATCH 17/26] fixed bug in CI workflow --- .github/workflows/ci-test.yml | 10 ++-- scaden/preprocessing/bulk_simulation.py | 77 +++++++++++++++---------- 2 files changed, 52 insertions(+), 35 deletions(-) diff --git a/.github/workflows/ci-test.yml b/.github/workflows/ci-test.yml index a21f3a8..d8a69fd 100644 --- a/.github/workflows/ci-test.yml +++ b/.github/workflows/ci-test.yml @@ -25,16 +25,16 @@ jobs: - name: Create example data run: | - scaden example - scaden simulate --pattern '*_counts.txt' + mkdir example_data + scaden example --out example_data/ - name: Simulate training data run: | - scaden simulate --pattern '*_counts.txt' + scaden simulate --data example_data/ --pattern '*_counts.txt' - name: Scaden process run: | - scaden process data.h5ad example_bulk_data.txt + scaden process data.h5ad example_data/example_bulk_data.txt - name: Scaden training run: | @@ -42,4 +42,4 @@ jobs: - name: Scaden predict run: | - scaden predict --model_dir model example_bulk_data.txt + scaden predict --model_dir model example_data/example_bulk_data.txt diff --git a/scaden/preprocessing/bulk_simulation.py b/scaden/preprocessing/bulk_simulation.py index 8cc495c..b0e0f32 100644 --- a/scaden/preprocessing/bulk_simulation.py +++ b/scaden/preprocessing/bulk_simulation.py @@ -12,6 +12,7 @@ logger = logging.getLogger(__name__) + def create_fractions(no_celltypes): """ Create random fractions @@ -24,7 +25,12 @@ def create_fractions(no_celltypes): return fracs -def create_subsample(x, y, sample_size, celltypes, available_celltypes, sparse=False): +def create_subsample(x, + y, + sample_size, + celltypes, + available_celltypes, + sparse=False): """ Generate artifical bulk subsample with random fractions of celltypes If sparse is set to true, add random celltypes to the missing celltypes @@ -39,9 +45,9 @@ def create_subsample(x, y, sample_size, celltypes, available_celltypes, sparse=F if sparse: no_keep = np.random.randint(1, len(available_celltypes)) - keep = np.random.choice( - list(range(len(available_celltypes))), size=no_keep, replace=False - ) + keep = np.random.choice(list(range(len(available_celltypes))), + size=no_keep, + replace=False) available_celltypes = [available_celltypes[i] for i in keep] no_avail_cts = len(available_celltypes) @@ -61,7 +67,8 @@ def create_subsample(x, y, sample_size, celltypes, available_celltypes, sparse=F for i in range(no_avail_cts): ct = available_celltypes[i] cells_sub = x.loc[np.array(y["Celltype"] == ct), :] - cells_fraction = np.random.randint(0, cells_sub.shape[0], samp_fracs[i]) + cells_fraction = np.random.randint(0, cells_sub.shape[0], + samp_fracs[i]) cells_sub = cells_sub.iloc[cells_fraction, :] artificial_samples.append(cells_sub) @@ -91,9 +98,8 @@ def create_subsample_dataset(x, y, sample_size, celltypes, no_samples): pbar = tqdm(range(no_samples)) pbar.set_description(desc="Normal samples") for _ in pbar: - sample, label = create_subsample( - x, y, sample_size, celltypes, available_celltypes - ) + sample, label = create_subsample(x, y, sample_size, celltypes, + available_celltypes) X.append(sample) Y.append(label) @@ -102,9 +108,12 @@ def create_subsample_dataset(x, y, sample_size, celltypes, no_samples): pbar = tqdm(range(n_sparse)) pbar.set_description(desc="Sparse samples") for _ in pbar: - sample, label = create_subsample( - x, y, sample_size, celltypes, available_celltypes, sparse=True - ) + sample, label = create_subsample(x, + y, + sample_size, + celltypes, + available_celltypes, + sparse=True) X.append(sample) Y.append(label) X = pd.concat(X, axis=1).T @@ -160,21 +169,24 @@ def filter_matrix_signature(mat, genes): mat = mat[genes] return mat + def load_celltypes(path, name): """ Load the cell type information """ try: y = pd.read_table(path) # Check if has Celltype column if not 'Celltype' in y.columns: - logger.error(f"No 'Celltype' column found in {name}_celltypes.txt! Please make sure to include this column.") + logger.error( + f"No 'Celltype' column found in {name}_celltypes.txt! Please make sure to include this column." + ) sys.exit() except FileNotFoundError as e: - logger.error(f"No celltypes file found for {name}. It should be called {name}_celltypes.txt.") + logger.error( + f"No celltypes file found for {name}. It should be called {name}_celltypes.txt." + ) sys.exit(e) - - return y - + return y def load_dataset(name, dir, pattern): @@ -190,7 +202,7 @@ def load_dataset(name, dir, pattern): y = load_celltypes(dir + name + "_celltypes.txt", name) x = pd.read_table(dir + name + pattern, index_col=0) - + return (x, y) @@ -203,7 +215,9 @@ def merge_unkown_celltypes(y, unknown_celltypes): :return: """ celltypes = list(y["Celltype"]) - new_celltypes = ["Unknown" if x in unknown_celltypes else x for x in celltypes] + new_celltypes = [ + "Unknown" if x in unknown_celltypes else x for x in celltypes + ] y["Celltype"] = new_celltypes return y @@ -273,9 +287,8 @@ def generate_signature(x, y): return signature_matrix -def simulate_bulk( - sample_size, num_samples, data_path, out_dir, pattern, unknown_celltypes -): +def simulate_bulk(sample_size, num_samples, data_path, out_dir, pattern, + unknown_celltypes): """ Simulate artificial bulk samples from single cell datasets :param sample_size: number of cells per sample @@ -287,16 +300,17 @@ def simulate_bulk( """ num_samples = int( - num_samples / 2 - ) # divide by two so half is sparse and half is normal samples + num_samples / + 2) # divide by two so half is sparse and half is normal samples # List available datasets - files = glob.glob(data_path + pattern) + files = glob.glob(os.path.join(data_path + pattern)) files = [os.path.basename(x) for x in files] datasets = [x.split("_")[0] for x in files] if len(datasets) == 0: - logging.error("No datasets fround! Have you specified the pattern correctly?") + logging.error( + "No datasets fround! Have you specified the pattern correctly?") sys.exit() print("Datasets: " + str(datasets)) @@ -326,11 +340,14 @@ def simulate_bulk( # Create datasets for i in range(len(xs)): print("Subsampling " + datasets[i] + "...") - tmpx, tmpy = create_subsample_dataset( - xs[i], ys[i], sample_size, celltypes, num_samples - ) - tmpx.to_csv(out_dir + datasets[i] + "_samples.txt", sep="\t", index=False) - tmpy.to_csv(out_dir + datasets[i] + "_labels.txt", sep="\t", index=False) + tmpx, tmpy = create_subsample_dataset(xs[i], ys[i], sample_size, + celltypes, num_samples) + tmpx.to_csv(out_dir + datasets[i] + "_samples.txt", + sep="\t", + index=False) + tmpy.to_csv(out_dir + datasets[i] + "_labels.txt", + sep="\t", + index=False) gc.collect() print("Finished!") From 14d13c756c5ca6f8496e825b5a0d0eb1b949ac8e Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Sat, 19 Dec 2020 14:02:04 +0100 Subject: [PATCH 18/26] bugfix in example script --- scaden/example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scaden/example.py b/scaden/example.py index 9d948b2..311773a 100644 --- a/scaden/example.py +++ b/scaden/example.py @@ -28,7 +28,7 @@ def exampleData(n_cells=10, n_genes=100, n_samples=10, out_dir="./"): df = pd.DataFrame(counts, columns=gene_names) # Generate example celltype labels - celltypes = ['celltype'] * np.random.randint(n_cells - 1) + celltypes = ['celltype'] * np.random.randint(low=2, high=n_cells - 1) for i in range(len(celltypes)): celltypes[i] = celltypes[i] + str(i) celltype_list = random.choices(celltypes, k=n_cells) From 0c8bc9e7f9c9c0f957614f7807f1574f8df5ac4a Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Mon, 21 Dec 2020 07:45:14 +0100 Subject: [PATCH 19/26] fixed filename bug --- README.md | 3 ++- scaden/preprocessing/bulk_simulation.py | 5 ++++- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 63d9cce..2b67ffd 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,8 @@ ![Install with pip](https://img.shields.io/badge/Install%20with-pip-blue) ![Install with Bioconda](https://img.shields.io/badge/Install%20with-conda-green) ![Downloads](https://static.pepy.tech/personalized-badge/scaden?period=total&units=international_system&left_color=blue&right_color=green&left_text=Downloads) - +![Docker](https://github.com/kevinmenden/scaden/workflows/Docker/badge.svg) +![Scaden CI](https://github.com/kevinmenden/scaden/workflows/Scaden%20CI/badge.svg) ## Single-cell assisted deconvolutional network Scaden is a deep-learning based algorithm for cell type deconvolution of bulk RNA-seq samples. It was developed diff --git a/scaden/preprocessing/bulk_simulation.py b/scaden/preprocessing/bulk_simulation.py index b0e0f32..2fac7a2 100644 --- a/scaden/preprocessing/bulk_simulation.py +++ b/scaden/preprocessing/bulk_simulation.py @@ -9,6 +9,7 @@ import pandas as pd import numpy as np from tqdm import tqdm +from pathlib import Path logger = logging.getLogger(__name__) @@ -304,7 +305,9 @@ def simulate_bulk(sample_size, num_samples, data_path, out_dir, pattern, 2) # divide by two so half is sparse and half is normal samples # List available datasets - files = glob.glob(os.path.join(data_path + pattern)) + if not data_path.endswith("/"): + data_path += "/" + files = glob.glob(os.path.join(data_path, pattern)) files = [os.path.basename(x) for x in files] datasets = [x.split("_")[0] for x in files] From a9cb861ee46dd5b7d7f83c05196749faece87371 Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Mon, 21 Dec 2020 08:03:33 +0100 Subject: [PATCH 20/26] made input reading of simulate more robust --- scaden/preprocessing/bulk_simulation.py | 47 ++++++++++++++---------- scaden/preprocessing/create_h5ad_file.py | 15 ++++---- 2 files changed, 35 insertions(+), 27 deletions(-) diff --git a/scaden/preprocessing/bulk_simulation.py b/scaden/preprocessing/bulk_simulation.py index 2fac7a2..ffc7380 100644 --- a/scaden/preprocessing/bulk_simulation.py +++ b/scaden/preprocessing/bulk_simulation.py @@ -171,11 +171,22 @@ def filter_matrix_signature(mat, genes): return mat -def load_celltypes(path, name): - """ Load the cell type information """ +def load_dataset(name, dir, pattern): + """ + Load a dataset given its name and the directory + :param name: name of the dataset + :param dir: directory containing the data + :param sig_genes: the signature genes for filtering + :return: X, Y + """ + pattern = pattern.replace("*", "") + print("Loading " + name + " dataset ...") + + # Try to load celltypes try: - y = pd.read_table(path) + y = pd.read_table(os.path.join(dir, name + "_celltypes.txt")) # Check if has Celltype column + print(y.columns) if not 'Celltype' in y.columns: logger.error( f"No 'Celltype' column found in {name}_celltypes.txt! Please make sure to include this column." @@ -187,22 +198,20 @@ def load_celltypes(path, name): ) sys.exit(e) - return y - - -def load_dataset(name, dir, pattern): - """ - Load a dataset given its name and the directory - :param name: name of the dataset - :param dir: directory containing the data - :param sig_genes: the signature genes for filtering - :return: X, Y - """ - pattern = pattern.replace("*", "") - print("Loading " + name + " dataset ...") + # Try to load data file + try: + x = pd.read_table(os.path.join(dir, name + pattern), index_col=0) + except FileNotFoundError as e: + logger.error( + f"No counts file found for {name}. Was looking for file {name + pattern}" + ) - y = load_celltypes(dir + name + "_celltypes.txt", name) - x = pd.read_table(dir + name + pattern, index_col=0) + # Check that celltypes and count file have same number of cells + if not y.shape[0] == x.shape[0]: + logger.error( + f"Different number of cells in {name}_celltypes and {name + pattern}! Make sure the data has been processed correctly." + ) + sys.exit(1) return (x, y) @@ -314,7 +323,7 @@ def simulate_bulk(sample_size, num_samples, data_path, out_dir, pattern, if len(datasets) == 0: logging.error( "No datasets fround! Have you specified the pattern correctly?") - sys.exit() + sys.exit(1) print("Datasets: " + str(datasets)) diff --git a/scaden/preprocessing/create_h5ad_file.py b/scaden/preprocessing/create_h5ad_file.py index 6ce1656..86ae01e 100644 --- a/scaden/preprocessing/create_h5ad_file.py +++ b/scaden/preprocessing/create_h5ad_file.py @@ -15,6 +15,7 @@ logger = logging.getLogger(__name__) + def parse_data(x_path, y_path): """ Parse data and labels and divide them into training and testset @@ -94,18 +95,16 @@ def create_h5ad_file(data_dir, out_path, unknown, pattern="*_samples.txt"): x = x.sort_index(axis=1) ratios = pd.DataFrame(y, columns=celltypes) - ratios["ds"] = pd.Series(np.repeat(train_file, y.shape[0]), index=ratios.index) + ratios["ds"] = pd.Series(np.repeat(train_file, y.shape[0]), + index=ratios.index) print("Processing " + str(train_file)) x = pd.DataFrame(x) adata.append( - anndata.AnnData( - X=x.to_numpy(), obs=ratios, var=pd.DataFrame(columns=[], index=list(x)) - ) - ) - - - + anndata.AnnData(X=x.to_numpy(), + obs=ratios, + var=pd.DataFrame(columns=[], index=list(x)))) + for i in range(1, len(adata)): print("Concatenating " + str(i)) adata[0] = adata[0].concatenate(adata[1]) From ccea5fa413d9d9f61ae15b1d20b05a78f454e34a Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Mon, 21 Dec 2020 08:16:32 +0100 Subject: [PATCH 21/26] changelog update --- CHANGELOG.md | 18 ++++++++++----- docs/changelog.md | 57 ++++++++++++++++++++++++++++++----------------- 2 files changed, 49 insertions(+), 26 deletions(-) diff --git a/CHANGELOG.md b/CHANGELOG.md index b904aa7..8726159 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,37 +1,43 @@ # Scaden Changelog +### Version 1.0.0 + +* added `scaden example` command which allows to generate example data for test-running scaden and to inpstec the expected file format +* added more tests and checks input reading function in `scaden simulate` +* fixed bug in reading input data + ### Version 0.9.6 + fixed Dockerfile (switched to pip installation) + added better error messages to `simulate` command + cleaned up dependencies -## v0.9.5 +### v0.9.5 * added `--seed` parameter to allow reproducible Scaden runs * added `scaden simulate` command to perform bulk simulation and training file creation * changed CLI calling -## v0.9.4 +### v0.9.4 * fixed dependencies (added python>=3.6 requirement) -## v0.9.3 +### v0.9.3 * upgrade to tf2 * cleaned up dependencies -## v0.9.2 +### v0.9.2 * small code refactoring * RAM usage improvement -## v0.9.1 +### v0.9.1 * added automatic removal of duplicate genes * changed name of prediction file -## v0.9.0 +### v0.9.0 Initial release of the Scaden deconvolution package. diff --git a/docs/changelog.md b/docs/changelog.md index 2f0e14a..8726159 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -1,31 +1,48 @@ -# Changelog +# Scaden Changelog + +### Version 1.0.0 + +* added `scaden example` command which allows to generate example data for test-running scaden and to inpstec the expected file format +* added more tests and checks input reading function in `scaden simulate` +* fixed bug in reading input data ### Version 0.9.6 + + fixed Dockerfile (switched to pip installation) + added better error messages to `simulate` command + cleaned up dependencies -### Version 0.9.5 -+ added `scaden simulate` command to perform bulk simulation and training file creation -+ added `--seed` parameter to allow reproducible Scaden runs +### v0.9.5 -### Version 0.9.4 -+ fixed dependencies (added python>=3.6 requirement) +* added `--seed` parameter to allow reproducible Scaden runs +* added `scaden simulate` command to perform bulk simulation and training file creation +* changed CLI calling -### Version 0.9.3 -+ upgrade to Tensorflow 2 -+ cleaned up dependencies +### v0.9.4 + +* fixed dependencies (added python>=3.6 requirement) + +### v0.9.3 + +* upgrade to tf2 +* cleaned up dependencies + +### v0.9.2 + +* small code refactoring +* RAM usage improvement + +### v0.9.1 + +* added automatic removal of duplicate genes +* changed name of prediction file + +### v0.9.0 -### Version 0.9.2 -+ RAM usage improvement +Initial release of the Scaden deconvolution package. -### Version 0.9.1 -+ Added automatic removal of duplicate genes in Mixture file -+ Changed name of final prediction file -+ Added Scaden logo to main script +Commands: -### Version 0.9.0 -This is the initial release version of Scaden. While this version contains full functionality for pre-processing, training and prediction, it does not -contain thorough error messages, plotting functionality and a solid helper function for generation training data. These are all features -planned for the release of v.1.0.0. -The core functionality of Scaden is, however, implemented and fully operational. Please check the [Usage](usage) section to learn how to use Scaden. \ No newline at end of file +* `scaden process`: Process a training dataset for training +* `scaden train`: Train a Scaden model +* `scaden predict`: Predict cell type compositions of a given sample \ No newline at end of file From f0a4ee6ed3498cbedfcfb865352fad913158354b Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Mon, 21 Dec 2020 08:19:43 +0100 Subject: [PATCH 22/26] changelog update --- CHANGELOG.md | 1 + docs/changelog.md | 1 + 2 files changed, 2 insertions(+) diff --git a/CHANGELOG.md b/CHANGELOG.md index 8726159..a53be87 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -2,6 +2,7 @@ ### Version 1.0.0 +* Rebuild Scaden model and training to use TF2 Keras API instead of the old compatibility functions * added `scaden example` command which allows to generate example data for test-running scaden and to inpstec the expected file format * added more tests and checks input reading function in `scaden simulate` * fixed bug in reading input data diff --git a/docs/changelog.md b/docs/changelog.md index 8726159..a53be87 100644 --- a/docs/changelog.md +++ b/docs/changelog.md @@ -2,6 +2,7 @@ ### Version 1.0.0 +* Rebuild Scaden model and training to use TF2 Keras API instead of the old compatibility functions * added `scaden example` command which allows to generate example data for test-running scaden and to inpstec the expected file format * added more tests and checks input reading function in `scaden simulate` * fixed bug in reading input data From 384a76c801a53c956705f7d9311771eda4691a5d Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Mon, 21 Dec 2020 08:25:02 +0100 Subject: [PATCH 23/26] merge bugfix in setup.py --- setup.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/setup.py b/setup.py index 18d993b..9ab0c45 100644 --- a/setup.py +++ b/setup.py @@ -3,8 +3,6 @@ from setuptools import setup, find_packages version = '1.0.0' -======= - with open("README.md", "r", encoding="UTF-8") as fh: long_description = fh.read() @@ -33,4 +31,3 @@ 'pandas', 'numpy', 'scikit-learn', 'tensorflow>=2.0', 'anndata', 'tqdm', 'click', 'h5py~=2.10.0' ]) - From 76c9795fd86b194243bc69c4f58336ab1e5ae309 Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Mon, 21 Dec 2020 09:10:46 +0100 Subject: [PATCH 24/26] usage docs update --- docs/usage.md | 86 ++++++++++++++++++++++++++++----------------------- 1 file changed, 48 insertions(+), 38 deletions(-) diff --git a/docs/usage.md b/docs/usage.md index a64bfda..21b06b3 100644 --- a/docs/usage.md +++ b/docs/usage.md @@ -1,13 +1,18 @@ # Usage -For a typical deconvolution with Scaden you will have to perform three steps: +The Scaden deconvolution workflow consists of three mandatory steps: -* pre-processing of training data -* training of Scaden model -* prediction +* `scaden process` +* `scaden train` +* `scaden predict` -This assumes that you already have a training dataset. If not, Scaden contains functionality to create a dataset from one or several scRNA-seq datasets. -Please refer to the [data generation](#training-data-generation) section for instructions on how to create training datasets. +In the `process` step, Scaden performs pre-processing on your training data, making sure it has the same genes as your prediction (bulk) data and performing some data transformations to make the data suitable for machine learning + +In the `train` step, training of the Scaden ensemble model is performed. + +Finally, the predictions are generated in the `predict` step, for which the trained model is used. + +This assumes that you already have a training dataset. If not, Scaden contains functionality to create a dataset from one or several scRNA-seq datasets. For this, you can use the `scaden simulate` command after preparing some data. Have a look at the [scaden simulate](#scaden-simulate) section for instructions on how to use this command. Note that we already provide datasets for certain tissues. All available datasets are listed in the [Datasets](datasets) section. We will update this section when new datasets are added. @@ -19,7 +24,7 @@ If you don't want to install Scaden on your local machine, we now provide a webt It contains pre-generated training datasets for several tissues, and all you need to do is to upload your expression data. Please note that this is still in preview. -## Pre-processing +## scaden process The first step is to pre-process your training data. For this you need your training data and the dataset you want to perform deconvolution on. In this step, Scaden will create a new file for training which only contains the intersection of genes between the training and the prediction data. Furthermore, the training data will be log2-transformed and scaled to the range [0,1]. Use the following command for pre-processing: @@ -28,43 +33,40 @@ Furthermore, the training data will be log2-transformed and scaled to the range scaden process ``` -## Training +By the default, the output file will be called "processed.h5ad". Have a look at `scaden process --help` for further options. + +## scaden train Now that your data is set-up, you can start training a Scaden ensemble model. Scaden consists of three deep neural network models. By default, -each of them will be trained for 20,000 steps. You can train longer if you want, although we got good results with this number for datasets of -around 30,000 samples. Use the following command to just train a model for 20,000 steps: +each of them will be trained for 5,000 steps, which is the recommended number of training steps. You can train longer if you want, although we got good results with this number for datasets of +around 30,000 samples. Use the following command to just train a model: ```console scaden train ``` -This will save the model parameters in your working directory. If you want to create a specific directory for your trained models instead, -and train for 30,00 steps, you can use this command: +This will save the model parameters in your working directory. We recommend to explicitly specify a model directory like so: ```console -scaden train --model_dir --steps 30000 +scaden train --model_dir ``` - -You can also adjust the batch size and the learning rate, although we recommend using the default values. If you want to adjust them anyway, use these flages: - +For more options, have a look at ```console ---batch_size - ---learning_rate +scaden train --help ``` -## Prediction -Finally, after your model is trained, you can start the prediction. If you haven't specified any model directory and just trained a model +## scaden predict +Finally, after your model is trained, you can perform the prediction. If you haven't specified any model directory and just trained a model in your current directory, you can use the following command to perform the deconvolution: ```console scaden predict ``` -Scaden will then generate a file called 'cdn_predictions.txt' (this name will change in future releases) in your current directory. If the models were saved elsewhere, +Scaden will then generate a file called 'scaden_predictions.txt' in your current directory. If the models were saved elsewhere, you have to tell Scaden where to look for them: ```console @@ -78,27 +80,24 @@ You can also change the path and name of the output predictions file using the ` --outname Date: Mon, 21 Dec 2020 11:19:26 +0100 Subject: [PATCH 25/26] applied suggestions; updated readme --- .github/workflows/python-publish.yml | 2 +- README.md | 81 ++++++++++++++----------- scaden/__main__.py | 2 +- scaden/preprocessing/bulk_simulation.py | 16 ++--- 4 files changed, 57 insertions(+), 44 deletions(-) diff --git a/.github/workflows/python-publish.yml b/.github/workflows/python-publish.yml index 48c52e1..fd627ff 100644 --- a/.github/workflows/python-publish.yml +++ b/.github/workflows/python-publish.yml @@ -19,7 +19,7 @@ jobs: python-version: "3.x" - name: Install dependencies run: | - python -m pip install --upgrade pip + python -m pip install --upgrade pip setuptools wheel twine pip install setuptools wheel twine - name: Build and publish env: diff --git a/README.md b/README.md index 6a84454..cdda247 100644 --- a/README.md +++ b/README.md @@ -25,29 +25,24 @@ Scaden overview. a) Generation of artificial bulk samples with known cell type c of Scaden model ensemble on simulated training data. c) Scaden ensemble architecture. d) A trained Scaden model can be used to deconvolve complex bulk mixtures. -### 1. System requirements -Scaden was developed and tested on Linux (Ubuntu 16.04 and 18.04). It was not tested on Windows or Mac, but should -also be usable on these systems when installing with Pip or Bioconda. Scaden does not require any special -hardware (e.g. GPU), however we recommend to have at least 16 GB of memory. -Scaden requires Python 3. All package dependencies should be handled automatically when installing with pip or conda. -### 2. Installation guide +## Installation guide Scaden can be easily installed on a Linux system, and should also work on Mac. There are currently two options for installing Scaden, either using [Bioconda](https://bioconda.github.io/) or via [pip](https://pypi.org/). -## pip +### pip To install Scaden via pip, simply run the following command: `pip install scaden` -## Bioconda +### Bioconda You can also install Scaden via bioconda, using: `conda install -c bioconda scaden` -## GPU +### GPU If you want to make use of your GPU, you will have to additionally install `tensorflow-gpu`. For pip: @@ -58,7 +53,7 @@ For conda: `conda install tensorflow-gpu` -## Docker +### Docker If you don't want to install Scaden at all, but rather use a Docker container, we provide that as well. For every release, we provide two version - one for CPU and one for GPU usage. To pull the CPU container, use this command: @@ -76,38 +71,54 @@ Additionally, we now proivde a web tool: It contains pre-generated training datasets for several tissues, and all you need to do is to upload your expression data. Please note that this is still in preview. -### 3. Demo -We provide several curated [training datasets](https://scaden.readthedocs.io/en/latest/datasets/) for Scaden. For this demo, -we will use the human PBMC training dataset, which consists of 4 different scRNA-seq datasets and 32,000 samples in total. -You can download it here: -[https://figshare.com/s/e59a03885ec4c4d8153f](https://figshare.com/s/e59a03885ec4c4d8153f). +## Usage +We provide a detailed instructions for how to use Scaden at our [Documentation page](https://scaden.readthedocs.io/en/latest/usage/) + +A deconvolution workflow with Scaden consists of four major steps: +* data simulation +* data processing +* training +* prediction + +If training data is already available, you can start at the data processing step. Otherwise you will first have to process scRNA-seq datasets and perform data simulation to generate a training dataset. As an example workflow, you can use Scaden's function `scaden example` to generate example data and go through the whole pipeline. + +First, make an example data directory and generate the example data: +```bash +mkdir example_data +scaden example --out example_data/ +``` +This generates the files "example_counts.txt", "example_celltypes.txt" and "example_bulk_data.txt" in the "example_data" directory. Next, you can generate training data: -For this demo, you will also need to download some test samples to perform deconvolution on, along with their associated labels. -You can download the data we used for the Scaden paper here: -[https://figshare.com/articles/Publication_Figures/8234030](https://figshare.com/articles/Publication_Figures/8234030) +```bash +scaden simulate --data example_data/ -n 100 --pattern "*_counts.txt +``` -We'll perform deconvolution on simulated samples from the data6k dataset. You can find the samples and labels in 'paper_data/figures/figure2/data/data6k_500_*' -once you have downloaded this data from the link mentioned above. +This generates 100 samples of training data in your current working directory. The file you need for your next step is called "data.h5ad". Now you need to perform the preprocessing using the training data and the bulk data file: -The first step is to perform preprocessing on the training data. This is done with the following command: +```bash +scaden process data.h5ad example_data/example_bulk_data.txt +``` -`scaden process pbmc_data.h5ad paper_data/figures/figure2/data/data6k_500_samples.txt` +As a result, you should now have a file called "processed.h5ad" in your directory. Now you can perform training. The following command performs training for 5000 steps per model and saves the trained weights to the "model" directory, which will be created: -This will generate a file called 'processed.h5ad', which we will use for training. The training data -we have downloaded also contains samples from the data6k scRNA-seq dataset, so we have to exclude them from training -to get a meaningfull test of Scaden's performance. The following command will train a Scaden ensemble for 5000 steps per model (recommended), -and store it in 'scaden_model'. Data from the data6k dataset will be excluded from training. Depending on your machine, this can take about 10-20 minutes. +```bash +scaden train processed.h5ad --steps 5000 --model_dir model +``` -`scaden train processed.h5ad --steps 5000 --model_dir scaden_model --train_datasets 'data8k donorA donorC'` +Finally, you can use the trained model to perform prediction: -Finally, we can perform deconvolution on the 500 simulates samples from the data6k dataset: +```bash +scaden predict --model_dir model example_data/example_bulk_data.txt +``` -`scaden predict paper_data/figures/figure2/data/data6k_500_samples.txt --model_dir scaden_model` +Now you should have a file called "scaden_predictions.txt" in your working directory, which contains your estimated cell compositions. -This will create a file named 'cdn_predictions.txt' (will be renamed in future version to 'scaden_predictions.txt'), which contains -the deconvolution results. You can now compare these predictions with the true values contained in -'paper_data/figures/figure2/data/data6k_500_labels.txt'. This should give you the same results as we obtained in the Scaden paper -(see Figure 2). -### 4. Instructions for use -For a general description on how to use Scaden, please check out our [usage documentation](https://scaden.readthedocs.io/en/latest/usage/). + + +### 1. System requirements +Scaden was developed and tested on Linux (Ubuntu 16.04 and 18.04). It was not tested on Windows or Mac, but should +also be usable on these systems when installing with Pip or Bioconda. Scaden does not require any special +hardware (e.g. GPU), however we recommend to have at least 16 GB of memory. + +Scaden requires Python 3. All package dependencies should be handled automatically when installing with pip or conda. \ No newline at end of file diff --git a/scaden/__main__.py b/scaden/__main__.py index 66cbae6..e3deeba 100644 --- a/scaden/__main__.py +++ b/scaden/__main__.py @@ -155,7 +155,7 @@ def process(data_path, prediction_data, processed_path, var_cutoff): help="Number of samples to simulate [default: 1000]") @click.option( '--pattern', - default="*_norm_counts_all.txt", + default="*_counts.txt", help="File pattern to recognize your processed scRNA-seq count files") @click.option( '--unknown', diff --git a/scaden/preprocessing/bulk_simulation.py b/scaden/preprocessing/bulk_simulation.py index eaf4a6f..52ce3f7 100644 --- a/scaden/preprocessing/bulk_simulation.py +++ b/scaden/preprocessing/bulk_simulation.py @@ -170,21 +170,24 @@ def filter_matrix_signature(mat, genes): mat = mat[genes] return mat + def load_celltypes(path, name): """ Load the cell type information """ try: y = pd.read_table(path) # Check if has Celltype column if not 'Celltype' in y.columns: - logger.error(f"No 'Celltype' column found in {name}_celltypes.txt! Please make sure to include this column.") + logger.error( + f"No 'Celltype' column found in {name}_celltypes.txt! Please make sure to include this column." + ) sys.exit() except FileNotFoundError as e: - logger.error(f"No celltypes file found for {name}. It should be called {name}_celltypes.txt.") + logger.error( + f"No celltypes file found for {name}. It should be called {name}_celltypes.txt." + ) sys.exit(e) - - return y - + return y def load_dataset(name, dir, pattern): @@ -338,10 +341,9 @@ def simulate_bulk(sample_size, num_samples, data_path, out_dir, pattern, if len(datasets) == 0: logging.error( - "No datasets fround! Have you specified the pattern correctly?") + "No datasets found! Have you specified the pattern correctly?") sys.exit(1) - print("Datasets: " + str(datasets)) # Load datasets From fe12c055db0c4d83f24989b8ea92d8a1cd0ace00 Mon Sep 17 00:00:00 2001 From: kevinmenden Date: Mon, 21 Dec 2020 15:19:02 +0100 Subject: [PATCH 26/26] more intuitive train function --- scaden/__main__.py | 8 +++++--- scaden/train.py | 2 +- 2 files changed, 6 insertions(+), 4 deletions(-) diff --git a/scaden/__main__.py b/scaden/__main__.py index e3deeba..580d27b 100644 --- a/scaden/__main__.py +++ b/scaden/__main__.py @@ -56,9 +56,11 @@ def cli(): type=click.Path(exists=True), required=True, metavar='') -@click.option('--train_datasets', - default='', - help='Datasets used for training. Uses all by default.') +@click.option( + '--train_datasets', + default='', + help= + 'Comma-separated list of datasets used for training. Uses all by default.') @click.option('--model_dir', default="./", help='Path to store the model in') @click.option('--batch_size', default=128, diff --git a/scaden/train.py b/scaden/train.py index 1d32d24..1694b54 100644 --- a/scaden/train.py +++ b/scaden/train.py @@ -48,7 +48,7 @@ def training(data_path, if train_datasets == '': train_datasets = [] else: - train_datasets = train_datasets.split() + train_datasets = train_datasets.split(',') print(f"Training on: {train_datasets}") # Training of M256 model