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gtex_analysis.sh
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#!/bin/bash
#
# Gregory Way 2019
# BioBombe
# 8.gtex-interpret/gtex_analysis.sh
#
# Perform the full pipeline for the GTEx analysis and validation
# Convert all notebooks to scripts
jupyter nbconvert --to=script \
--FilesWriter.build_directory=scripts/nbconverted \
*.ipynb
# Step 0 - Apply BioBombe Network Projection to the VAE features of interest
jupyter nbconvert --to=html \
--FilesWriter.build_directory=scripts/html \
--ExecutePreprocessor.kernel_name=python3 \
--ExecutePreprocessor.timeout=10000000 \
--execute 0.process-gtex-blood-vae-feature.ipynb
# Step 1 - Download and process publicly available gene expression datasets
# A - Neutrophils: Two Leukemia Cell Line Datasets (GSE103706)
jupyter nbconvert --to=html \
--FilesWriter.build_directory=scripts/html \
--ExecutePreprocessor.kernel_name=python3 \
--ExecutePreprocessor.timeout=10000000 \
--execute 1A.download-neutrophil-data.ipynb
# B - Monocytes: Several isolated populations in stages of hematopoiesis (GSE24759)
jupyter nbconvert --to=html \
--FilesWriter.build_directory=scripts/html \
--ExecutePreprocessor.kernel_name=python3 \
--ExecutePreprocessor.timeout=10000000 \
--execute 1B.download-hematopoietic-data.ipynb
# Step 2 - Apply the signatures identified in step 0 to the public datasets
jupyter nbconvert --to=html \
--FilesWriter.build_directory=scripts/html \
--ExecutePreprocessor.kernel_name=python3 \
--ExecutePreprocessor.timeout=10000000 \
--execute 2.apply-signatures.ipynb
# Step 3 - Apply compressed feature signatures to neutrophil and monocyte data
jupyter nbconvert --to=html \
--FilesWriter.build_directory=scripts/html \
--ExecutePreprocessor.kernel_name=python3 \
--ExecutePreprocessor.timeout=10000000 \
--execute 3.detect-signature-separation-ttest.ipynb
# Step 4 - Generate the final figure visualizing the applied signatures
Rscript --vanilla scripts/nbconverted/4.visualize-signatures.r