knowengdev/signature_analysis_pipeline repository overview

⁠KnowEnG's Signature Analysis Pipeline

This is the Knowledge Engine for Genomics (KnowEnG), an NIH BD2K Center of Excellence, Signature Analysis Pipeline.

This pipeline performs network-based signature analysis on the columns of a given spreadsheet, where spreadsheet's columns correspond to sample-labels and rows correspond to gene-labels. The signature is based on correlating gene expression data (network enriched) against known gene signature data.

There are four similarity "signature" methods that one can choose from:

If a phenotype data file is included this pipeline evaluates the clustering result.

KnowEnG Signature Analisis on GitHub⁠

⁠How to run this pipeline's docker image with Our data

1. Install Docker - follow the instructions in this link. ============

Install Docker Engine⁠

1. Get the current Docker Image. On the command line with internet connection: ============

docker pull knowengdev/signature_analysis_pipeline:05_10_2018

1. Create or change to a directory to hold the output data. ============

mkdir local_results and / or cd local_results

1. Start the docker image connected to the (local_results) directory. =========== docker run -v local_results:/home/test/run_dir -it knowengdev/signature_analysis_pipeline:10_12_2017

2. At the docker image command prompt change to the test directory. =========== .../home# cd test

3. Set up the environment. ============= .../home/test# make env_setup

1. Use one of the following "make" commands to select and run a clustering option: ===========

⁠How to run this pipeline with Your data

Follow steps 1-3 above then do the following:

⁠* Create your run directory

mkdir run_directory

⁠* Change directory to the run_directory

cd run_directory

⁠* Create your results directory

mkdir results_directory

⁠* Create run_paramters file (YAML Format)

Look for examples of run_parameters in the Gene_Signature_Pipeline/data/run_files zTEMPLATE_cc_net_spearman.yml

⁠* Modify run_paramters file (YAML Format)

Change processing_method to one of: serial, parallel depending on your machine.

processing_method: serial

set the data file targets to the files you want to run, and the parameters as appropriate for your data.

⁠* Run the Gene Signature Pipeline:

• Update PYTHONPATH enviroment variable

export PYTHONPATH='../src':$PYTHONPATH    

• Run

python3 ../src/gene_signature.py -run_directory ./run_dir -run_file zTEMPLATE_cc_net_spearman.yml

⁠Description of "run_parameters" file

gg_network_name = STRING_experimental_gene_gene.edge spreadsheet_name = ProGENI_rwr20_STExp_GDSC_500.rname.gxc.tsv signature_data_name =

⁠Description of Output files saved in results directory

• Output files of all four methods save samples by signature similarity "correlation" with name similarity_matrix_{method}{measure}{timestamp}_viz.tsv.