README.md

In Sage-Bionetworks/dten: Infers drugs of interest based on expression

Drug Target Expression Networks

A suite of tools designed to enable identification of drugs effective in a disease of interest based on gene expression data.

Related Packages

Many packages are used by this workflow to enable proper assessment of the gene expressiond ata.

• MetaViper: MetaViper is used to identify proteins of activity from gene expression data
• Drug Target Explorer: DTEx is used to identify protein-drug interactions
• PCSF: Prize-collecting steiner forest package is used for network reduction and pathway enrichment

Using DTEN

The DTEN scientific workflow comprises a series of steps that can be performed individually or in sequence.

Formatted Data

We have built another workflow that aligns and annotates fastq files and uploads them to Synapse. TODO: create workflow that combines aligned counts into tidieid data frame

Getting Proteins of interest

DTEN assumes that files are in a tidied data frame with the following headers:

| Header name | Description | | --- | --- | | gene| Name of gene, either entrez identifier or hugo | | sample | some sample identifier | | counts | quantification of counts | | conditions | Conditions under which that sample applies |

Building networks and getting pathway enrichment

Once we have the proteins we can add them to networks

Storing results on Synapse

Selecting drugs and pathways across conditions

Getting started

1. Install Docker
2. Install cwltool
3. Format your inputs in JSON format
4. cwl-runner workflow-name.cwl `inputfile.json'

Sage-Bionetworks/dten documentation built on Oct. 8, 2019, 5:10 p.m.