timedreamer / gene_coexpression_mr

mr2mods: Mutual Ranks to Modules workflow

Jen Wisecaver

2019-09-25

Set of scripts to identify co-expressed gene sets (i.e., modules) in gene co-expression networks.

Transforming Pearson’s or Spearman's correlation coefficents (PCCs/SCCs) into Mutual Ranks (MRs) — first described by Obayashi & Kinoshita — is a good idea if you want to compare between different datasets and/or functional categories (Obayashi et al, 2018; Liesecke et al, 2018). However, the transformation requires significant memory and disk space to compute. Moreover, MRs range from 1 to n-1 (where n is the number of genes in the genome), which does not translate well to network edge weights. We’ve implemented a series of R and Python scripts for creating MRs and gene modules directly from a directory of Kallisto gene abundance estimates. The code as been developed to run on multiple threads when possible, which significantly decreasing the total runtime. Finally, the pipeline applies exponential decay functions to transform MRs into edge weights and call co-expressed gene sets (i.e. modules) using the program ClusterONE.

See Wisecaver et al. 2017 Plant Cell | PDF

Steps

An in depth tutorial is provided and includes information on files types, run time, memory requirements, etc. The TL:DR steps are provided below.

1. Create a gene expression matrix

Rscript ../scripts/transform_counts.r -a example/abundance_files.txt -l example/transcripts2genes.txt -s example/sample_conditions.txt -t tag -o example/gene_counts 

2. Calculate PCC/SCC and MR for all gene pairs

# for Pearson's
python ../scripts/calc_mr_pcc.py -i example/gene_counts_normalized.matrix -o example/gene_counts_normalized_mr -t 20

# for Spearman's
python ../scripts/calc_mr_scc.py -i example/gene_counts_normalized.matrix -o example/gene_counts_normalized_mr -t 20

3. Run clusterONE and call co-expressed gene modules

python ../scripts/create_network_and_modules.py -i example/gene_counts_normalized_mr -c ../scripts/cluster_one-1.0.jar -d 5