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CoGAPS

Coordinated Gene Activity in Pattern Sets (CoGAPS) implements a Bayesian MCMC matrix factorization algorithm, GAPS, and links it to gene set statistic methods to infer biological process activity. It can be used to perform sparse matrix factorization on any data, and when this data represents biomolecules, to do gene set analysis.

Installing CoGAPS

Via Bioconductor:

install.packages("BiocManager")
BiocManager::install("FertigLab/CoGAPS")

The most up-to-date version of CoGAPS can be installed directly from the FertigLab Github Repository:

devtools::install_github("FertigLab/CoGAPS")

Using CoGAPS

Follow the vignette here and available as static html here

Run as nextflow pipeline

The example below will attempt running CoGAPS with number of patterns 3 and 4 on every .rds and .h5ad file in the input folder (tests/nextflow).

nextflow run main.nf --input tests/nextflow --outdir out -c nextflow.config -profile docker --max_memory 10GB --npatterns 3,4

Supported CLI params and their defaults are:

npatterns = "5"
nsets = 1
niterations = 100
sparse = 0
seed = 42
distributed = "null"
nthreads = 1
max_memory = '128.GB'
max_cpus = 8
max_time = '72.h'
n_top_genes = 5000