mts_GeneDepID: CRISPR vs gene expression induced dependency pipeline...

In MultiSEp: Predict Synthetic Lethality and Other Gene Dependency Relationships from Multiomics Data

CRISPR vs gene expression induced dependency pipeline (SynLeGG)

Description

Run the MultiSEp CRISPR vs gene expression induced dependency pipeline, optionally with tissue data. Minimally, a gene expression matrix and a CRISPR matrix must be provided.

Usage

mts_GeneDepID(exprsMatrix = exprsMatrix, crisprMatrix = crisprMatrix,
cores = cores, fcVal = fcVal, pVal = pVal, tissueMatrix = tissueMatrix)

Arguments

exprsMatrix	Log2 gene expression matrix in a gene by sample format where rownames are genes and the column names are samples.
crisprMatrix	Log2 CRISPR score matrix in a gene by sample format where rownames are genes and the column names are samples.
cores	The number of compute cores to use, defaults to 1.
fcVal	A log 2 fold change value filter, defaults to 0.1 (positive fold-change values are expected to reflect induced dependency relationships).
pVal	A p value filter, defaults to 0.1.
tissueMatrix	Matrix with two columns where column 1 is a sample ID and column 2 is a tissue ID.

Value

A list containing up to 2 tables:

CRISPR_Results	Cluster assignment table output from the mts_InducedDependency function.
CRISPR_TS_Results	Output from the mts_CrisprTS function (generated if a tissueMatrix is supplied).

Examples

data(depMapCRISPRscores_subset)
data(depMapXPR_subset)
data(depMapTissue_subset)
mtsGeneDep_ID_Results <- mts_GeneDepID(exprsMatrix=depMapXPR_subset[1:3,], 
crisprMatrix=depMapCRISPRscores_subset[1:10,], cores=1, fcVal=0.1, pVal=0.25,
tissueMatrix=depMapTissue_subset)

MultiSEp documentation built on Aug. 27, 2026, 5:07 p.m.