mts_GeneDepCrispr: CRISPR and gene expression gene-dependency prediction...

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

CRISPR and gene expression gene-dependency prediction (SynLeGG)

Description

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

Usage

mts_GeneDepCrispr(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, default is 1.
fcVal	A log 2 fold change value filter, default is -0.1. Running with negative fold-change values (<0) allows prediction of synthetic lethal relationships, specifying positive fold-change values (>0) corresponds to 'induced dependency' relationships.
pVal	A p value filter, default is 0.1.
tissueMatrix	Matrix of two columns where column 1 is a sample ID and column 2 is a tissue ID.

Value

A list containing up to two tables:

CRISPR_Results	Cluster assignment table output from the mts_Crispr 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)
exprsSub  <- depMapXPR_subset["PCYT1B",]
crisprSub <- depMapCRISPRscores_subset["PCYT1A",]
mts_GeneDepCrispr(
exprsMatrix = exprsSub, crisprMatrix = crisprSub,
cores = 1, fcVal = -0.5, pVal = 0.25, tissueMatrix = depMapTissue_subset)

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