mts_Mutation: Integrate gene cluster assignments with mutation data

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

Integrate gene cluster assignments with mutation data

Description

MultiSEp detects mutations that are enriched in gene clusters (for example, from mRNA expression); providing insight into the genetic background relevant to candidate 'Achilles Heel' relationships suggested by analysis of the CRISPR screen and transcriptome data. A chi-squared test is performed for each gene to derive p-values for the enrichment of mutation classes across the gene expression clusters on a per-tissue basis.

Usage

mts_Mutation(resultList = resultList, mutMatrix = mutMatrix, tissueMatrix = tissueMatrix, 
cores = cores, pVal = pVal)

Arguments

resultList	A result list object produced by the mts_mixModelCluster function.
mutMatrix	A mutation score matrix in gene by sample format, where row names correspond to genes and column names correspond to sample names. The value should be either 'WT' or a mutation detail (e.g. amino acid change or coding sequence change).
tissueMatrix	A data frame containing 2 columns where column 1 is the sample id and column 2 is the tissue id.
pVal	A p-value filter, the default is 0.01.
cores	The number of compute cores to use, the default is 1.

Value

A data frame containing the results of MultiSEp analysis of mutations by gene expression clusters, with the following columns:

mrna_gene	Identifier for the genes or other molecular species provided in the resultList cluster object
mutation_gene	Identifier for the genes with mutation data
tissue	Description of the tissue evaluated
chiSqPvalue	p-value from chi-squared test, assessing the distribution of mutation classes across the clusters
mutation_per_mode	Total number of mutated samples in the clusters (modes) for the given mutation_gene.

See Also

mts_mixModelCluster

Examples

data(depMapXPR_subset)
data(depMapMUT_subset)
data(depMapTissue_subset)
mixModelClusters <- mts_mixModelCluster(dataMatrix = depMapXPR_subset[1,])

multisepMut <- mts_Mutation(
  resultList = mixModelClusters[1],
  mutMatrix = depMapMUT_subset, 
  tissueMatrix = depMapTissue_subset,
  pVal = 0.01
)

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