mts_crisprPartition: Partition CRISPR gene effect scores

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

Partition CRISPR gene effect scores

Description

Partitions the CRISPR gene effect scores using the supplied threshold. For example a score of -0.5 is the default threshold, which aims to partition samples into 'lethal' (representative of cell death or stasis) and 'non-lethal' groups.

Usage

mts_crisprPartition(
  dataMatrix, 
  NumSampleThreshold = 20,
  partitionThreshold=-0.5,
  verbose=FALSE,
  cores)

Arguments

dataMatrix	CRISPR score matrix in gene by sample format, where row names correspond to genes and column names correspond to sample names.
NumSampleThreshold	The minimum number of samples in which a gene must have a CRISPR score for it to be retained; genes with scores in more than NumSampleThreshold samples are kept. Defaults to 20.
partitionThreshold	The score value used to split data into two partitions, default is -0.5.
verbose	Controls the production of reporting messages, default is FALSE (i.e. no verbose messages).
cores	The number of compute cores to use, default is 1.

Value

A list object for each gene in the dataMatrix object

Samples	Sample identifiers.
Values	The analysed value for each sample, for example the gene effect chronos score.
Cluster_Assignment	The assigned cluster value for each sample.

See Also

mts_patternDetection

Examples

data("depMapCRISPRscores_subset")
crisprClusters_thresholded <- mts_crisprPartition(
  dataMatrix=depMapCRISPRscores_subset[1:5,], cores=1
)

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