prep.binary.lsn.mtx: Prepare Binary Lesion Matrix

In GRIN2: Genomic Random Interval (GRIN)

Prepare Binary Lesion Matrix

Description

Prepares a lesion matrix with each gene affected by a certain type of lesion as a row and each patient as a column.

Usage

prep.binary.lsn.mtx(ov.data, min.ngrp = 0)

Arguments

ov.data	list of six data.frames that represent the output results of the find.gene.lsn.overlaps function.
min.ngrp	if specified, rows with number of patients affected by a specific type of lesion that's less than the specified number will be discarded (default is 0; function will return all genes affected by a lesion in at least one patient), for example if only one patient is affected by gain in MYB gene.

Details

The function uses the output results of the find.gene.lsn.overlaps function and create a binary lesion matrix with each gene affected by certain lesion type as a row and each patient as a column. Rownames are labelled as gene.ID_lesion.type (for example: ENSG00000118513_gain for gains affecting MYB gene). The entry for each patient in the table will be denoted as 1 if the patient is affected by this specific type of lesion in the gene, for example gain in MYB gene (ENSG00000118513) or 0 otherwise.

Value

The function returns a binary lesion matrix with each row labelled as gene.ID_lesion.type and each column is a patient. Entry for each patient in the table will be denoted as 1 if the gene is affected by this specific type of lesion or 0 otherwise.

Author(s)

Stanley Pounds [e-mail]

References

Pounds, Stan, et al. (2013) A genomic random interval model for statistical analysis of genomic lesion data.

Cao, X., Elsayed, A. H., & Pounds, S. B. (2023). Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-omics.

See Also

prep.gene.lsn.data(), find.gene.lsn.overlaps()

Examples

data(lesion.data)
data(hg19.gene.annotation)

# prepare gene and lesion data for later computations:
prep.gene.lsn=prep.gene.lsn.data(lesion.data,
                                 hg19.gene.annotation)

# determine lesions that overlap each gene (locus):
gene.lsn.overlap=find.gene.lsn.overlaps(prep.gene.lsn)

# prepare the lesion binary matrix with a minimum of 5 patients affected by the lesion to be
# included in the final matrix:
lsn.binary.mtx=prep.binary.lsn.mtx(gene.lsn.overlap, min.ngrp=5)

GRIN2 documentation built on April 4, 2025, 1:41 a.m.