View source: R/prep.lsn.type.matrix.R
| prep.lsn.type.matrix | R Documentation |
Constructs a lesion type matrix summarizing the genomic lesion type(s) affecting each gene across patients. Each row represents a gene, and each column represents a patient.
prep.lsn.type.matrix(ov.data,
min.ngrp = 0)
ov.data |
GRIN gene-lesion overlap results, typically the output from
the |
min.ngrp |
Optional integer specifying the minimum number of patients
affected by any lesion type in a given gene for that gene to be retained in
the output matrix. The default is |
The function uses the gene-lesion overlaps in ov.data$gene.lsn.hits to
construct a character matrix with genes as rows and patients as columns.
For each gene-patient combination:
"none" indicates that no lesion overlaps the gene in that patient.
A specific lesion type indicates that only one distinct lesion type affects the gene in that patient.
"multiple" indicates that more than one distinct lesion type
affects the same gene in that patient.
Multiple lesions of the same type affecting the same gene in a patient are
represented by that lesion type rather than "multiple".
When min.ngrp > 0, genes affected by any lesion type in fewer than
min.ngrp patients are excluded from the final matrix.
A character matrix in which:
Rows represent genes.
Columns represent patient IDs.
Entries are "none", a specific lesion type, or "multiple".
Abdelrahman Elsayed abdelrahman.elsayed@stjude.org and Stanley Pounds stanley.pounds@stjude.org
Cao, X., Elsayed, A. H., & Pounds, S. B. (2023). Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-omics.
prep.gene.lsn.data,
find.gene.lsn.overlaps,
prep.binary.lsn.mtx
data(lesion_data)
data(hg38_gene_annotation)
# 1) Prepare gene and lesion data
prep.gene.lsn <- prep.gene.lsn.data(lesion_data,
hg38_gene_annotation)
# 2) Identify gene-lesion overlaps
gene.lsn.overlap <- find.gene.lsn.overlaps(prep.gene.lsn)
# 3) Create lesion type matrix for genes affected in at least 5 patients
lsn.type.mtx <- prep.lsn.type.matrix(gene.lsn.overlap,
min.ngrp = 5)
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