GRIN2 (version 2.0.0)

genomewide.lsn.plot: Genome-wide Lesion Plot

Description

Generates a genome-wide lesion plot displaying all lesion types affecting different chromosomes.

Usage

genomewide.lsn.plot(
  grin.res,
  ordered = FALSE,
  pt.order = NULL,
  lsn.colors = NULL,
  max.log10q = NULL
)

Value

A genome-wide lesion plot consisting of three aligned panels:

• Middle panel: genome-wide lesion map across all chromosomes and patients.

• Left panel: -log10(q-values) of each locus from GRIN results showing Statistical Significance of Lesion Frequencies.

• Right panel: number of affected patients at each locus, colored by lesion category.

Arguments

grin.res

GRIN results (output from the grin.stats function).

ordered

Logical; if TRUE, patient IDs will be reordered according to the pt.order data frame. If FALSE (default), patient IDs are ordered alphabetically.

pt.order

A data frame with two columns: "ID" (patient identifiers matching those in the lesion data) and "pts.order" (numeric vector specifying the new patient order from 1 to n patients). Only required if ordered = TRUE.

lsn.colors

A named vector of colors assigned to lesion types. If not provided, colors will be automatically assigned using the default.grin.colors function.

max.log10q

Numeric; maximum value for -log10(q-value) used in the plot. Any value greater than max.log10q will be capped at this value in the left panel of the plot.

Author

Abdelrahman Elsayed and Stanley Pounds

Details

This function uses genome-wide coordinates (from compute.gw.coordinates) to generate a three-panel plot. The middle panel shows lesions by chromosome across patients. The left panel displays the -log10(q-values) from the GRIN results for each gene, and the right panel shows the number of patients affected at each locus, color-coded by lesion type.

References

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

See Also

compute.gw.coordinates

Examples

data(lesion_data)
data(hg38_gene_annotation)
data(hg38_chrom_size)

# Run GRIN analysis
grin.results <- grin.stats(lesion_data,
                           hg38_gene_annotation,
                           hg38_chrom_size)

# Generate genome-wide lesion plot with alphabetical patient ordering
genomewide.plot <- genomewide.lsn.plot(grin.results, max.log10q = 50)