GRIN2 (version 2.0.0)

compute.gw.coordinates: Compute Genome-wide Plotting Coordinates

Description

Computes and assigns genome-wide plotting coordinates to lesion, gene, and chromosome data for use in genome-wide lesion plots.

Usage

compute.gw.coordinates(grin.res, scl = 1e+06)

Value

A list identical in structure to the original grin.res object, with the following additions:

gene.hits

Unchanged. GRIN gene-level summary statistics, including hit counts and p/q-values.

gene.lsn.data

Unchanged. Gene-lesion overlaps showing which lesion affects which gene for each patient.

lsn.data

Input lesion data with added x.start and x.end columns for genome-wide coordinates.

gene.data

Input gene annotation data with added x.start and x.end columns for genome-wide coordinates.

chr.size

Chromosome size table (22 autosomes + X and Y) with added x.start and x.end columns for plotting.

gene.index

Mapping of gene.lsn.data rows to their corresponding chromosomes.

lsn.index

Mapping of gene.lsn.data rows to their corresponding lesions.

Arguments

grin.res

GRIN results, typically the output of the grin.stats function.

scl

Chromosome unit length in base pairs. Default is 1,000,000, meaning each chromosome is divided into segments of 1 million base pairs for plotting.

Author

Abdelrahman Elsayed , Stanley Pounds

Details

This function processes the GRIN results to add genome-wide x-axis coordinates necessary for plotting lesions and genes across all chromosomes. It divides each chromosome into segments based on the specified scl value and computes cumulative start and end positions across chromosomes to ensure a continuous x-axis. Specifically:

• Chromosome sizes are updated to include x.start and x.end columns, where each chromosome starts where the previous one ends.

• Gene and lesion data are similarly updated with x.start and x.end coordinates, scaled by scl, and adjusted for cumulative chromosome positions.

References

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

See Also

grin.stats

Examples

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

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

# Assign genome-wide coordinates for plotting
genome.coord <- compute.gw.coordinates(grin.results)