grin.lsn.boundaries: GRIN Evaluation of Lesion Boundaries

In GRIN2: Genomic Random Interval (GRIN)

GRIN Evaluation of Lesion Boundaries

Description

This function evaluates copy number variations (CNVs), specifically gains and deletions, using unique lesion start and end positions to define genomic boundaries. The analysis is lesion-type specific and spans the entire genome.

Usage

grin.lsn.boundaries(lsn.data, chrom.size)

Arguments

lsn.data	A lesion data table containing only gain or deletion events. If gains are subdivided (e.g., into gains and amplifications based on log2Ratio values), both subtypes can be included. The same applies for deletions (e.g., homozygous and heterozygous).
chrom.size	A chromosome size table with two required columns: "chrom" (chromosome identifier) and "size" (chromosome size in base pairs).

Details

The function identifies unique CNV boundaries by evaluating the start and end positions of lesions on each chromosome. Large lesions may be split into smaller boundaries based on overlapping smaller lesions in other samples. This boundary-based approach ensures comprehensive genome-wide coverage, including intergenic regions and areas without annotated features.

The first boundary on each chromosome spans from the start of the chromosome to the start of the first lesion affecting any of the included patient samples. Similarly, the last boundary extends from the end of the last lesion to the end of the chromosome.

This method is particularly useful when analyzing CNV data at a finer resolution than gene-level annotation allows.

Value

A data.frame with five columns:

gene	Boundary identifier, based on unique start and end positions.
chrom	Chromosome on which the boundary resides.
loc.start	Start position of the boundary.
loc.end	End position of the boundary.
diff	Length of the boundary in base pairs.

Author(s)

Abdelrahman Elsayed [e-mail] and Stanley Pounds [e-mail]

See Also

grin.stats

Examples

data(lesion_data)
data(hg38_chrom_size)

# This analysis is lesion-type specific. For example, extract gains:
gain <- lesion_data[lesion_data$lsn.type == "gain", ]

# Generate lesion boundaries for gains:
lsn.bound.gain <- grin.lsn.boundaries(gain, hg38_chrom_size)

# Run GRIN using lesion boundaries as markers instead of gene annotations:
GRIN.results.gain.bound <- grin.stats(gain, lsn.bound.gain, hg38_chrom_size)

# The same analysis can be applied to deletions, mutations, or structural rearrangements.

GRIN2 documentation built on June 17, 2025, 9:11 a.m.