grin.lsn.boundaries: GRIN Evaluation of Lesion Boundaries

In GRIN2: Genomic Random Interval (GRIN)

GRIN Evaluation of Lesion Boundaries

Description

Generates genome-wide genomic boundaries defined by the unique start and end positions of interval-based lesions, such as copy-number gains or deletions, for use as marker regions in GRIN analyses.

Usage

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

Arguments

lsn.data	A lesion data table containing interval-based lesions, typically copy-number gains or deletions. The data may include related subtypes analyzed together, such as gains and amplifications or heterozygous and homozygous deletions.
chrom.size	A chromosome size table with two required columns: "chrom" containing chromosome identifiers and "size" containing chromosome sizes in base pairs.

Details

The function partitions each chromosome into non-overlapping genomic boundaries using all unique lesion start and end positions observed in the supplied lesion data. A new boundary begins at each lesion start position and immediately after each lesion end position.

As a result, large lesions may be divided into multiple smaller boundaries when other lesions begin or end within the same genomic region. This allows GRIN to evaluate recurrent lesion patterns at a finer resolution than the original lesion intervals.

Boundaries span the entire chromosome, including regions outside observed lesions. The first boundary begins at position 1, and the final boundary extends to the end of the chromosome.

This approach is particularly useful for copy-number variation analyses in which recurrent genomic regions are evaluated independently of existing gene or feature annotations.

Value

A data.frame with five columns:

gene

Unique boundary identifier constructed from chromosome, start position, and end position.

chrom

Chromosome containing the boundary.

loc.start

Start position of the boundary in base pairs.

loc.end

End position of the boundary in base pairs.

diff

Length of the boundary in base pairs.

Author(s)

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

References

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

See Also

grin.stats, genomewide.log10q.plot

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 genome-wide 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 approach can be applied to deletions or related
# copy-number subtypes analyzed together.

GRIN2 documentation built on Aug. 22, 2026, 5:09 p.m.