prep.gene.lsn.data {GRIN2} | R Documentation |
Prepare Gene and Lesion Data for GRIN Analysis
Description
Prepares and indexes gene and lesion data for downstream GRIN (Genomic Random Interval) analysis. This function merges and orders gene and lesion coordinates to support efficient computation of overlaps between genes and all different types of genomic lesions (structural or sequence lesions).
Usage
prep.gene.lsn.data(lsn.data, gene.data, mess.freq = 10)
Arguments
lsn.data |
A
|
gene.data |
A
|
mess.freq |
Integer specifying the frequency at which progress messages are displayed. Messages are printed every |
Details
This function performs pre-processing by ordering and indexing both gene and lesion data. It combines gene and lesion coordinates into a unified structure, marking each with a specific code (cty
) that identifies whether the row represents a gene or lesion. This merged data is then used in the find.gene.lsn.overlaps()
function to detect gene-lesion overlaps.
Value
A list with the following components:
- lsn.data
Original lesion data.
- gene.data
Original gene annotation data.
- gene.lsn.data
Combined and ordered data.frame of gene and lesion intervals. The
cty
column encodes position type: 1 = gene start, 2 = lesion start, 3 = lesion end, 4 = gene end.- gene.index
Index data.frame indicating the start and end rows for each chromosome within
gene.lsn.data
for genes.- lsn.index
Index data.frame indicating the start and end rows for each lesion (grouped by type, chromosome, and subject) within
gene.lsn.data
.
Author(s)
Abdelrahman Elsayed abdelrahman.elsayed@stjude.org and Stanley Pounds stanley.pounds@stjude.org
References
Pounds, S., et al. (2013). A genomic random interval model for statistical analysis of genomic lesion data. Cao, X., Elsayed, A. H., & Pounds, S. B. (2023). Statistical Methods Inspired by Challenges in Pediatric Cancer Multi-omics.
See Also
order.index.gene.data
, order.index.lsn.data
, find.gene.lsn.overlaps
Examples
data(lesion_data)
data(hg38_gene_annotation)
# Prepare gene and lesion data for GRIN analysis:
prep.gene.lsn <- prep.gene.lsn.data(lesion_data, hg38_gene_annotation)