CNGPLD: Case-Control Copy-Number Analysis Using Gaussian Process Latent Difference

Authors

David J H Shih
Ruoxing Li
Peter Müller
W Jim Zheng
Kim-Anh Do
Shiaw-Yih Lin
Scott L Carter

Publication Date

4-12-2022

Journal

Bioinformatics

Abstract

MOTIVATION: Cross-sectional analyses of primary cancer genomes have identified regions of recurrent somatic copy-number alteration, many of which result from positive selection during cancer formation and contain driver genes. However, no effective approach exists for identifying genomic loci under significantly different degrees of selection in cancers of different subtypes, anatomic sites or disease stages.

RESULTS: CNGPLD is a new tool for performing case-control somatic copy-number analysis that facilitates the discovery of differentially amplified or deleted copy-number aberrations in a case group of cancer compared with a control group of cancer. This tool uses a Gaussian process statistical framework in order to account for the covariance structure of copy-number data along genomic coordinates and to control the false discovery rate at the region level.

AVAILABILITY AND IMPLEMENTATION: CNGPLD is freely available at https://bitbucket.org/djhshih/cngpld as an R package.

SUPPLEMENTARY INFORMATION: CNGPLD is freely available at https://bitbucket.org/djhshih/cngpld as an R package.

Keywords

Humans, Cross-Sectional Studies, Genome, Genomics, DNA Copy Number Variations, Neoplasms, Case-Control Studies, Software

DOI

10.1093/bioinformatics/btac096

PMID

35176131

PMCID

PMC9004638

PubMedCentral® Posted Date

2-17-2022

PubMedCentral® Full Text Version

Post-print

Published Open-Access

yes