Change-point analysis of paired allele-specific copy number variation data.
The recent genome-wide allele-specific copy number variation data enable us to explore two types of genomic information including chromosomal genotype variations as well as DNA copy number variations. For a cancer study, it is common to collect data for paired normal and tumor samples. Then, two types of paired data can be obtained to study a disease subject. However, there is a lack of methods for a simultaneous analysis of these four sequences of data. In this study, we propose a statistical framework based on the change-point analysis approach. The validity and usefulness of our proposed statistical framework are demonstrated through the simulation studies and applications based on an experimental data set.
Department of Statistics and Biostatistics Center, The George Washington University, Washington, DC, USA.
Yinglei
Y
Lai
D000483:Alleles; D045744:Cell Line, Tumor; D002478:Cells, Cultured; D002890:Chromosomes, Human, Pair 20 / Q000235:genetics*; D002891:Chromosomes, Human, Pair 21 / Q000235:genetics*; D003198:Computer Simulation; D056915:DNA Copy Number Variations; D004273:DNA, Neoplasm; D015894:Genome, Human; D023281:Genomics / Q000379:methods; D005838:Genotype; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide
D004273:DNA, Neoplasm