Analysis of array CGH data for cancer studies using fused quantile regression.
Bioinformatics
; 23(18): 2470-6, 2007 Sep 15.
Article
in En
| MEDLINE
| ID: mdl-17644559
ABSTRACT
MOTIVATION The identification of DNA copy number changes provides insights that may advance our understanding of initiation and progression of cancer. Array-based comparative genomic hybridization (array-CGH) has emerged as a technique allowing high-throughput genome-wide scanning for chromosomal aberrations. A number of statistical methods have been proposed for the analysis of array-CGH data. In this article, we consider a fused quantile regression model based on three motivations (1) quantile regression may provide a more comprehensive picture for the ratio profile of copy numbers than the standard mean regression approach; (2) for simplicity, most available methods assume uniform spacing between neighboring clones, while incorporating the information of physical locations of clones may be helpful and (3) most current methods have a set of tuning parameters that must be carefully tuned, which introduces complexity to the implementation. RESULTS:
We formulate the detection of regions of gains and losses in a fused regularized quantile regression framework, incorporating physical locations of clones. We derive an efficient algorithm that computes the entire solution path for the resulting optimization problem, and we propose a simple estimate for the complexity of the fitted model, which leads to convenient selection of the tuning parameter. Three published array-CGH datasets are used to demonstrate our approach.AVAILABILITY:
R code are available at http//www.stat.lsa.umich.edu/~jizhu/code/cgh/. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Collection:
01-internacional
Database:
MEDLINE
Main subject:
Sequence Alignment
/
Chromosome Mapping
/
Sequence Analysis, DNA
/
Gene Dosage
/
Oligonucleotide Array Sequence Analysis
/
Neoplasms
Type of study:
Diagnostic_studies
/
Prognostic_studies
Limits:
Humans
Language:
En
Journal:
Bioinformatics
Journal subject:
INFORMATICA MEDICA
Year:
2007
Document type:
Article
Affiliation country:
Estados Unidos