Name
Abstract | ||
|---|---|---|
Motivation: DNA copy number gains and losses are commonly found in tumor tissue, and some of these aberrations play a role in tumor genesis and development. Although high resolution DNA copy number data can be obtained using array-based techniques, no single method is widely used to distinguish between recurrent and sporadic copy number aberrations. Results: Here we introduce Discovering Copy Number Aberrations Manifested In Cancer (DiNAMIC), a novel method for assessing the statistical significance of recurrent copy number aberrations. In contrast to competing procedures, the testing procedure underlying DiNAMIC is carefully motivated, and employs a novel cyclic permutation scheme. Extensive simulation studies show that DiNAMIC controls false positive discoveries in a variety of realistic scenarios. We use DiNAMIC to analyze two publicly available tumor datasets, and our results show that DiNAMIC detects multiple loci that have biological relevance. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1093/bioinformatics/btq717 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Computer science,Source code,Bioinformatics | Journal | 27 |
Issue | ISSN | Citations |
5 | 1367-4803 | 8 |
PageRank | References | Authors |
0.88 | 5 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vonn Walter | 1 | 8 | 0.88 |
| Andrew B Nobel | 2 | 254 | 21.11 |
| Fred A Wright | 3 | 52 | 5.42 |