Name
Abstract | ||
|---|---|---|
Gene set testing has become an indispensable tool for the analysis of high-dimensional genomic data. An important motivation for testing gene sets, rather than individual genomic variables, is to improve statistical power by reducing the number of tested hypotheses. Given the dramatic growth in common gene set collections, however, testing is often performed with nearly as many gene sets as underlying genomic variables. To address the challenge to statistical power posed by large gene set collections, we have developed spectral gene set ltering (SGSF), a novel technique for independent ltering of gene set collections prior to gene set testing. The SGSF method uses as a lter statistic the p-value measuring the statistical signicance of the association between each gene set and the sample principal components (PCs), taking into account the signicance of the associated eigenvalues. Because this lter statistic is independent of standard gene set test statistics under the null hypothesis but dependent under the alternative, the proportion of enriched gene sets is increased without impacting the type I error rate. As shown using simulated and real gene expression data, the SGSF algorithm accurately lters gene sets unrelated to the experimental outcome resulting in signicantly increased gene set testing powe |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1109/TCBB.2015.2415815 | IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB) |
Keywords | Field | DocType |
gene set testing,tracy-widom,gene set enrichment,principal component analysis,random matrix theory,screening-testing,computational biology,testing,gene expression,genomics,bioinformatics | Data mining,Null hypothesis,Computer science,Artificial intelligence,Type I and type II errors,Statistical power,Statistical hypothesis testing,Statistic,Filter (signal processing),Bioinformatics,Machine learning,Principal component analysis,Gene expression profiling | Journal |
Volume | Issue | ISSN |
PP | 99 | 1545-5963 |
Citations | PageRank | References |
1 | 0.35 | 15 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| H. Robert Frost | 1 | 67 | 7.54 |
| Zhigang Li | 2 | 34 | 11.35 |
| Folkert W. Asselbergs | 3 | 121 | 9.36 |
| Jason H. Moore | 4 | 1223 | 159.43 |