Gene set analysis controlling for length bias in RNA-seq experiments.
BACKGROUND
In gene set analysis, the researchers are interested in determining the gene sets that are significantly correlated with an outcome, e.g. disease status or treatment. With the rapid development of high throughput sequencing technologies, Ribonucleic acid sequencing (RNA-seq) has become an important alternative to traditional expression arrays in gene expression studies. Challenges exist in adopting the existent algorithms to RNA-seq data given the intrinsic difference of the technologies and data. In RNA-seq experiments, the measure of gene expression is correlated with gene length. This inherent correlation may cause bias in gene set analysis.


RESULTS
We develop SeqGSA, a new method for gene set analysis with length bias adjustment for RNA-seq data. It extends from the R package GSA designed for microarrays. Our method compares the gene set maxmean statistic against permutations, while also taking into account of the statistics of the other gene sets. To adjust for the gene length bias, we implement a flexible weighted sampling scheme in the restandardization step of our algorithm. We show our method improves the power of identifying significant gene sets that are affected by the length bias. We also show that our method maintains the type I error comparing with another representative method for gene set enrichment test.


CONCLUSIONS
SeqGSA is a promising tool for testing significant gene pathways with RNA-seq data while adjusting for inherent gene length effect. It enhances the power to detect gene sets affected by the bias and maintains type I error under various situations.
Department of Biostatistics, SUNY University at Buffalo, Buffalo, 14214 USA.
Xing
X
Ren
Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, 14263 USA.
Qiang
Q
Hu
Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, 14263 USA.
Song
S
Liu
Department of Biostatistics and Bioinformatics, Roswell Park Cancer Institute, Buffalo, 14263 USA.
Jianmin
J
Wang
Department of Biostatistics, SUNY University at Buffalo, Buffalo, 14214 USA.
Jeffrey C
JC
Miecznikowski
Gene length bias; Gene set analysis; Maxmean statistic; RNA-seq; Restandardization