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.
Gene length bias in RNA-seq data
The LNCaP data set shows the probability of significant p-values (p<0.05) increases as gene length. Group 1 is the genes of shortest length and Group 10 is the genes with longest length
Researchers at SUNY Buffalo have developed 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. Their 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, the researchers implemented a flexible weighted sampling scheme in the restandardization step of their algorithm. They show their method improves the power of identifying significant gene sets that are affected by the length bias. They also show that the method maintains the type I error comparing with another representative method for gene set enrichment test.
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.
Availability – SeqGSA can be accessed at https://github.com/xingrenub/SeqGSA.