Length bias correction for RNA-seq data in gene set analyses

• Published in: Bioinformatics (Oxford, England) England), 2011-03, Vol.27 (5), p.662-669
• Main Authors: Gao, Liyan, Fang, Zhide, Zhang, Kui, Zhi, Degui, Cui, Xiangqin
• Format: Article
• Language: English
• Subjects: Adjustment; Bias; Bioinformatics; Biological and medical sciences; Computer Simulation; Fundamental and applied biological sciences. Psychology; Gene Expression Profiling - methods; General aspects; Genes; Humans; Mathematical models; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Oligonucleotide Array Sequence Analysis; Original Papers; RNA - analysis; Roots; Scripts; Sequence Analysis, RNA - methods; Statistics
• ISSN: 1367-4803; 1367-4811
• DOI: 10.1093/bioinformatics/btr005

Next-generation sequencing technologies are being rapidly applied to quantifying transcripts (RNA-seq). However, due to the unique properties of the RNA-seq data, the differential expression of longer transcripts is more likely to be identified than that of shorter transcripts with the same effect size. This bias complicates the downstream gene set analysis (GSA) because the methods for GSA previously developed for microarray data are based on the assumption that genes with same effect size have equal probability (power) to be identified as significantly differentially expressed. Since transcript length is not related to gene expression, adjusting for such length dependency in GSA becomes necessary. In this article, we proposed two approaches for transcript-length adjustment for analyses based on Poisson models: (i) At individual gene level, we adjusted each gene's test statistic using the square root of transcript length followed by testing for gene set using the Wilcoxon rank-sum test. (ii) At gene set level, we adjusted the null distribution for the Fisher's exact test by weighting the identification probability of each gene using the square root of its transcript length. We evaluated these two approaches using simulations and a real dataset, and showed that these methods can effectively reduce the transcript-length biases. The top-ranked GO terms obtained from the proposed adjustments show more overlaps with the microarray results. R scripts are at http://www.soph.uab.edu/Statgenetics/People/XCui/r-codes/.