While RNAseq provides more abundant information than microarray, its analysis has proved considerably more complicated than that of microarray analysis. Several methods have been proposed, related software packages have been developed, and studies have been conducted to evaluate these packages. But to date, no consensus has been reached on the best approach for RNAseq-based differential expression analysis. Not surprisingly, different studies have drawn different conclusions as to the best approach to identify differentially expressed genes based upon their own criteria and scenarios considered. Furthermore, the lack of effective quality control may lead to misleading results interpretation and erroneous conclusions. To solve these aforementioned problems, we propose a simple yet safe and practical rank-sum approach for RNAseq-based differential gene expression analysis named MultiRankSeq. MultiRankSeq compares the study groups using several of the most commonly applied analytical methods and combines their results to generate a new rank-sum interpretation. MultiRankSeq provides a unique analysis approach to RNAseq differential expression analysis. MultiRankSeq is written in R, and it is easily applicable. Detailed graphical and tabular analysis reports can be generated with a single command line.


MultiRankSeq can be downloaded from github.


Guo Y, Zhao SL, Ye F, Sheng QH, Shyr Y: MultiRankSeq: Multiperspective Approach for RNAseq Differential Expression Analysis and Quality Control. Biomed Research International 2014.