MultiRankSeq: Multiperspective approach for RNAseq differential expression analysis and quality control

Yan Guo, Shilin Zhao, Fei Ye, Quanhu Sheng, Yu Shyr

Research output: Contribution to journalArticlepeer-review

36 Citations (Scopus)

Abstract

Background. After a decade of microarray technology dominating the field of high-throughput gene expression profiling, the introduction of RNAseq has revolutionized gene expression research. While RNAseq provides more abundant information than microarray, its analysis has proved considerably more complicated. 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 first performs quality control assessment. For data meeting the quality control criteria, 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.

Original languageEnglish
Article number248090
JournalBioMed research international
Volume2014
DOIs
Publication statusPublished - 2014

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)
  • Immunology and Microbiology(all)

Fingerprint Dive into the research topics of 'MultiRankSeq: Multiperspective approach for RNAseq differential expression analysis and quality control'. Together they form a unique fingerprint.

Cite this