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2014 ; 2014
(ä): 248090
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MultiRankSeq: multiperspective approach for RNAseq differential expression
analysis and quality control
#MMPMID24977143
Guo Y
; Zhao S
; Ye F
; Sheng Q
; Shyr Y
Biomed Res Int
2014[]; 2014
(ä): 248090
PMID24977143
show ga
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.