Statistical strategies for microRNAseq batch effect reduction.
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ABSTRACT:
SUBMITTER: Guo Y
PROVIDER: S-EPMC4171948 | biostudies-literature | 2014 Jun
REPOSITORIES: biostudies-literature
Publications
Statistical strategies for microRNAseq batch effect reduction.
Guo Yan Y Zhao Shilin S Su Pei-Fang PF Li Chung-I CI Ye Fei F Flynn Charles R CR Shyr Yu Y
Translational cancer research 20140601 3
RNAseq technology is replacing microarray technology as the tool of choice for gene expression profiling. While providing much richer data than microarray, analysis of RNAseq data has been much more challenging. Among the many difficulties of RNAseq analysis, correctly adjusting for batch effect is a pivotal one for large-scale RNAseq based studies. The batch effect of RNAseq data is most obvious in microRNA (miRNA) sequencing studies. Using real miRNA sequencing (miRNAseq) data, we evaluated se ...[more]
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