This ensemble method use results from four current RNA-seq differential analysis methods (DESeq2, EBSeq, SAMSeq, NOISeq) and declare a gene is significantly differentially expressed only when the gene is detected significant by all four methods. The rank of selected genes is based on a weighted algorithm that assign more weights on genes detected by the DESeq2 and EBSeq methods than the SAMSeq and NOISeq methods. The ensemble method has much lower false discovery rate than all other popular RNA-Seq differential analysis methods especially when sample size is as small as three shown in our simulation studies. The ensemble method greatly increases the probability of true positive findings for studies with small sample sizes.
|Bioconductor views||ChIPSeq DifferentialExpression GeneExpression RNASeq SAGE Sequencing|
|Maintainer||Dongmei Li <Dongmei_Li@urmc.rochester.edu>|
|License||GPL (>= 2)|
|Package repository||View on GitHub|
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