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A comparison of methods for differential expression analysis of RNA-seq data

BMC Bioinformatics · 2013 · Vol. 14(1) · pp. 91–91
Charlotte SonesonMauro Delorenzi

Abstract

Very small sample sizes, which are still common in RNA-seq experiments, impose problems for all evaluated methods and any results obtained under such conditions should be interpreted with caution. For larger sample sizes, the methods combining a variance-stabilizing transformation with the 'limma' method for differential expression analysis perform well under many different conditions, as does the nonparametric SAMseq method.

Gene expression and cancer classificationMolecular Biology Techniques and ApplicationsCancer-related molecular mechanisms researchRNA-SeqDNA microarrayComputational biologyBiologyComputer scienceData miningTranscriptomeGeneticsGene expressionGene

MeSH terms

AnimalsMice, Inbred C57BLMice, Inbred DBARNA, MessengerSoftwareGenomeSequence Analysis, RNADNA, ComplementaryGene Expression ProfilingGenomicsMice
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