article Open AccessTop 1% cited
A comparison of methods for differential expression analysis of RNA-seq data
BMC Bioinformatics · 2013 · Vol. 14(1) · pp. 91–91
Charlotte Soneson✉(SIB Swiss Institute of Bioinformatics)Mauro Delorenzi(University Hospital of Lausanne)
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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