Journal article

How data analysis affects power, reproducibility and biological insight of RNA-seq studies in complex datasets

Lucia Peixoto, Davide Risso, Shane G Poplawski, Mathieu E Wimmer, Terence P Speed, Marcelo A Wood, Ted Abel

NUCLEIC ACIDS RESEARCH | OXFORD UNIV PRESS | Published : 2015

Abstract

The sequencing of the full transcriptome (RNA-seq) has become the preferred choice for the measurement of genome-wide gene expression. Despite its widespread use, challenges remain in RNA-seq data analysis. One often-overlooked aspect is normalization. Despite the fact that a variety of factors or 'batch effects' can contribute unwanted variation to the data, commonly used RNA-seq normalization methods only correct for sequencing depth. The study of gene expression is particularly problematic when it is influenced simultaneously by a variety of biological factors in addition to the one of interest. Using examples from experimental neuroscience, we show that batch effects can dominate the sig..

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University of Melbourne Researchers

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Awarded by NRSA


Awarded by DARPA


Awarded by NIH



Awarded by NATIONAL HEART, LUNG, AND BLOOD INSTITUTE


Awarded by NATIONAL INSTITUTE OF GENERAL MEDICAL SCIENCES


Awarded by NATIONAL INSTITUTE OF MENTAL HEALTH


Awarded by NATIONAL INSTITUTE OF NEUROLOGICAL DISORDERS AND STROKE


Awarded by NATIONAL INSTITUTE ON DRUG ABUSE


Funding Acknowledgements

NRSA training [T32NS007413 to L.P., M.R., P.I. and T32HL007953 to M.E.W., A.I.P., P.I.]; Brush Family Professorship to T.A.; DARPA 58077 LSDRP (S.Bhatnagar, PI) and R01MH087463 to T.A. DA036984 and R01MH101491 to M.A.W. Funding for open access charge: [NIH R01MH087463 to T.A.].