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Nucleic Acids Res. 2015 Sep 3;43(15):e97. doi: 10.1093/nar/gkv412. Epub 2015 Apr 29.

Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses.

Author information

1
Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia.
2
Stem Cells and Cancer Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia Department of Medical Biology, The University of Melbourne, Parkville, Victoria 3010, Australia.
3
Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia Department of Medical Biology, The University of Melbourne, Parkville, Victoria 3010, Australia.
4
Bioinformatics Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia.
5
Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia Department of Medical Biology, The University of Melbourne, Parkville, Victoria 3010, Australia School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria 3010, Australia mritchie@wehi.edu.au.

Abstract

Variations in sample quality are frequently encountered in small RNA-sequencing experiments, and pose a major challenge in a differential expression analysis. Removal of high variation samples reduces noise, but at a cost of reducing power, thus limiting our ability to detect biologically meaningful changes. Similarly, retaining these samples in the analysis may not reveal any statistically significant changes due to the higher noise level. A compromise is to use all available data, but to down-weight the observations from more variable samples. We describe a statistical approach that facilitates this by modelling heterogeneity at both the sample and observational levels as part of the differential expression analysis. At the sample level this is achieved by fitting a log-linear variance model that includes common sample-specific or group-specific parameters that are shared between genes. The estimated sample variance factors are then converted to weights and combined with observational level weights obtained from the mean-variance relationship of the log-counts-per-million using 'voom'. A comprehensive analysis involving both simulations and experimental RNA-sequencing data demonstrates that this strategy leads to a universally more powerful analysis and fewer false discoveries when compared to conventional approaches. This methodology has wide application and is implemented in the open-source 'limma' package.

PMID:
25925576
PMCID:
PMC4551905
DOI:
10.1093/nar/gkv412
[Indexed for MEDLINE]
Free PMC Article

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