Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses.
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.
Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia.
Ruijie
R
Liu
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.
Aliaksei Z
AZ
Holik
Molecular Medicine Division, The Walter and Eliza Hall Institute of Medical Research, 1G Royal Parade, Parkville, Victoria 3052, Australia.
Shian
S
Su
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.
Natasha
N
Jansz
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.
Kelan
K
Chen
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.
Huei San
HS
Leong
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.
Marnie E
ME
Blewitt
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.
Marie-Liesse
ML
Asselin-Labat
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.
Gordon K
GK
Smyth
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.
Matthew E
ME
Ritchie
D000818:Animals; D045744:Cell Line, Tumor; D002868:Chromosomal Proteins, Non-Histone / Q000235:genetics; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D016014:Linear Models; D051379:Mice; D015203:Reproducibility of Results; D017423:Sequence Analysis, RNA / Q000379:methods*
D002868:Chromosomal Proteins, Non-Histone; C529024:SmcHD1 protein, mouse