Why weight? Modelling sample and observational level variability improves power in RNA-seq analyses
Walter and Eliza Hall Institute of Medical Research · University of Melbourne
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…
Citation impact
- FWCI
- 9.49
- Percentile
- 100%
- References
- 36
Authors
10- RLRuijie LiuCorresponding
Walter and Eliza Hall Institute of Medical Research
- AZAliaksei Z. Holik
Walter and Eliza Hall Institute of Medical Research, University of Melbourne
- SSShian Su
Walter and Eliza Hall Institute of Medical Research
- NJNatasha Jansz
Walter and Eliza Hall Institute of Medical Research, University of Melbourne
- KCKelan Chen
University of Melbourne, Walter and Eliza Hall Institute of Medical Research
Topics & keywords
- Biology
- Observational study
- RNA-Seq
- Sample (material)
- Genetics
- Computational biology
- Statistics
- Transcriptome