EDASeq: Exploratory Data Analysis and Normalization for RNA-Seq. Numerical and graphical summaries of RNA-Seq read data. Within-lane normalization procedures to adjust for GC-content effect (or other gene-level effects) on read counts: loess robust local regression, global-scaling, and full-quantile normalization (Risso et al., 2011). Between-lane normalization procedures to adjust for distributional differences between lanes (e.g., sequencing depth): global-scaling and full-quantile normalization (Bullard et al., 2010).
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References in zbMATH (referenced in 5 articles )
Showing results 1 to 5 of 5.
- Alnasir, Jamie J.; Shanahan, Hugh P.: Transcriptomics: quantifying non-uniform read distribution using MapReduce (2018)
- Martella, Francesca; Alfò, Marco: A finite mixture approach to joint clustering of individuals and multivariate discrete outcomes (2017)
- Cleynen, A.; Robin, S.: Comparing change-point location in independent series (2016)
- Cleynen, Alice; Dudoit, Sandrine; Robin, Stéphane: Comparing segmentation methods for genome annotation based on RNA-seq data (2014)
- Datta, Somnath (ed.); Nettleton, Dan (ed.): Statistical analysis of next generation sequencing data (2014)