paper.md

In MonashBioinformaticsPlatform/varistran: Variance Stabilizing Transformations appropriate for RNA-Seq expression data

title: "Varistran: Anscombe's variance stabilizing transformation for RNA-seq gene expression data" tags: - RNA-seq - gene expression - variance stabilizing transformation - R package authors: - name: Paul Francis Harrison orcid: 0000-0002-3980-268X affiliation: 1 affiliations: - name: Monash Bioinformatics Platform, Monash University index: 1 date: 8 March 2017 bibliography: paper.bib

Summary

RNA-seq measures RNA expression levels in a biological sample using high-throughput cDNA sequencing, producing counts of the number of reads aligning to each gene. Noise in RNA-seq read count data is commonly modelled as following a negative binomial distribution, where the variance is a quadratic function of the expression level. However many statistical, machine learning, and visualization methods work best when the noise in a data set has equal variance. Varistran is an R package that uses Anscombe's [-@Anscombe1948] variance stabilizing transformation for the negative binomial distribution to transform RNA-seq count data, so that the noise has equal variance across all measured gene expression levels. The transformed data may be treated as log~2~ transformed gene expression levels, but with variability reduced at low read counts. Varistran also includes a function to open a Shiny report with simple diagnostic visualizations, including a plot to assess how effective the variance stabilization was, a biplot of samples and genes, and a heatmap. This allows defective samples, sample mislabling, and batch effects to be easily identified.

References

MonashBioinformaticsPlatform/varistran documentation built on March 21, 2020, 3:20 p.m.