Assessment of data transformations for model-based clustering of RNA-Seq data

PloS One
Janelle R Noel-MacDonnellBrooke L Fridley

Abstract

Quality control, global biases, normalization, and analysis methods for RNA-Seq data are quite different than those for microarray-based studies. The assumption of normality is reasonable for microarray based gene expression data; however, RNA-Seq data tend to follow an over-dispersed Poisson or negative binomial distribution. Little research has been done to assess how data transformations impact Gaussian model-based clustering with respect to clustering performance and accuracy in estimating the correct number of clusters in RNA-Seq data. In this article, we investigate Gaussian model-based clustering performance and accuracy in estimating the correct number of clusters by applying four data transformations (i.e., naïve, logarithmic, Blom, and variance stabilizing transformation) to simulated RNA-Seq data. To do so, an extensive simulation study was carried out in which the scenarios varied in terms of: how genes were selected to be included in the clustering analyses, size of the clusters, and number of clusters. Following the application of the different transformations to the simulated data, Gaussian model-based clustering was carried out. To assess clustering performance for each of the data transformations, the adjusted ...Continue Reading

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Citations

Mar 8, 2020·Laboratory Investigation; a Journal of Technical Methods and Pathology·Evelien SchaafsmaChao Cheng
Aug 31, 2021·The Annals of Applied Statistics·David K LimJoseph G Ibrahim

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Methods Mentioned

BETA
RNA-Seq

Software Mentioned

R package
mclust
CASAVA
R package VGAM
Ensembl
TopHat2
VST
R
RSEM
Illumina

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