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

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 simu

SUBMITTER: Noel-MacDonnell JR 

PROVIDER: S-EPMC5828440 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature