Document Type
Article
Publication Date
2-27-2018
Identifier
DOI: 10.1371/journal.pone.0191758; PMCID: PMC5828440
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 rand index, clustering error rate, and concordance index were utilized. As expected, our results showed that clustering performance was gained in scenarios where data transformations were applied to make the data appear "more" Gaussian in distribution.
Journal Title
PLoS One
Volume
13
Issue
2
MeSH Keywords
Cluster Analysis; Female; Humans; Likelihood Functions; Models, Genetic; Ovarian Neoplasms; Sequence Analysis, RNA
Keywords
Cluster Analysis; Female; Humans; Likelihood Functions; Models, Genetic; Ovarian Neoplasms; Sequence Analysis, RNA
Recommended Citation
Noel-MacDonnell JR, Usset J, Goode EL, Fridley BL. Assessment of data transformations for model-based clustering of RNA-Seq data. PLoS One. 2018;13(2):e0191758. Published 2018 Feb 27. doi:10.1371/journal.pone.0191758
Comments
This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Publisher's Link: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0191758