Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles.
Ontology highlight
ABSTRACT: Motivation
Data transformations are an important step in the analysis of RNA-seq data. Nonetheless, the impact of transformation on the outcome of unsupervised clustering procedures is still unclear.Results
Here, we present an Asymmetric Winsorization per-Sample Transformation (AWST), which is robust to data perturbations and removes the need for selecting the most informative genes prior to sample clustering. Our procedure leads to robust and biologically meaningful clusters both in bulk and in single-cell applications.Availability and implementation
The AWST method is available at https://github.com/drisso/awst. The code to reproduce the analyses is available at https://github.com/drisso/awst_analysis.Supplementary information
Supplementary data are available at Bioinformatics online.
SUBMITTER: Risso D
PROVIDER: S-EPMC8388024 | biostudies-literature | 2021 Aug
REPOSITORIES: biostudies-literature
Publications
Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles.
Risso Davide D Pagnotta Stefano Maria SM
Bioinformatics (Oxford, England) 20210801 16
<h4>Motivation</h4>Data transformations are an important step in the analysis of RNA-seq data. Nonetheless, the impact of transformation on the outcome of unsupervised clustering procedures is still unclear.<h4>Results</h4>Here, we present an Asymmetric Winsorization per-Sample Transformation (AWST), which is robust to data perturbations and removes the need for selecting the most informative genes prior to sample clustering. Our procedure leads to robust and biologically meaningful clusters bot ...[more]
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