Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles

Davide Risso; Stefano Maria Pagnotta

doi:10.1093/bioinformatics/btab091

Per-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles

Bioinformatics. 2021 Aug 25;37(16):2356-2364. doi: 10.1093/bioinformatics/btab091.

Authors

Davide Risso¹, Stefano Maria Pagnotta²

Affiliations

¹ Department of Statistical Sciences, Università degli Studi di Padova, Padova, Italy.
² Department of Science and Technology, Università degli Studi del Sannio, Benevento, Italy.

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.

Abstract

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