Open AccessPer-sample standardization and asymmetric winsorization lead to accurate clustering of RNA-seq expression profiles
Author(s) -
Davide Risso,
Stefano Maria Pagnotta
Publication year - 2021
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/btab091
Subject(s) - cluster analysis , computer science , standardization , sample (material) , data mining , transformation (genetics) , source code , code (set theory) , artificial intelligence , biology , gene , genetics , set (abstract data type) , chemistry , chromatography , programming language , operating system
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.