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.
Accelerating Research
Robert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom
Address
John Eccles HouseRobert Robinson Avenue,
Oxford Science Park, Oxford
OX4 4GP, United Kingdom