Open AccessDrivAER: Identification of driving transcriptional programs in single-cell RNA sequencing data
Author(s) -
Lukas M. Simon,
Fangfang Yan,
Zhongming Zhao
Publication year - 2020
Publication title -
gigascience
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.947
H-Index - 54
ISSN - 2047-217X
DOI - 10.1093/gigascience/giaa122
Subject(s) - relevance (law) , computer science , identification (biology) , set (abstract data type) , benchmark (surveying) , machine learning , computational biology , artificial intelligence , data mining , biology , botany , geodesy , political science , law , programming language , geography
Single-cell RNA sequencing (scRNA-seq) unfolds complex transcriptomic datasets into detailed cellular maps. Despite recent success, there is a pressing need for specialized methods tailored towards the functional interpretation of these cellular maps.