GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership

Autor:	Peter Carbonetto, Kaixuan Luo, Abhishek Sarkar, Anthony Hung, Karl Tayeb, Sebastian Pott, Matthew Stephens
Jazyk:	angličtina
Rok vydání:	2023
Předmět:	Gene expression Single-cell RNA-seq Single-cell ATAC-seq Differential expression analysis Dimensionality reduction Parts-based representations Biology (General) QH301-705.5 Genetics QH426-470
Zdroj:	Genome Biology, Vol 24, Iss 1, Pp 1-37 (2023)
Druh dokumentu:	article
ISSN:	1474-760X 18939368
DOI:	10.1186/s13059-023-03067-9
Popis:	Abstract Parts-based representations, such as non-negative matrix factorization and topic modeling, have been used to identify structure from single-cell sequencing data sets, in particular structure that is not as well captured by clustering or other dimensionality reduction methods. However, interpreting the individual parts remains a challenge. To address this challenge, we extend methods for differential expression analysis by allowing cells to have partial membership to multiple groups. We call this grade of membership differential expression (GoM DE). We illustrate the benefits of GoM DE for annotating topics identified in several single-cell RNA-seq and ATAC-seq data sets.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/4270b099569c4995a25bdf1893936866 Zobrazit plný text záznamu Full text from SpringerLink View record in DOAJ