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

Genome Biology

Method

Kaixuan Luo¹  Karl Tayeb²  Matthew Stephens³  Peter Carbonetto⁴  Anthony Hung⁵  Sebastian Pott⁵  Abhishek Sarkar⁶ 

[1] Department of Human Genetics, University of Chicago, Chicago, IL, USA;Department of Human Genetics, University of Chicago, Chicago, IL, USA;Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA;Department of Human Genetics, University of Chicago, Chicago, IL, USA;Department of Statistics, University of Chicago, Chicago, IL, USA;Department of Human Genetics, University of Chicago, Chicago, IL, USA;Research Computing Center, University of Chicago, Chicago, IL, USA;Department of Human Genetics, University of Chicago, Chicago, IL, USA;Section of Genetic Medicine, University of Chicago, Chicago, IL, USA;Department of Human Genetics, University of Chicago, Chicago, IL, USA;Vesalius Therapeutics, Cambridge, MA, USA;

关键词: Gene expression;    Single-cell RNA-seq;    Single-cell ATAC-seq;    Differential expression analysis;    Dimensionality reduction;    Parts-based representations;    Matrix factorization;    Topic modeling;   

DOI  :  10.1186/s13059-023-03067-9

 received in 2023-03-03, accepted in 2023-09-20,  发布年份 2023

来源: Springer

【 摘 要 】

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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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