GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership
Carbonetto, Peter; Luo, Kaixuan; Sarkar, Abhishek; Hung, Anthony; Tayeb, Karl; Pott, Sebastian; Stephens, Matthew
2023
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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.

Details

Title GoM DE: interpreting structure in sequence count data with differential expression analysis allowing for grades of membership
Author Carbonetto, Peter : University of Chicago : (http://orcid.org/0000-0003-1144-6780)
Luo, Kaixuan : University of Chicago
Sarkar, Abhishek : University of Chicago
Hung, Anthony : University of Chicago
Tayeb, Karl : University of Chicago
Pott, Sebastian : University of Chicago
Stephens, Matthew : University of Chicago
Content Type Article
Published in Genome Biology
Keywords
Identifier(s) DOI: https://doi.org/10.1186/s13059-023-03067-9
Data availability statement The fastTopics R package is available on GitHub (https://github.com/stephenslab/fastTopics) and CRAN (https://cran.r-project.org/package=fastTopics). A Seurat wrapper for fastTopics is available from https://github.com/stephenslab/seurat-wrappers. The data sets supporting the conclusions of this article are available in Zenodo repositories (https://doi.org/10.5281/zenodo.7962782; https://doi.org/10.5281/zenodo.7962831). These Zenodo repositories also include the source code implementing the analyses and workflowr websites (https://doi.org/10.12688/f1000research.20843.1) for browsing the code and results. Permission to use the source code in these repositories is granted under the MIT license. Numerical implementations of the contributed statistical methods, including tools for visualizing the results generated by these methods, are available from the fastTopics R package (http://arxiv.org/abs/2105.13440; https://www.github.com/stephenslab/fastTopics) under the MIT license. The gene sets used in the GSEA were compiled into an R package (https://github.com/stephenslab/pathways), also distributed under the MIT license. All data sets used in the study were obtained from public sources (https://www.10xgenomics.com/support/single-cell-gene-expression; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103354; https://shendurelab.github.io/mouse-atac/; https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE96772). A description of how these data sets were used is provided in the “Methods” section.
Funding Information NHGRI, 5R01HG002585
Publication Date 2023-10-19
Language English
Copyright Statement

© The Author(s) 2023

This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

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Record Appears in Biological Sciences Division > Genetics, Genomics, and Systems Biology
Biological Sciences Division > Human Genetics
Physical Sciences Division > Statistics
Biological Sciences Division > Medicine
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Record Created 2023-12-03

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