Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector

Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between...

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Veröffentlicht in:	Nature communications Jg. 14; H. 1; S. 4400 - 13
Hauptverfasser:	Ceglia, Nicholas, Sethna, Zachary, Freeman, Samuel S., Uhlitz, Florian, Bojilova, Viktoria, Rusk, Nicole, Burman, Bharat, Chow, Andrew, Salehi, Sohrab, Kabeer, Farhia, Aparicio, Samuel, Greenbaum, Benjamin D., Shah, Sohrab P., McPherson, Andrew
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Nature Publishing Group UK 20.07.2023 Nature Publishing Group Nature Portfolio
Schlagworte:	38/91 631/114/1305 631/114/794 631/208/212/2019 631/67/2329 631/67/69 Cluster Analysis Embedding Exome Sequencing Gene expression Gene Expression Profiling Gene sequencing Genes Humanities and Social Sciences multidisciplinary Phenotypes Principal Component Analysis Principal components analysis Reduction Ribonucleic acid RNA Science Science (multidisciplinary) Sequence Analysis, RNA - methods Single-Cell Analysis - methods Sparse gene Transcription Vector spaces
ISSN:	2041-1723, 2041-1723
Online-Zugang:	Volltext
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Zusammenfassung:	Deciphering individual cell phenotypes from cell-specific transcriptional processes requires high dimensional single cell RNA sequencing. However, current dimensionality reduction methods aggregate sparse gene information across cells, without directly measuring the relationships that exist between genes. By performing dimensionality reduction with respect to gene co-expression, low-dimensional features can model these gene-specific relationships and leverage shared signal to overcome sparsity. We describe GeneVector, a scalable framework for dimensionality reduction implemented as a vector space model using mutual information between gene expression. Unlike other methods, including principal component analysis and variational autoencoders, GeneVector uses latent space arithmetic in a lower dimensional gene embedding to identify transcriptional programs and classify cell types. In this work, we show in four single cell RNA-seq datasets that GeneVector was able to capture phenotype-specific pathways, perform batch effect correction, interactively annotate cell types, and identify pathway variation with treatment over time. In single-cell RNA-seq analyses, it would be critical to measure the relationships between genes. Here, the authors develop a framework for single-cell dimensionality reduction that incorporates gene-specific relationships - GeneVector -, and use it for tasks such as annotating cell types and analysing pathway variation after treatment.
Bibliographie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	2041-1723 2041-1723
DOI:	10.1038/s41467-023-39985-2