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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Bibliographic Details
Published in:Nature communications Vol. 14; no. 1; pp. 4400 - 13
Main Authors: 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
Language:English
Published: London Nature Publishing Group UK 20.07.2023
Nature Publishing Group
Nature Portfolio
Subjects:
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 Access:Get full text
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Summary: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.
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ISSN:2041-1723
2041-1723
DOI:10.1038/s41467-023-39985-2