ArticleNature communications2023
Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector.
Article in Nature communications, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
7 citing papers in PubMed.
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- scGPT: toward building a foundation model for single-cell multi-omics using generative AI.Nature methods · 2024Article
- Immune profiling after allogeneic hematopoietic cell transplantation in pediatric acute myeloid leukemia.Blood advances · 2023Article
- Identification of transcriptional programs using dense vector representations defined by mutual information with GeneVector.Nature communications · 2023Article
- Article
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Authors and funding
14 authors.
Funding
Abstract
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.
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