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Automatic identification of relevant genes from low-dimensional embeddings of single-cell RNA-seq data.
Bioinformatics 36, 4291-4295 (2020)
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DOI
PMC
MOTIVATION: Dimensionality reduction is a key step in the analysis of single-cell RNA-sequencing data. It produces a low-dimensional embedding for visualization and as a calculation base for downstream analysis. Nonlinear techniques are most suitable to handle the intrinsic complexity of large, heterogeneous single-cell data. However, with no linear relation between gene and embedding coordinate, there is no way to extract the identity of genes driving any cell's position in the low-dimensional embedding, making it difficult to characterize the underlying biological processes. RESULTS: In this article, we introduce the concepts of local and global gene relevance to compute an equivalent of principal component analysis loadings for non-linear low-dimensional embeddings. Global gene relevance identifies drivers of the overall embedding, while local gene relevance identifies those of a defined sub-region. We apply our method to single-cell RNA-seq datasets from different experimental protocols and to different low-dimensional embedding techniques. This shows our method's versatility to identify key genes for a variety of biological processes. AVAILABILITY AND IMPLEMENTATION: To ensure reproducibility and ease of use, our method is released as part of destiny 3.0, a popular R package for building diffusion maps from single-cell transcriptomic data. It is readily available through Bioconductor. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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Publication type
Article: Journal article
Document type
Scientific Article
Keywords
Diffusion Maps; Expression; Reveals
ISSN (print) / ISBN
1367-4803
Journal
Bioinformatics
Quellenangaben
Volume: 36,
Issue: 15,
Pages: 4291-4295
Publisher
Oxford University Press
Publishing Place
Oxford
Non-patent literature
Publications
Reviewing status
Peer reviewed
Grants
DFG
Joachim Herz Stiftung
German research foundation (DFG) fellowship through the Graduate School of Quantitative Biosciences Munich (QBM)
Joachim Herz Stiftung
German research foundation (DFG) fellowship through the Graduate School of Quantitative Biosciences Munich (QBM)