Automatic identification of relevant genes from low-dimensional embeddings of single-cell RNA-seq data
Angerer P, Fischer DS, Theis FJ, Scialdone A, Marr C (2020)
Publication Type: Journal article
Publication year: 2020
Journal
Book Volume: 36
Pages Range: 4291-4295
Journal Issue: 15
DOI: 10.1093/bioinformatics/btaa198
Abstract
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.
Involved external institutions
Germany (DE)How to cite
APA:
Angerer, P., Fischer, D.S., Theis, F.J., Scialdone, A., & Marr, C. (2020). Automatic identification of relevant genes from low-dimensional embeddings of single-cell RNA-seq data. Bioinformatics, 36(15), 4291-4295. https://doi.org/10.1093/bioinformatics/btaa198
MLA:
Angerer, Philipp, et al. "Automatic identification of relevant genes from low-dimensional embeddings of single-cell RNA-seq data." Bioinformatics 36.15 (2020): 4291-4295.
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