Automatic identification of relevant genes from low-dimensional embeddings of single-cell RNA-seq data.

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

SUBMITTER: Angerer P 

PROVIDER: S-EPMC7520047 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature