Integrative Analysis of Gene Expression and Alternative Polyadenylation from Single-Cell RNA-seq Data

Abstract

Single-cell RNA-seq (scRNA-seq) is a powerful technique for assaying transcriptional profile of individual cells. However, high dropout rate and overdispersion inherent in scRNA-seq hinders the reliable quantification of genes. Recent bioinformatic studies switched the conventional gene-level analysis to APA (alternative polyadenylation) isoform level, and revealed cell-to-cell heterogeneity in APA usages and APA dynamics in different cell types. The additional layer of APA isoforms creates immense potential to develop cost-efficient approaches for dissecting cell types by integrating multiple modalities derived from existing scRNA-seq experiments. Here we proposed a pipeline called scAPAfuse for enhancing cell type clustering and identifying of novel/rare cell types by combing gene expression and APA profiles from the same scRNA-seq data. scAPAfuse first maps gene expression and APA profiles to a shared low-dimensional space using partial least squares. Then anchors (i.e., similar cells) between gene and APA profiles were identified by constructing the nearest neighbors of cells in the low-dimensional space, using algorithms like hyperplane local sensitive hash and shared nearest neighbor. Finally, gene and APA profiles were integrated to a fused matrix, using the Gaussian kernel function. Applying scAPAfuse on four public scRNA-seq datasets including human peripheral blood mononuclear cells (PBMCs) and Arabidopsis roots, new subpopulations of cells that were undetectable using the gene expression or APA profile alone were found. scAPAfuse provides a unique strategy to mitigate the high sparsity of scRNA-seq by fusing gene expression and APA profiles to improve cell type clustering, which can be included in many other routine scRNA-seq pipelines.