Autism Spectrum Disorder (ASD) is a complex and heterogeneous disorder, which has been reported affected by many different causative genes. In recent years, single-nucleus RNA sequencing (snRNA-seq) have been widely used to reveal variation in gene expression across cell types during the pathology of ASD. However, the vast majority of current studies only consider information on the single modality of gene expression profile obtained from snRNA-seq. Recent bioinformatics studies have successfully captured key transcriptomic information on alternative polyadenylation (APA) from single-cell RNA-seq (scRNA-seq) data to reveal APA dynamics between cell types. Here we integrated multimodal information of gene expression and APA to gain a more comprehensive understanding of the molecular mechanisms of ASD. APA sites were first identified at the single cell level in patients with ASD and healthy populations under different cell types. Gene features were selected by two unsupervised methods using profiles of APA usages and gene expression, respectively. Then these gene features were integrated to build a cell-type-specific classification model based on multiview privileged support vector machines to predict whether a cell is from a ASD patient or a healthy control. With this model, the contribution of an individual modality was calculated, and genes from different modalities were ranked by importance score to identify high-risk genes that are highly associated with ASD. The functions of these high-risk genes were found to be associated with ASD-related biological functions and significantly different across cell types, highlighting the cell-type-specificity of ASD. These high confidence risk genes can serve as potential biomarkers for the diagnosis and treatment of ASD.

MOTIVATION: Alternative polyadenylation (APA) is a widespread post-transcriptional regulatory mechanism across all eukaryotes. With the accumulation of genome-wide APA sites, especially those with single-cell resolution, it is imperative to develop easy-to-use visualization tools to guide APA analysis. RESULTS: We developed an R package called vizAPA for visualizing APA dynamics from bulk and single-cell data. vizAPA implements unified data structures for APA data and genome annotations. vizAPA also enables identification of genes with differential APA usage across biological samples and/or cell types. vizAPA provides four unique modules for extensively visualizing APA dynamics across biological samples and at the single-cell level. vizAPA could serve as a plugin in many routine APA analysis pipelines to augment studies for APA dynamics. AVAILABILITY AND IMPLEMENTATION: https://github.com/BMILAB/vizAPA.