Supporting data for "scMAPA: Identification of Cell-type-specific Alternative Polyadenylation in Complex Tissues"

Alternative polyadenylation (APA) causes shortening or lengthening of the 3'-untranslated region (3'-UTR) of genes (APA genes) in diverse cellular processes such as cell proliferation and differentiation (cell-type-specific APA). To identify cell-type-specific APA genes in scRNA-Seq data, current bioinformatic methods have several limitations. First, they assume certain read coverage shapes in the scRNA-Seq data, which can be violated in multiple APA genes. Second, their identification is limited between two cell types and not directly applicable to the data of multiple cell types. Third, they do not control undesired source of variance that potentially introduces noise to the cell-type-specific identification of APA genes. To address these limitations, we developed a combination of a computational change-point algorithm and a statistical model, single-cell Multi-group identification of APA (scMAPA). To avoid the assumptions on the read coverage shape, scMAPA formulates a change-point problem after transforming the 3' biased scRNA-Seq data to represent the full-length 3'UTR signal. To identify cell-type-specific APA genes while adjusting for undesired source of variation, scMAPA models APA isoforms in consideration of the cell types and the undesired source. In our novel simulation data and human peripheral blood monocellular data, scMAPA outperforms existing methods in terms of sensitivity, robustness, and stability. In mouse brain data consisting of multiple cell types sampled from multiple regions, scMAPA identifies cell-type-specific APA genes, elucidating novel roles of APA for dividing immune cells and differentiated neuron cells and in multiple brain disorders. Altogether, scMAPA elucidates the cell-type-specific function of APA events and sheds novel insights into the functional roles of APA events in complex tissues.