Fig. 1

Overview of scAI. a scAI learns aggregated epigenomic profiles and low-dimensional representations from both transcriptomic and epigenomic data in an iterative manner. scAI uses parallel scRNA-seq and scATAC-seq/single cell DNA methylation data as inputs. Each row represents one gene or one locus, and each column represents one cell. In the first step, the epigenomic profile is aggregated based on a cell-cell similarity matrix that is randomly initiated. In the second step, transcriptomic and aggregated epigenomic data are simultaneously decomposed into a set of low-rank matrices. Entries in each factor (column) of the gene loading matrix (gene space), locus loading matrix (epigenomic space), and cell loading matrix (cell space) represent the contributions of genes, loci, and cells for the factor, respectively. In the third step, a cell-cell similarity matrix is computed based on the cell loading matrix. These three steps are repeated iteratively until the stop criterion is satisfied. b scAI ranks genes and loci in each factor based on their loadings. For example, four genes and loci are labeled with the highest loadings in factor 3. c Simultaneous visualization of cells, marker genes, marker loci, and factors in a 2D space by an integrative visualization method VscAI, which is constructed based on the four low-rank matrices learned by scAI. Small filled dots represent the individual cells, colored by true labels. Large red circles, black filled dots, and diamonds represent projected factors, marker genes, and marker loci, respectively. d The regulatory relationships are inferred via correlation analysis and nonnegative least square regression modeling of the identified marker genes and loci. An arch represents a regulatory link between one locus and the transcription start site (TSS) of each marker gene. The arch colors indicate the Pearson correlation coefficients for gene expression and loci accessibility. The red stem represents the TSS region of the gene, and the black stem represents each locus