Fig. 5. Strategies for the integrative analysis of single-cell multiomics data.

Blue and green heat maps represent the data matrixes for the transcriptome and DNA methylome, respectively. The symbols n, m₁, and m₂ denote the numbers of cells (n) and genes with the levels of mRNA (m₁) and DNA methylation (m₂). Colors in the heat maps represent the levels of mRNA and DNA methylation (see color bars; Max, the maximum level). a Correlation analysis between mRNA and DNA methylation levels. Scatter plots show mRNA and DNA methylation levels for genes 1 (top) and 2 (bottom). Line, regression line; r, Pearson correlation. Negative and positive correlations are shown for genes 1 and 2, respectively. b Analysis of scRNA-seq data followed by the integration of scBS-seq data. Principal component analysis (PCA) is first applied to scRNA-seq data to obtain score values for k PCs, the pairwise Euclidean distances of cells are computed using the score values for k PCs to generate a distance matrix, t-stochastic neighbor embedding (t-SNE) clustering is applied to the distance matrix to identify cell populations, and scBS-seq data are then integrated into these cell populations as described in the text. C1-3, cell populations 1-3, respectively. c Integrative analysis of scRNA-seq and scBS-seq data to generate the overall single-cell map. The analytical scheme of MOFA is shown. Two-way matrix decomposition is performed for scRNA-seq and scBS-seq data using k factors, resulting in weight matrixes (m₁ × k for scRNA-seq data and m₂ × k for scBS-seq data) and a factor loading matrix (k × n for n cells). Factor loading values are used to compute a distance matrix that is then used for t-SNE clustering. The t-SNE plot shows cell populations 1-4 (C1-4) identified collectively by scRNA-seq and scBS-seq data.