Extended Data Fig. 5: Schematic representation of non-negative matrix factorization (NMF).

a, Schematic for how to identify patterns using non-negative matrix factorization (NMF). Specifically, for a given source matrix A with dimensions of i genes and j observations, such as the snRNA-seq data, we use NMF to decompose A into two matrices W, representing the feature-level weights matrix with dimensions of i genes and k NMF patterns, and H, representing the observation-level weights matrix for j observations and k patterns. Gray matrices are known matrices, blue matrices are generated by the analysis step. Panel a is created with BioRender.com. b, Schematic for how we transfer the NMF patterns into other datasets, for example, the SRT data. Specifically, we use the transposed feature-level weights matrix W (learned from the source data, glow) and a target dataset matrix (A’ with dimensions of i’ genes and j’ observations) to obtain a new observation-level weights matrix from the target (H’ with dimensions of k patterns and j’ observations). Therefore, for every observation (spot or nuclei) in the target dataset, we have a weight for every corresponding NMF pattern learned from the source data (snRNA-seq). Gray matrices are known matrices, blue matrices are generated by the analysis step.