Fig. 1: The SCMER approach.

(a) Workflow of SCMER. SCMER selects the features that preserve the manifold from a single-cell omics dataset $\mathbf{\text{X}}$. Features can be selected from either $\mathbf{\text{X}}$ or another co-assayed omics ${\mathbf{\text{X}}}^{\prime }$. Vector $\mathbf{\text{w}}$ indicates the selection. $\mathbf{\text{Y}}$ is the dataset after feature selection. $\mathbf{\text{P}}$ and $\mathbf{\text{Q}}$ are cell-cell similarity matrices for $\mathbf{\text{X}}$ and $\mathbf{\text{Y}}$, respectively.

(b) Applications of SCMER. SCMER selects features that preserve the manifold and retain inter- and intra-cluster diversity, and thus can be applied to discover rich molecular pathways, integrate modalities, and design customized DNA/RNA/antibody panels of restricted sizes.

(c) Capabilities of SCMER compared with mainstream label/cluster-based differential expression (DE) analysis methods and correlation-based methods. The hypothetical branching trajectories contain common progenitors, precursors for A and B, and mature A and B.