Scalable multi-sample single-cell data analysis by Partition-Assisted Clustering and Multiple Alignments of Networks
Fig 7
Calculation of sample clusters and their underlying network structures.
(a) In the batch effect simulation data, PAC was used to discover several subpopulations per sample without advanced knowledge of the exact number of subpopulations. Here, the colors denote the different clusters within each sample. Panels (b)-(c) show the networks of the subpopulations in both samples 1 and 2, respectively, that are discovered in (a). In these networks, the nodes denote the markers (or genes) measured (in this simulation data, the dimensions are named V1, V2,…, V5). The edges denote correlative relationship in terms of mutual information. These networks can be grouped by similarities to organize the subpopulations across samples. In the PAC-MAN implementation, the alignment is based on Jaccard dissimilarity network structure, and we organize the networks with hierarchical clustering of the Jaccard scores.