Fig. 1: PAGODA and t-SNE reveals at least two clusters in differentiating preadipocytes.

a Single-cell RNA seq was performed on differentiating preadipocytes beginning at 80% confluency and differentiated for 7 days. PAGODA was used to determine the optimal cell clustering based on the genes driving the heterogeneity. The result was plotted by t-SNE. A total of 2092 are shown. b Single-cell expression profiles in were analyzed to determine the genes and pathways driving the heterogeneity. PAGODA was used to perform weighted principle component analysis on pre-defined (e.g., Gene Ontology terms) and de novo gene sets. The gene sets were scored on their significance. Correlated gene sets were coalesced in order to reduce redundancy. The heatmap of the significance gene sets shows a few de novo gene sets captured major aspects (i.e., non-redundant principal components) of heterogeneity. c Differential gene expression was performed between the left and right clusters for each day of differentiation in differentiating preadipocytes beginning at 100% confluency. Gene set enrichment analysis was performed on the differentially expressed genes and the top 10 up- and down-regulated pathways sorted by z-score are shown for day 7. d Differential gene expression was performed between the left and right clusters of day 7 adipocytes. The genes shown are previously-studied adipocyte markers for lineage or differentiation stage. For each gene, the maximum likelihood estimate and 95% confidence interval of the log2 expression ratio (right cluster over left cluster) is shown. The brown adipocyte markers MYF5 and UCP1 were not detected in any cells.