Supplementary Figure 5: Data from single cells are essential for detecting subpopulations.
From: Single-cell ChIP-seq reveals cell subpopulations defined by chromatin state
A) The correlation of H3K4me3 Drop-ChIP profiles for a mixture of 200 ES cells and 200 MEFs with H3K4me3 bulk profiles of ES cells (y-axis) is plotted vs their correlation with H3K4me3 MEF bulk profile (x-axis) (left). Two groups of cells, one correlating stronger with ES profile and another resembling MEF profile, are clearly observed (and enables correct assignment of cell type with >95% accuracy; Fig. 4C). Similar clusters could also be derived by unsupervised divisive hierarchical clustering (middle). Aggregate profiles derived for these clusters closely match ES cells and MEFs, respectively (right; shown for region on Chromosome 17). B) To demonstrate the importance of single-cell data, we combined reads from randomly selected sets of 5 cells and repeated both analyses. Neither supervised (left) nor unsupervised (middle) procedures were able to distinguish the two cell states within the mixed population when starting from these compromised data. Consequently, the chromatin profiles could no longer be deconvoluted (right). C) For comparison, bulk H3K4me3 profiles for ES cells and MEFs are shown over the same locus.
