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Fig. 1 | Genome Biology

Fig. 1

From: Single-cell epigenomic variability reveals functional cancer heterogeneity

Fig. 1

Strategy for identifying a cell surface marker co-varying with identified varying transcription factors. a Cartoon illustrating the strategy: single-cell ATAC-seq is followed by sequencing and analysis of cell-to-cell variation, focusing on transcription factor (TF) motifs. RNA-seq and single-cell RNA-seq data are used to correlate cell surface expression with expression of the transcription factor with highest identified variability. The expression of the cell surface protein is subsequently used to isolate subpopulations, which can then be analyzed for molecular and functional characteristics. b Hierarchical clustering of cells (rows) and high-variance transcription factors (columns). Scores represent relative accessibility and are reproduced from Buenrostro et al. [19]. c Single-cell RNA-seq data of K562 cells. Coefficient of variation is plotted against the mean FPKM, data points are colored by distance to running mean. Red dots indicate CD expression markers. d Re-analysis of RNA-seq data of GATA1 and GATA2 knockdown in K562 cells. Control FPKM is plotted against knockdown FPKM; data points are colored by density. Red dots indicate CD expression markers. FACS fluorescence-activated cell sorting, qRT-PCR quantitative reverse transcription PCR

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