Figure 6.
CD4+ GM-CSF+ IFN-γ− T cells have a distinct developmental trajectory and use cluster-specific regulons. Single cells from the scRNAseq data set presented in Figure 2 were analyzed by using Monocle2 and SCENIC statistical packages. (A-B) Monocle single-cell trajectory of CD4+ T cells labeled according to cluster in aggregate (A) and depicted as individual clusters (B). (C) Single-cell trajectory of CD4+ T cells as labeled by Monocle state. (D) The percentage of cells from each cluster of cells falling into a particular Monocle state. The P value from Fisher’s exact test comparing the distribution of Monocle states among clusters is shown. (E) t-distributed stochastic neighbor embedding (tSNE) plot of CD4+ T cells mapped based on SCENIC binary regulon activity and labeled according to Seurat cluster. (F) tSNE plots of selected regulons used in CD4+ T cells from clusters 2 to 5.

CD4+ GM-CSF+ IFN-γ T cells have a distinct developmental trajectory and use cluster-specific regulons. Single cells from the scRNAseq data set presented in Figure 2 were analyzed by using Monocle2 and SCENIC statistical packages. (A-B) Monocle single-cell trajectory of CD4+ T cells labeled according to cluster in aggregate (A) and depicted as individual clusters (B). (C) Single-cell trajectory of CD4+ T cells as labeled by Monocle state. (D) The percentage of cells from each cluster of cells falling into a particular Monocle state. The P value from Fisher’s exact test comparing the distribution of Monocle states among clusters is shown. (E) t-distributed stochastic neighbor embedding (tSNE) plot of CD4+ T cells mapped based on SCENIC binary regulon activity and labeled according to Seurat cluster. (F) tSNE plots of selected regulons used in CD4+ T cells from clusters 2 to 5.

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