Figure 4. Machine learning classifier distinguishes cell types in the AML ecosystem.
A. Schematic of machine learning classifiers used to predict AML cell types (Classifier 1) and to distinguish malignant from normal cells in AML tumors (Classifier 2).
B. KNN visualization shows single-cell transcriptomes from normal BM (gray; as in Figure 1D). Cells from AML samples for which wild-type or mutant transcripts were detected were projected onto this graph according to their similarity to the normal cells. Boxes depicting the density of projected cells are colored according to the ratio between wild-type and mutant transcripts. Cells with mutant transcripts (red) project along the HSC to myeloid differentiation axis.
C. Barplot shows classification of AML cells with mutant transcripts by the first Random forest classifier. The majority are classified as one of six cell types along the HSC to myeloid axis, thereby defining six malignant cell types.
D-E. t-SNE plots of AML556 (D) and AML707B (E) as in Figure 2C–D with cells colored by their classification as malignant (red) or normal (grey).
F. Scatter plot compares clinical blast counts (y-axis) to the fraction of cells classified as malignant by the machine learning classifier (x-axis). Each point corresponds to a specific AML BM aspirate (n = 27).
G. Heatmaps show cell type prediction scores (rows) for all malignant cells (columns) from five representative tumors. Cells in which wild-type and/or mutant transcripts were detected, or that express cell cycle signature genes are indicated below.
H. KNN visualizations show single-cell transcriptomes of normal BM cells (gray; as in Figure 1D). Malignant cells from the respective AMLs were projected onto this graph according to their similarity to the normal cells. The density of projected cells (red) conveys the distinct cell type compositions of these tumors.
I. Flow cytometry plots show expression of myeloid differentiation markers by the AML samples.