(a) Shown is a t-SNE map highlighting clusters of cells with similar transcriptomes derived by RaceID3 on single-cell RNA-seq data of intestinal epithelial cells1. (b) Heatmap of log2-transformed averaged normalized expression across clusters. The cluster number and color are indicated on the right. Only clusters with >3 cells were included. A hierarchical clustering dendogram is shown on the right margin. (c-g) The fate bias, corresponding to the probability of a cell to be assigned to a given lineage, is color-coded in the t-SNE map. The fate bias predicted by FateID (left) and STEMNET2 (middle) is shown along with log2-transformed aggregated normalized expression of two lineage markers. Fate bias and marker gene expression is shown for the (c) Paneth cell, (d) the goblet cell, (e) the enteroendocrine, (f) the enterocyte, and (g) the tuft cell lineage. In (a-g) data for 505 cells from n=3 animals are shown. (h) Barplot comparing Spearman’s correlation coefficient between the expression levels of early lineage markers and fate bias computed by FateID and STEMNET. Error bars correspond to standard errors of Fisher’s z-transformed correlation values calculated across all cells after removal of target clusters (303 cells from three independent experiments with n = 3 mice). P-values were derived from the difference of z-scores divided by the standard error assuming a standard normal distribution using William’s test (*P < 0.05, **P < 0.001). (i-l) Shown is a t-SNE maps highlighting log2-transfromed normalized transcript levels of (i) Neurog3, (j) Neurod1, (k) Muc2, and (l) Clca4. (m-q) Scatterplots comparing fate bias predicted by FateID and STEMNET for (m) the Paneth cell, (n) the goblet cell, (o) the enteroendocrine cell, (p) the enterocyte cell, and (q) the tuft cell lineage. Although the predictions are overall correlated, STEMNET predicts more uniform levels across a larger fraction of the multipotent cell population. In (a-g) and (i-q) data for 505 cells from three independent experiments with n = 3 mice are shown.
1. Grün, D. et al. De Novo Prediction of Stem Cell Identity using Single-Cell Transcriptome Data. Cell Stem Cell 19, 266–277 (2016).
2. Velten, L. et al. Human haematopoietic stem cell lineage commitment is a continuous process. Nat. Cell Biol. 19, 271–281 (2017).