Fig. 7

Optimisation of the G0 arrest signature for use in single-cell RNA-seq data. a Methodology for refining the gene signature of G0 arrest: random forest classifiers are trained to distinguish arrested from cycling tumours on three high confidence datasets; Gini index thresholding is optimised to prioritise a final list of 35 genes. b Gini index variation, correlation with experimentally measured quiescence via EdU and phospho-Rb staining assays, and corresponding p-values are plotted as the number of genes considered in the model is increased. The vertical black dashed line indicates the threshold chosen for the final solution of 35 genes. The horizontal grey dotted line indicates the threshold for p-value significance. c Additional external validation of the 35 gene signature acting as a classifier of G0 arrested and proliferating cells in single-cell and bulk datasets. d Dropout in single-cell data by gene signature. The percentage of genes out of the 35 (red) and 139 (grey) gene lists with reported expression across the single-cell RNA-seq datasets analysed in this study. e Proportion of cycling and G0 arrested cells estimated in single-cell datasets of p53 wild-type and mutant lines treated with 5FU, as well as cells treated with EGFR inhibitors. Data as in Fig. 6