Extended Data Fig. 10: The workflow of TCellMap.

a) Schematic view of the bioinformatic flow of TCellMap, created with BioRender.com. b) Leave-one-out cross-validation of the performance of TCellMap using scRNA-seq datasets included in this study. Scatter plot showing the accuracy (ACC) of T cell state prediction. A total of 24 scRNA-seq datasets with ≥5,000T cells were selected (x axis), and the prediction accuracy was calculated by comparing T cell states automatically assigned for 32 states of the 5 major cell types using the reference maps with that manually annotated by this study. The size of the bubble corresponds to the number of T cells in each scRNA-seq dataset. c) Visualization of the output of TCellMap. Four scRNA-seq datasets that were not included in original data collection of this study were used as the query datasets. UMAP views of CD8 (top) and CD4 (bottom) T cells mapped in each query dataset. Cell clusters are color coded in the same way as in Fig. 2a (CD8 T cells map) and Fig. 3a (CD4 T cell map). LUAD, lung adenocarcinoma; CRC, colorectal carcinoma; HCC, hepatocellular cell carcinoma; HNSC, head and neck cancer. The gene expression count matrices were downloaded from the Gene Expression Omnibus (GEO) database and the accession codes (GSE#) are labeled for each dataset. Further details of each query dataset are provided in the Supplementary Table 16.