Fig. 5

Irrelevant reference subsets can be excluded to tailor CIPR pipeline to different user needs. CIPR pipeline allows users to easily exclude the reference subsets that are of no interest for the study at hand. Limiting the analysis only to the relevant reference subsets can increase the readability of the graphical outputs and may better differentiate closely related single cell clusters. To demonstrate this capability, we subsetted the scRNAseq dataset described in Fig. 1 to contain only T cells (as defined by the simultaneous expression of Cd3e and Cd4 or Cd8a marker genes). We then performed CIPR analyses with or without limiting the pipeline to T cell references within the ImmGen dataset. a Uniform manifold approximation and projection (UMAP) plot with 6 distinct single-cell clusters shows the heterogeneity within the T cell subsets in the tumor microenvironment. b Representative feature plots indicate that the clusters are composed of Cd4+ helper and Cd8a+ cytotoxic T cells some of which exhibited an activated phenotype (Ifng+ cells) while others appeared to have naïve-memory phenotype (Sell+ cells). Of note, cluster 06 is composed of Foxp3+ regulatory T cells (Tregs). c CIPR analysis using logFC dot product method shows that highest scoring reference subsets for cluster 06 are regulatory T cell subsets within the ImmGen reference data. d Graphs show that identity scores calculated by CIPR, SingleR and scmap are positively correlated for both cluster 01 (activated Cd8a+ cells) and cluster 06 (Tregs). For these analyses, the entire ImmGen reference data (296 samples spanning 20 different cell types) were used, and the calculations were performed at the cluster level as described above. e The positive correlation between different analytical approaches were stronger when the reference dataset was limited to T cell subsets (70 samples in ImmGen data). In general, the highest scoring reference cell subsets in CIPR also scored the highest in scmap and SingleR methods