Fig. 1

CIPR provides a R/Shiny-powered graphical user interface to facilitate cluster annotation in scRNAseq experiments. a T-distributed stochastic neighbor embedding (t-SNE) plot for the example scRNAseq data derived from murine melanoma tumor infiltrating lymphocytes shows 15 distinct immune cell clusters within the tumor microenvironment (the dataset contains 13,985 features and 11,054 cells) [28]. To demonstrate the capabilities of CIPR we focus on clusters 05 and 15 which distinctly expressed (b) natural killer cell (NK) and (c) plasmacytoid dendritic cell (pDC) markers respectively. d We used the CIPR pipeline to score the gene expression profiles of cluster 15 (pDC) against 296 mouse immune cells found in the ImmGen reference. CIPR algorithm calculates a distinct identity score for each reference cell type and generates a graphical summary of the results. In these plots, 4 highest data points (red rectangle) correspond to pDC samples within the ImmGen reference. The shaded regions in the graphs delineate 1 and 2 standard deviations around the mean identity score calculated from the entire reference data frame. Data points are color-coded based on the reference cell type allowing an easy assessment of the results. e The CIPR results for cluster 05 (NK cells) is shown. Marked data points depict the NK cells in the ImmGen dataset that had the highest identity scores. Users can visualize graphs for each cluster separately and have the option of further manipulating the plots if the R package implementation of CIPR is used. f CIPR can also generate graphical outputs to summarize the 5 top-scoring reference samples for each experimental cluster. The scatter plot shows the pDC and NK cell subsets that had the highest scores for clusters 05 and 15. In Shiny implementation of CIPR, users can draw rectangles around these points to prompt a table output which provides further information about the reference cell types on the graph