Fig. 4

CIPR allows users to limit the analysis to highly variable reference genes to improve cluster annotations. As genes with variable expression profiles contain more information to discriminate cell types, we implemented a variance filtering parameter in CIPR. The user-defined variance threshold parameter instructs the algorithm to utilize the genes with variances above a certain quantile across the reference dataset, thus limiting the analysis to highly variable genes. Plots compare the CIPR results with or without variance thresholding when the all-genes Spearman’s method is used. Identity- and z-scores were calculated for clusters 05 (NK cells) and 15 (pDCs) using ImmGen reference and results for individual reference samples types are plotted as color-coded data points. Applying variance thresholding and increasing its stringency from top 10% to top 1% reduced the identity scores of low/intermediate-scoring reference cell subsets while the highest scoring reference cell subsets remained unaffected as evidenced by data points overlapping with y = x line for (a) cluster 05, and (b) cluster 15. Similar trends were observed for other clusters in analysis (not shown). The differential impact on identity scores of high- and low-scoring reference cell subsets lead to an increased z-score for the highest-scoring reference subsets for both (c) cluster 05 and (d) cluster 15. These findings suggest that variance thresholding can improve the discrimination of some reference cell subsets. Although the best thresholding value remains to be determined in individual studies, CIPR pipeline allows a level of flexibility to be adapted to different experimental contexts