Fig. 2

Different analytical methods implemented in CIPR performs comparably to annotate single cell clusters. Three of the analytical methods in CIPR (logFC dot product, logFC Spearman’s or Pearson’s correlation) utilizes only differentially expressed genes in clusters. The recommended approach in CIPR is logFC dot product method since it takes both the direction and the amount of differential expression into account when calculating identity scores per cluster. The other approaches in CIPR are designed to analyze the expression profiles of all the genes in the experimental data regardless of their differential expression status. This figure compares the predictions of the logFC dot product method to other analytical approaches in CIPR. Data points in the scatter plots indicate the identity score of individual ImmGen reference cell subsets calculated for clusters 05 and 15 by different methods. As expected, there is a strong correlation between the results of logFC dot product method and (a) logFC Spearman’s and (b) logFC Pearson’s correlation methods for both clusters. c, d The same strong correlation was observed when the z-scores were compared for these methods, although logFC dot product differentiated the highest scoring reference subsets slightly better as evidenced by a higher z-score. The results of (e) all-genes Spearman’s and (f) all-genes Pearson’s methods show an overall positive correlation with those from logFC dot product method, although logFC dot product approach was able to better differentiate the top-scoring reference subsets as evidenced by higher z-scores shown in panels g and h. Similar observations were made for other clusters in the experimental dataset but are not shown due to space constraints