The PBMC1 dataset is defined in [21], and the hES dataset is described in [23]. account the hierarchical structure of cell types. We illustrate the application of the new metrics in constructed examples as well as several real single cell datasets and show that they provide more biologically plausible results. cells and a total of pairwise associations, the RI computes the proportion of associations that are in agreement between the clustering and the reference. In other words, for each pair, the relationship defined in the reference is considered either correctly recovered or not. The RI computes the success rate Rabbit Polyclonal to ELOA3 of correctly recovering the relationship, giving all pairwise associations the same weight. The ARI adjusts the RI by considering the expected value under the null probability model that this clustering is performed randomly given the marginal distributions of cluster sizes. In our proposed wRI, we assign different weights for each pairwise relationship based on the cell type hierarchy information. For example, putting two cells from closely related subtypes (CD4 and CD8 T cells) into one cluster accrues less penalty VR23 than grouping cells from more distinct cell types (T cells and B cells). In addition, breaking up a pair of cells of the same type into individual clusters may receive less penalty if cells of that type show higher variation from the mean cell type-specific expression profile, compared to breaking up pairs from a tight cluster. The mutual information (MI) is usually a measure of shared information between two partitions. It is the proportion of entropy in the reference partition explained by the clustering. Even when the reference knowledge has a hierarchy, the MI ignores the tree structure and only makes use of memberships in the leaf nodes. By definition, there is no entropy among cells within the same leaf node. For a group of cells separated into two cell types, the entropy is the same whether the two cell types are loosely or closely related. In our proposed wNMI, we use a structured entropy that considers the hierarchical associations between cell types to reflect the accuracy of a clustering algorithm in recovering the cell populations structure. Detailed description of the wRI and wNMI methods is usually provided in the Method and material section. Case studies Constructed examplesWe first show constructed toy examples to illustrate the advantages of wRI and wMI in Fig.?1. There are four cell types (represented as A1, A2, B1, and B2) in the true reference with 2, 14, 14, and 20 cells, respectively. We consider two hypothetical tree structures for the cell types, shown as tree A (Fig.?1a) and tree B (Fig.?1b). Two clustering results, both forming four clusters, are compared here. Physique?1c shows the confusion matrices of the clustering results. Clustering 1 (C1) correctly clusters the cells of type A1 and A2, but mistakenly clusters some B2 cells with B1 cells. Clustering 2 (C2) correctly clusters the cells of type A1 and B1, but mistakenly clusters some B2 cells with A2 cells. Intuitively, since B1 VR23 and B2 both belong to type B, the mistakes in C1 may be considered more tolerable compared to those in C2, especially when the truth is tree A where B1 and B2 cells are very similar. Open in a separate windows Fig. 1 Illustrative examples for using RI/MI and wRI/wMI to evaluate the clustering results. a, b Two examples of hierarchical relationship between a group of A1, A2, B1, and VR23 B2 cells. Texts under the trees indicate cell types from R, reference; C1, clustering 1; and C2, clustering 2. c Confusion matrices of two clustering and steps of clustering performance under reference a or b The VR23 classical metrics (ARI and NMI) give the two clustering results identical scores when the true cell type hierarchy is usually either tree A or tree B. This is because the classical metrics treat four groups as completely exchangeable, and the.