Resolution Tradeoffs in Modularity Clustering with Application to Single Cell RNA-seq

Modularity based clustering was introduced in the network literature for community detection and is now commonly applied to single cell RNA-seq (scRNAseq) datasets for cell type identification. Modularity clustering depends on a resolution parameter, which implicitly determines the number of clusters inferred, but no theory exists describing clustering as a function of the resolution. For scRNAseq, an improperly chosen resolution parameter can lead to erroneous or missed cell types.

In this work, we provide an explicit description of clustering as a function of the resolution parameter through the notion of a splitting resolution, the minimum resolution at which a graph or subgraph is split into multiple clusters. Ve show that the splitting resolution of a subgraph is inversely proportional to the frequency of the subgraph within the graph. This result extends the resolution limit result of Fortunato and Barthelemy to the setting of a general resolution parameter value.

In the network literature, the starting point for modularity clustering is a graph, but in scRNAseq applications the starting point is a cell embedding used to form a graph. Ve show that cell embeddings in scRNAseq can be approximated by Gaussian mixtures. Ve then study splitting resolutions of k-nearest neighbor graphs formed from cell embeddings distributed as a normal or a pair of isotropic normals. For such graphs, we derive formulas for the splitting resolution as a function of sample size, embedding dimension, and the covariance structure of the normals. Ve use our results to provide specific examples of type I vs II error tradeoffs implicit in the choice of the resolution parameter.