ClusterMap: compare multiple single cell RNA-Seq datasets across different experimental conditions

We have developed ClusterMap, a systematic method and workflow to facilitate the comparison of scRNA-seq profiles across distinct biological contexts. Using hierarchical clustering of the marker genes of each sub-group, ClusterMap matches the sub-types of cells across different samples and provides 'similarity' as a metric to quantify the quality of the match. We introduce a purity tree cut method designed specifically for this matching problem. We use Circos plot and regrouping method to visualize the results concisely. Furthermore, we propose a new metric 'separability' to summarize sub-population changes among all sample pairs. In the case studies, we demonstrate that ClusterMap has the ability to provide us further insight into the different molecular mechanisms of cellular sub-populations across different conditions. Availability and implementation: ClusterMap is implemented in R and available at https://github.com/xgaoo/ClusterMap.

Supplementary data are available at Bioinformatics online.