GiniClust3: a fast and memory-efficient tool for rare cell type identification

Abstract

MotivationWith the rapid development of single-cell RNA sequencing technology, it is possible to dissect cell-type composition at high resolution. A number of methods have been developed with the purpose to identify rare cell types. However, existing methods are still not scalable to large datasets, limiting their utility. To overcome this limitation, we present a new software package, called GiniClust3, which is an extension of GiniClust2 and significantly faster and memory-efficient than previous versions.\n\nResultsUsing GiniClust3, it only takes about 7 hours to identify both common and rare cell clusters from a dataset that contains more than one million cells. Cell type mapping and perturbation analyses show that GiniClust3 could robustly identify cell clusters.\n\nAvailabilityGiniCluster3 is implemented in the open-source python package, with source code freely available through the Github (https://github.com/rdong08/GiniClust3).\n\nContactgcyuan@jimmy.harvard.edu\n\nSupplementary informationSupplementary data are available at Bioinformatics online.