Development of the in-drop barcoding technique for single cells has opened the floodgate for production of large-scale scRNA-seq data. With the growing popularity of the assay and availability of affordable commercial platforms (ChromiumTM by 10x Genomics, ICELL8 by WaferGen Biosystems etc.) a sharp increase has been observed in the average sample sizes of the recent single-cell studies. A typical Drop-seq experiment involves profiling of several tens of thousands of cells on a single run.
Existing rare cell finding algorithms including GiniClust[PMID: 27368803] and RaceID [PMID: 26287467]) are computationally demanding and in practice, do not scale beyond 5-15K transcriptomes. We have developed FiRE, a patent-pending, linear-time, monolithic algorithm for rareness scoring of a massive number of cell transcriptomes in a matter of a few seconds.
Jindal, A., Gupta, P., Jayadeva, and Sengupta, D., 2018. Discovery of rare cells from voluminous single cell expression data.