CALISTA: Clustering and LINEAGE Inference in Single-Cell Transcriptional Analysis - Papili Gao, Nan; Hartmann, Thomas; Fang, Tao; Gunawan, Rudiyanto (Frontiers in Bioengineering and Biotechnology, 2020)
We present CALISTA (Clustering and Lineage Inference in Single-Cell Transcriptional Analysis), a numerically efficient and highly scalable toolbox for end-to-end analysis of single-cell transcriptomic profiles. CALISTA includes four essential single-cell analyses for cell differentiation studies, including single-cell clustering, reconstruction of cell lineage specification, transition gene identification, and cell pseudotime ordering, which can be applied individually or in a pipeline. In these analyses, we employ a likelihood-based approach where single-cell mRNA counts are described by a probabilistic distribution function associated with stochastic gene transcriptional bursts and random technical dropout events. We illustrate the efficacy of CALISTA using single-cell gene expression datasets from different single-cell transcriptional profiling technologies and from a few hundred to tens of thousands of cells. CALISTA is freely available on https://www.cabselab.com/calista.