AUTHOR=Papili Gao Nan , Hartmann Thomas , Fang Tao , Gunawan Rudiyanto TITLE=CALISTA: Clustering and LINEAGE Inference in Single-Cell Transcriptional Analysis JOURNAL=Frontiers in Bioengineering and Biotechnology VOLUME=8 YEAR=2020 URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2020.00018 DOI=10.3389/fbioe.2020.00018 ISSN=2296-4185 ABSTRACT=

We present Clustering and Lineage Inference in Single-Cell Transcriptional Analysis (CALISTA), a numerically efficient and highly scalable toolbox for an 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 hundreds to tens of thousands of cells. CALISTA is freely available on https://www.cabselab.com/calista.