CALISTA: Clustering and LINEAGE Inference in Single-Cell Transcriptional Analysis

Nan Papili Gao; Thomas Hartmann; Tao Fang; Rudiyanto Gunawan

doi:10.3389/fbioe.2020.00018

CALISTA: Clustering and LINEAGE Inference in Single-Cell Transcriptional Analysis

Front Bioeng Biotechnol. 2020 Feb 4:8:18. doi: 10.3389/fbioe.2020.00018. eCollection 2020.

Authors

Nan Papili Gao^{1

2}, Thomas Hartmann¹, Tao Fang¹, Rudiyanto Gunawan^{1

3}

Affiliations

¹ Institute for Chemical and Bioengineering, ETH Zurich, Zurich, Switzerland.
² Swiss Institute of Bioinformatics, Lausanne, Switzerland.
³ Department of Chemical and Biological Engineering, University at Buffalo, Buffalo, NY, United States.

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.

Keywords: cell clustering; cell differentiation; gene expression; lineage progression; pseudotime; random dropouts; single-cell; transcriptional bursts.