PT  - JOURNAL ARTICLE
AU  - Nan Papili Gao
AU  - Thomas Hartmann
AU  - Tao Fang
AU  - Rudiyanto Gunawan
TI  - CALISTA: Clustering and Lineage Inference in Single-Cell Transcriptional Analysis
AID  - 10.1101/257550
DP  - 2019 Jan 01
TA  - bioRxiv
PG  - 257550
4099  - http://biorxiv.org/content/early/2019/02/25/257550.short
4100  - http://biorxiv.org/content/early/2019/02/25/257550.full
AB  - We present CALISTA (Clustering and Lineage Inference in Single-Cell Transcriptional Analysis), 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 pseudotemporal cell ordering. 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 evaluated the performance of CALISTA by analyzing single-cell gene expression datasets from in silico simulations and various single-cell transcriptional profiling technologies, comprising a few hundreds to tens of thousands of cells. A comparison with existing single-cell expression analyses, including MONOCLE 2 and SCANPY, demonstrated the superiority of CALISTA in reconstructing cell lineage progression and ordering cells along cell differentiation paths. CALISTA is freely available on https://www.cabselab.com/calista.