scGPS: Determining Cell States and Global Fate Potential of Subpopulations

Frontiers in Genetics

Anne Senabouth¹  Tianqi Ma²  Quan Nguyen²  Michael Thompson²  Nathan J. Palpant²  Maika Matsumoto²  Joseph E. Powell³ 

[1] Garvan-Weizmann Centre for Cellular Genomics, Garvan Institute of Medical Research, Sydney, NSW, Australia;Institute for Molecular Bioscience, University of Queensland, Brisbane, QLD, Australia;UNSW Cellular Genomics Futures Institute, University of New South Wales, Sydney, NSW, Australia;

关键词: single cell;    machine learning;    clustering;    trajectory analysis;    cell fate;   

DOI  :  10.3389/fgene.2021.666771

来源: DOAJ

【 摘 要 】

Finding cell states and their transcriptional relatedness is a main outcome from analysing single-cell data. In developmental biology, determining whether cells are related in a differentiation lineage remains a major challenge. A seamless analysis pipeline from cell clustering to estimating the probability of transitions between cell clusters is lacking. Here, we present Single Cell Global fate Potential of Subpopulations (scGPS) to characterise transcriptional relationship between cell states. scGPS decomposes mixed cell populations in one or more samples into clusters (SCORE algorithm) and estimates pairwise transitioning potential (scGPS algorithm) of any pair of clusters. SCORE allows for the assessment and selection of stable clustering results, a major challenge in clustering analysis. scGPS implements a novel approach, with machine learning classification, to flexibly construct trajectory connections between clusters. scGPS also has a feature selection functionality by network and modelling approaches to find biological processes and driver genes that connect cell populations. We applied scGPS in diverse developmental contexts and show superior results compared to a range of clustering and trajectory analysis methods. scGPS is able to identify the dynamics of cellular plasticity in a user-friendly workflow, that is fast and memory efficient. scGPS is implemented in R with optimised functions using C++ and is publicly available in Bioconductor.

【 授权许可】

Unknown