ScGPS: Determining Cell States and Global Fate Potential of Subpopulations

Overview

Journal Front Genet

Date 2021 Aug 5

PMID 34349778

Authors

Michael Thompson

Maika Matsumoto

Tianqi Ma

Anne Senabouth

Nathan J Palpant

Joseph E Powell

Quan Nguyen

Affiliations

Soon will be listed here.

Abstract

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 () to characterise transcriptional relationship between cell states. decomposes mixed cell populations in one or more samples into clusters ( algorithm) and estimates pairwise transitioning potential ( algorithm) of any pair of clusters. allows for the assessment and selection of stable clustering results, a major challenge in clustering analysis. implements a novel approach, with machine learning classification, to flexibly construct trajectory connections between clusters. also has a feature selection functionality by network and modelling approaches to find biological processes and driver genes that connect cell populations. We applied in diverse developmental contexts and show superior results compared to a range of clustering and trajectory analysis methods. is able to identify the dynamics of cellular plasticity in a user-friendly workflow, that is fast and memory efficient. scGPS is implemented in with optimised functions using and is publicly available in Bioconductor.

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