scGPS: Determining Cell States and Global Fate Potential of Subpopulations

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 (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.

CZ CELL×GENE Discover: A single-cell data platform for scalable exploration, analysis and modeling of aggregated data

“…Modeling of single-cell data at scale is an important aspect of Census given its demonstrated uses for in silico experimentation, 53,54 data integration and annotation, 55,56 cell state prediction, 57,58 and clinical applications. 59,60,61 PyTorch 62 is one of the most popular machine learning frameworks in single-cell, with notable models built using the PyTorch library including scvi-tools models, Geneformer, 63 and scGPT.…”