scGPS: Determining Cell States and Global Fate Potential of Subpopulations - Thompson, Michael; Matsumoto, Maika; Ma, Tianqi; Senabouth, Anne; Palpant, Nathan J.; Powell, Joseph E.; Nguyen, Quan (Frontiers in Genetics, 2021)
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 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. scGPS implements feature selection, followed 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.