Inference of cell state transitions and cell fate plasticity from single-cell with MARGARET.

ABSTRACT: Despite recent advances in inferring cellular dynamics using single-cell RNA-seq data, existing trajectory inference (TI) methods face difficulty in accurately reconstructing the cell-state manifold and cell-fate plasticity for complex topologies. Here, we present MARGARET (https://github.com/Zafar-Lab/Margaret) for inferring single-cell trajectory and fate mapping for diverse dynamic cellular processes. MARGARET reconstructs complex trajectory topologies using a deep unsupervised metric learning and a graph-partitioning approach based on a novel connectivity measure, automatically detects terminal cell states, and generalizes the quantification of fate plasticity for complex topologies. On a diverse benchmark consisting of synthetic and real datasets, MARGARET outperformed state-of-the-ar

SUBMITTER: Pandey K 

PROVIDER: S-EPMC9410915 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature