An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data.

ABSTRACT: The increasing emergence of the time-series single-cell RNA sequencing (scRNA-seq) data, inferring developmental trajectory by connecting transcriptome similar cell states (i.e., cell types or clusters) has become a major challenge. Most existing computational methods are designed for individual cells and do not take into account the available time series information. We present IDTI based on the Increment of Diversity for Trajectory Inference, which combines time series information and the minimum increment of diversity method to infer cell state trajectory of time-series scRNA-seq data. We apply IDTI to simulated and three real diverse tissue development datasets, and compare it with six other commonly used trajectory inference methods in terms of topology similarity and branching accura

SUBMITTER: Hong Y 

PROVIDER: S-EPMC11330101 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature