ScEpath: energy landscape-based inference of transition probabilities and cellular trajectories from single-cell transcriptomic data.

ABSTRACT:

Motivation

Single-cell RNA-sequencing (scRNA-seq) offers unprecedented resolution for studying cellular decision-making processes. Robust inference of cell state transition paths and probabilities is an important yet challenging step in the analysis of these data.

Results

Here we present scEpath, an algorithm that calculates energy landscapes and probabilistic directed graphs in order to reconstruct developmental trajectories. We quantify the energy landscape using 'single-cell energy' and distance-based measures, and find that the combination of these enables robust inference of the transition probabilities and lineage relationships between cell states. We also identify marker genes and gene expression patterns associated with cell state transitions. Our approach produces p

SUBMITTER: Jin S 

PROVIDER: S-EPMC6658715 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature