ScLTNN: an innovative tool for automatically visualizing single-cell trajectories.

ABSTRACT:

Motivation

Cellular state identification and trajectory inference enable the computational simulation of cell fate dynamics using single-cell RNA sequencing data. However, existing methods for constructing cell fate trajectories demand substantial computational resources or prior knowledge of the developmental process.

Results

Here, based on the discovery of the consistent expression distribution of highly variable genes, we create a new tool named scRNA-seq latent time neural network (scLTNN) by combining an artificial neural network with a distribution model. This innovative tool is pre-trained and capable of automatically inferring the origin and terminal state of cells, and accurately illustrating the developmental trajectory of cells with minimal use of computational re

SUBMITTER: Xing C 

PROVIDER: S-EPMC11889453 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature