scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics

scTour is an innovative and comprehensive method for dissecting cellular dynamics by analysing datasets derived from single-cell genomics.

It provides a unifying framework to depict the full picture of developmental processes from multiple angles including the developmental pseudotime, vector field and latent space.

It further generalises these functionalities to a multi-task architecture for within-dataset inference and cross-dataset prediction of cellular dynamics in a batch-insensitive manner.

Key features

• cell pseudotime estimation with no need for specifying starting cells.

• transcriptomic vector field inference with no discrimination between spliced and unspliced mRNAs.

• latent space mapping by combining intrinsic transcriptomic structure with extrinsic pseudotime ordering.

• model-based prediction of pseudotime, vector field, and latent space for query cells/datasets/time intervals.

• insensitive to batch effects; robust to cell subsampling; scalable to large datasets.

Installation

scTour requires Python ≥ 3.7:

pip install sctour

Reference

Qian Li, scTour: a deep learning architecture for robust inference and accurate prediction of cellular dynamics, 2023, Genome Biology.