sctour 0.1.3

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

About scTour

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 developmental pseudotime, vector field and latent space, and 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.

Preprint

Consider citing this paper if you use scTour in your analysis.

scTour features

• unsupervised estimates of cell pseudotime along the trajectory with no need for specifying starting cells
• efficient inference of vector field with no dependence on the discrimination between spliced and unspliced mRNAs
• cell trajectory reconstruction using latent space that incorporates both intrinsic transcriptome and extrinsic time information
• model-based prediction of pseudotime, vector field, and latent space for query cells/datasets
• reconstruction of transcriptomic space given an unobserved time interval

scTour performance

✅ insensitive to batch effects

✅ robust to cell subsampling

✅ scalable to large datasets

Installation

pip install sctour

Documentation

Full documentation can be found here.