ATLAS

ATLAS is a framework for multi-omic trajectory inference from paired single-cell RNA and ATAC sequencing data. By integrating transcriptional and chromatin accessibility information within a unified representation, ATLAS enables pseudotime reconstruction and cell fate prediction that directly incorporate regulatory dynamics. See our publication to learn more:

Publication will come soon! :)

ATLAS main functionalities include:

• Computation of gene activity from scATAC-seq data

• Support for multi-omics representations using MuData

• Creation of a Weighted Nearest Neighbors graph

• Trajectory inference based on multiple strategies

• Trajectory evaluation based on unsupervised metrics

Getting started

Please refer to the documentation in particular, the API documentation.

Installation

To install ATLAS plase use:

pip install atlas-smilies

Release notes

See the CHANGELOG.

Contact

For questions, bug report and help requests, please use the issue tracker.

Related Works

• Lange, M., Bergen, V., Klein, M. et al. CellRank for directed single-cell fate mapping. Nat Methods 19, 159–170 (2022). https://doi.org/10.1038/s41592-021-01346-6

• Weiler, P., Lange, M., Klein, M. et al. CellRank 2: unified fate mapping in multiview single-cell data. Nat Methods 21, 1196–1205 (2024). https://doi.org/10.1038/s41592-024-02303-9

• Setty, M., Kiseliovas, V., Levine, J. et al. Characterization of cell fate probabilities in single-cell data with Palantir. Nat Biotechnol 37, 451–460 (2019). https://doi.org/10.1038/s41587-019-0068

• Louis Faure, Ruslan Soldatov, Peter V. Kharchenko, Igor Adameyko, scFates: a scalable python package for advanced pseudotime and bifurcation analysis from single cell data, Bioinformatics, btac746; doi: https://doi.org/10.1093/bioinformatics/btac746

Disclaimer

This project includes third-party code under MIT and BSD-3 licenses, see THIRD_PARTY_NOTICES for details.