califano-lab / acdc_py

scanpy-compatible suite for automated clustering of scRNA-seq data in python
https://acdc.readthedocs.io/
MIT License
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bioinformatics clustering python single-cell-rna-seq

acdc_py 🤘

pipy License: MIT Downloads Documentation Status

Automated Community Detection of Cell Populations in Python

This repo contains the current Python implementation of ACDC, an optimization-based framework to automatize clustering of cell populations from scRNA-seq data using community detection algorithms. acdc_py is currently under development and new functionalities will be released, following completion and benchmarking. acdc_py is deployed as a Python package and fully compatible with Scanpy.

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New releases will expand functionalities to new features, including the possibility to iteratively sub-cluster cell populations to find fine grain and biologically meaningful clustering solutions.

To receive updates when novel functionalities are released, feel free to add your email to the following form: https://forms.gle/NCRPJPmXzfbrMH7U7

STAY TUNED FOR UPDATES AND NOVEL DEVELOPMENTS!🤘🏾

**Please, be aware that while this project is "work in progress" and outcomes are continuously benchmarked, cross-platform compability might not yet be guaranteed.

Installation

pypi

pip install acdc-py

local

git clone https://github.com/califano-lab/acdc_py/
cd acdc_py
pip install -e .

... Start playing around! 🎸

References

  1. Kiselev, VY, Andrews, TS, Hemberg, M. (2019) Challenges in unsupervised clustering of single-cell RNA-seq data. Nat Rev Genet 20, 273–282.
  2. Blondel, V D, Guillaume, J, Lambiotte, R, Lefebvre, E (2008). Fast unfolding of communities in large networks". Journal of Statistical Mechanics: Theory and Experiment. (10) P10008.
  3. Satija R, Farrell JA, Gennert D, Schier AF, Regev A (2015). “Spatial reconstruction of single-cell gene expression data.” Nature Biotechnology, 33, 495-502.
  4. Traag, V.A., Waltman, L. & van Eck, N.J. (2019) From Louvain to Leiden: guaranteeing well-connected communities. Sci Rep 9, 5233.
  5. Xiang, Y., Gubian, S., Suomela, B.P., & Hoeng, J. (2013). Generalized Simulated Annealing for Global Optimization: The GenSA Package. R J., 5, 13.

Contacts

Alexander Wang - aw3436@cumc.columbia.edu

Luca Zanella - lz2841@cumc.columbia.edu

Alessandro Vasciaveo - av2729@cumc.columbia.edu