acdc-py 1.0.2

A package to quickly identify unbiased graph-based clusterings via parameter optimization in Python

acdc_py 🤘

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.

• Several graph-based clustering algorithms are available within acdc_py, including Leiden and Louvain.
• 2 optimization routines for parameter tuning are available, Grid Search and(generalized) Simulated Annealing.
• Optimization variables include the number of nearest neighbors, k, resolution, res, and the number of principal components, PCs.
• Several objective functions are available, including the Silhouette Score (default).

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 - [e-mail]

Luca Zanella - [e-mail]

Alessandro Vasciaveo - [e-mail]