scTDA is an object oriented python library for topological data analysis of high-throughput single-cell RNA-seq data. It includes tools for the preprocessing, analysis, and exploration of single-cell RNA-seq data based on topological representations.
To install scTDA run:
pip install scTDA
Alternatively, to install the most updated version you can download the source code and run:
python setup.py install
For optimal visualization results it is strongly recommended to have Graphviz tools and PyGraphviz installed.
A Docker container with a fully configured jupyter notebook environment and scTDA can be obtained running:
docker pull pcamara/sctda
To start the image use:
docker run -it -v /path/to/your/working/directory:/home/jovyan/work --rm -p 8888:8888 pcamara/sctda
/path/to/your/working/directory is the folder containing the data you want to analyze. In some systems it may be required replacing
//home/jovyan/work in the above command.
scTDA can be imported using the command:
A tutorial illustrating the basic scTDA workflow can be found in
doc/scTDA Tutorial.html. The source notebook and data files for the
tutorial can be downloaded here. For optimal visualization when working with notebooks, we recommend using
More details on the scTDA algorithm can be found in:
Rizvi, A. H.*, Camara, P. G.*, Kandror, E. K., Roberts, T. J., Scheiren, I., Maniatis, T., and Rabadan, R., "Single-Cell Topological RNA-Seq Analysis Reveals Insights Into Cellular Differentiation and Development", Nat. Biotechnol. (2017) 35: 551-560. [* These authors contributed equally to this work.]