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
where /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 with //home/jovyan/work in the above command.
scTDA can be imported using the command:
import scTDA
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 %matplotlib notebook.
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.]