This repository has been developed to reproduce the results in McCurdy, Ntranos, and Pachter (2017, BIORXIV/2017/159079). We implement the deterministic column subset selection (DCSS) algorithm (Papailiopoulos, Kyrillidis, and Boutsidis, 2014). We integrate the DCSS method and three comparision thresholding methods into two workflows for clustering (clustering_on_transcript_compatibility_counts and wishbone) and apply it to two single-cell RNA-Seq datasets (Paul et al., 2015; Zeisel et al. 2015). The code in this repository will download the datasets for you.

Installing the dependencies

To run this code you will need to install all of the dependencies for clustering_on_transcript_compatibility_counts and wishbone. Please see the respective README.md files. We used the anaconda package manager which can be obtained at https://www.continuum.io/downloads, and we include the two .yml files that we used for the analyses. You can simply create an "conda env" by

conda env create -f env.yml 
#
# To activate this environment, use:
# $ source activate env
#
# To deactivate this environment, use:
# $ source deactivate
#

Getting Started

In order to run the code, first clone this repository and the submodules.

git clone --recursive https://github.com/srmcc/dcss_single_cell.git

Then activate your conda environment:

source activate wishbone

or

source activate clustering

Change directory to the cloned repository:

cd to /path/to/dcss_single_cell/src/dcss/

And run the analysis files.

python analysis_wishbone.py

or

python analysis_clustering.py

The user experience and documentation for this package are still under construction.