singleToxoplasmaSeq

About

This respository includes scripts and jupyter notebooks used to re-generate analysis results of "A single-parasite transcriptional atlas of Toxoplasma gondii reveals novel control of antigen expression" (2020).

Interactive atlas

We have also generated an interactive browser to visualize our scRNA-seq dataset. Visit here

How to use

Git clone this repository:

git clone https://github.com/xuesoso/singleToxoplasmaSeq

Then download the bundled scripts and data to the repository folder in order to regenerate analysis results and figures.

Download data

Decompress the files

tar -xvf Submission_analysis.tar.gz

Optional: Assuming that one has anaconda distribution of python, create an environment for python 3.6.8 (should work on python 3.6+).

conda create -n toxoSeq python=3.6.8 ipykernel
conda activate toxoSeq

Optional: If you just set up a new conda environment, you may need to set up the jupyter kernel as well in order for jupyter notebook to run on this backend.

python -m ipykernel install --user

Now, install all the required python libraries.

pip install -r requirements.txt

Lastly, you can now open up the jupyter notebook and run each cell to regenerate the analysis results.

jupyter-notebook Scripts/figures.ipynb

Data description: See "data_description.csv" for a description of the data files in Submission_analysis/Data/

What is in "Scripts/"

--Scripts ----> figures.ipynb : Jupyter notebook to regenerate figures and analysis results.
    |
    |-------> _loadlib ---> utils/ : A list of utility plotting and analysis functions required. Imported library call name is "sat"
    |           |
    |           |-------> rh07.py : Library and variable definitions for RH (rh07; 384-well) dataset analysis.
    |           |
    |           |-------> me49_011.py : Library and variable definitions for ME49 (me49_011) dataset analysis.
    |           |
    |           |-------> pru0506.py : Library and variable definitions for Pru (pru0506) dataset analysis.
    |           |
    |------> _preprocess -> rh07.py : Preprocessing parameter and plots for RH (rh07; 384-well) dataset analysis.
    |           |
    |           |-------> rh019.py : Preprocessing parameter and plots for RH (rh019; 96-well) dataset analysis.
    |           |
    |           |-------> me49_011.py : Preprocessing parameter and plots for ME49 (me49_011) dataset analysis.
    |           |
    |           |-------> pru0506.py : Preprocessing parameter and plots for Pru (pru0506) dataset analysis.
    |           |
    |           |-------> readme.txt : A textfile with descriptions for each of the dataset.
    |
    |------> analysis_scripts -> cluster_dependence.py : Script to analyze and identify genes with poor co-variation to the underlying embedding.
                    |
                    |----------> align_pru_me49.py : Script to integrate and align ME49 (me49_011) and Pru (pru0506) datasets using Scanorama (Hie, B., Bryson, B. & Berger, B. Nat Biotechnol (2019))
                    |
                    |----------> Bradley_GRAs.csv : A comma-separated list of identified GRA genes.