scRNA-seq data analysis for figures appearing in the following publication:
Diaz-Cuadros M*, Wagner DE*, Budjan C, Hubaud A, Tarazona OA, Donelly S, Michaut A, Al Tanoury Z, Yoshioka-Kobayashi K, Niino Y, Kageyama R, Miyawaki A, Touboul J & Pourquié O. In vitro characterization of the human segmentation clock. Nature 2020. doi.org/10.1038/s41586-019-1885-9.
Start by cloning this repository using git:
git clone https://github.com/wagnerde/Diaz2019.git
cd Diaz2019
scRNA-seq data files are too large to include within this Github repo, and must be downloaded separately. Using the command line, download and unzip the Diaz et. al. 2019 scRNA-seq data. The unzipped '_rawData' directory should then reside in the 'Diaz2019' directory that we just created:
wget https://kleintools.hms.harvard.edu/paper_websites/diaz_2019/Diaz2019_inDropsCountsTables.zip --no-check-certificate
unzip Diaz2019_inDropsCountsTables.zip
Create a Python 3.6 conda environment to manage the required software packages:
conda create --name py36 python=3.6 -y
Activate the environment:
source activate py36
Begin installing packages with conda and pip:
conda install -y -q seaborn scikit-learn statsmodels numba pytables
conda install -y -q -c conda-forge python-igraph louvain jupyterlab leidenalg
conda install -y -q -c bioconda bbknn
pip install scanpy fa2 MulticoreTSNE
Install Scrublet:
git clone https://github.com/AllonKleinLab/scrublet.git
cd scrublet
pip install -r requirements.txt
pip install --upgrade .
cd ..
Activate the py36 environment and run Jupyter Lab. Within Jupyter lab, navigate to one of the dataset folders (mmE95, mmES, hsIPS), and open the .ipynb file.
source activate py36
jupyter lab