This respository provides the Jupyter Notebooks for analyzing the single cell RNA-seq data produce by Melms et al. in https://www.nature.com/articles/s41586-021-03569-1. This study analyzes lung tissue in patients who passed from COVID-19 and analyzes immune response at a cellular level.
Assuming you have git installed on your machine, in your terminal run ...
git clone https://github.com/zwallace425/veme
cd veme
The single cell data that these notebooks use are large and so are not deposited into the git repo directly. To get the data, you need to go to the Gene Expression Omnibus. There are three datasets these notebooks use, (1) raw counts from a single lung sample, (2) fully processes cell by gene matrix with all aggregated samples, and (3) the metadata file annotating all the cell types and clusters.
Download dataset (1) at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSM5226581
Scroll down to the bottom and download the http version of GSM5226581_L01cov_raw_counts.csv.gz
Download dataset (2) at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171524
Scroll down to the botomm and download the http version of GSE171524_processed_data.csv.gz
Download dataset (3) at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE171524
Scroll down to the bottom and download http version of GSE171524_lung_metaData.txt.gz
Deposit these datasets into your veme folder that was just cloned. Please DO NOT uncompress
these datasets. They will be uncompressed in memory when analyzing.
Assuming you have Juypter Lab installed on your machine, in the veme folder run ...
source env_setup.sh
Juypter Lab