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
To get the original scRNA-seq data, you need to go to the Gene Expressio Omnibus. There are three datasets use to analyze from GEO, (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.
Dataset (1) --- 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
Dataset (2) --- 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
Dataset (3) --- 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
To accomidate for lack of memory on certain machines, such as the Virtual Machines used in the VEME workshop, we have a downsampled version of the cell x gene expresion matrix in a Google Drive:
Downsampled Data --- https://drive.google.com/file/d/1cJPAidSgQHPNYDbnHvk3Ob7WuMcbK9mI/view Download this dataset from the Google Drive
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