Global Absence and Targeting of Protective Immune States in Severe COVID-19

This repository contains all code used to process and illustrate figured used in the 2020 Nature Publication.

The repository is organized as

base
  |- auxiliary_files (Various Files used during the analysis)
  |
  |- auxiliary_scripts (Various Scripts sourced during the analysis)
  |
  |- *.R (R scripts)
  |
  +- README.Md (This file)

All code has been thoroughly documented to describe major code functions.

Certain paths to files and scripts may be pointing to the files within our system, but the files/scripts themselves will be within the appropriate auxiliary_* folder within this repository.

All single cell sequencing data is retrievable using the accession GSE163668

Analysis pipeline

1. process_single_library.R --> processes each library to a identify major cell types
2. create_merged_colossal_paper_final.R -> Merged files generated by process_single_library.R to generate a Harmony batch-corrected Seurat object
3. annotate_and_split_merged_colossal_paper_final.R -> Annotated coarse clusters into one of 5 major types (Neutrophil, Mono_MACs_DC, B_Plasma, Platelet , and TNK), and create separate Harmony batch-corrected objects for each. This process was rerun once the individual objects were processed in the next step and noisy cells were identified for removal.
4. process_COMPARTMENT_final.R -> Will process a single compartment object to identify subtypes and ISG scores. Cells identified as junk are passed back to step 3 before producing a final, cleaned compartment-level object.

Citing our work

Combes, A.J., Courau, T., Kuhn, N.F. et al. Global absence and targeting of protective immune states in severe COVID-19. Nature 591, 124–130 (2021). https://doi.org/10.1038/s41586-021-03234-7

Contact Info

Arjun Rao (GH: @arkal, email: ArjunArkal[dot]Rao[at]ucsf[dot]edu) Alexis Combes (email: Alexis[dot]Combes[at]ucsf[dot]edu) Matthew Krummel (email: Matthew[dot]Krummel[at]ucsf[dot]edu)