COVID19-RAGE

scRNA-seq analysis of PBMC from COVID-19 patients of the Padova cohort

The code and the data in this repository enables the reproduction of the analyses and plots for the single-cell RNA-seq data in the paper "RAGE engagement by SARS-CoV-2 enables monocyte infection and underlies COVID-19 severity" (https://doi.org/10.1101/2022.05.22.492693).

An html version of the notebooks is accessible here.

Filtering, dimensionality reduction, cell annotation

Links: jupyter notebook and html file.

Notebook that containes the preliminary steps of the single-cell data analysis

1. initial quality filters
2. log-normalization of the counts and HVG selection
3. dimensionality reduction (PCA and UMAP) with Harmony integration
4. Leiden clustering and celltype annotation
5. preliminary analysis and visualization of the annotated data

Differential abundance analysis

Links: html file.

RMarkdown code for the computation of the differential abundance of the cell families that have been defined in the dataset.

Data exploration and ingestion of external annotations

Links: jupyter notebook and html file.

Plots of the relevant cell metadata and comparison with ingested cell annotations based on Wilk et al.

Pseudobulk data computation

Links: jupyter notebook and html file.

Computation of the pseudobulk data for each patient sample

Differential expression analysis

Links: html file.

Differential expression analysis of the pseudobulk data with edgeR GL model

RAGE pathway exploration

Links: jupyter notebook and html file.

Supervised exploration of the expression of the genes included in the RAGE binding gene list

External dataset summary

Links: jupyter notebook and html file.

Summary of the results of the RAGE exploration performed on the publicly available datasets of single-cell RNA-seq of COVID-19 patients

Detailed analisys workflows are described in the following notebooks:

• Arunachalam et al. 2020
• Silvin et al. 2020
• Yao et al. 2021
• Bost et al. 2021
• Stephenson et al. 2021
• Yu et al. 2020
• Combes et al. 2021
• Su et al. 2020
• Zhu et al. 2020
• Lee et al. 2020
• Wen et al. 2020
• Schulte-Schrepping et al. 2020
• Wilk et al. 2020

Drug repurposing analysis

Links: folder.

Analysis of the transcriptomic signatures based on the Connectivity Map (CMap) database. This resource has been used to:

• detect whether current COVID-19 therapeutics are were able to mimic the transcriptional changes occurring from in the subjects of the study from admission to recovery (01_known_drugs.R);
• determine which drugs included in the cMap database are able to interfere specifically with the RAGE pathway determined from Ingenuity Pathway Analysis (02_RAGE_IPA.R);
• carry our an unbiased analysis of the CMap database, seeking compounds potentially capable of reverting severity signatures across cell families (03_cMAP_enrich_trt_unbiased.R);
• retrieve gene expression changes upon a specific treatment from level 4 cMAP data (04_zscore_fromGSE.R).

---

Note: this README file has been generated automatically.
Please do not modify it directly but instead work on this config file.