Published December 10, 2021
| Version Version 1.0.0
Dataset
Open
Complement activation induces excessive T cell cytotoxicity in severe COVID-19: Analysis of single cell data cohort 1 (Berlin).
Authors/Creators
- 1. (1) Department of Infectious Diseases and Respiratory Medicine, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany
- 2. (2) Institute of Pathology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 10117 Berlin, Germany. (3) IRI Life Sciences, Humboldt-Universität zu Berlin, 10117 Berlin, Germany
- 3. (4) Genomics and Immunoregulation, Life and Medical Sciences (LIMES) Institute, University of Bonn, 53115 Bonn, Germany, (5) Systems Medicine, Deutsches Zentrum für Neurodegenerativen Erkrankungen (DZNE), 53127 Bonn, Germany
- 4. (6) Institute of Medical Immunology, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, 13353 Berlin, Germany, (7) Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
Description
This repository contains the R Markdown files with the analysis of CyTOF and scRNA-seq data corresponding to cohort 1 (Berlin) analysed in Georg et al. 2021 "Complement activation induces excessive T cell cytotoxicity in severe COVID-19". Additionally, here we include the necessary CyTOF data to reproduce this analysis.
CyTOF data:
- The debarcoded fcs files (before batch-correction) can be found in https://flowrepository.org/id/FR-FCM-Z4P5. \
- Here you can find the necessary data to reproduce the analysis (cytof_analysis.Rmd, cytof_analysis.html):
- data_norm_all.csv: single-cell protein expression data (after batch-normalization and in linear scale).
- data_Tcells_annotated.csv: single-cell protein expression of gated T cells with cluster annotation.
- phenograph_CD4_k30.csv, phenograph_CD8_k30.csv, phenograph_TCRgd_k30.csv: output from Louvain Clustering computed with PhenoGraph (https://github.com/jacoblevine/PhenoGraph) per T cell compartment.
- clusterannotation.csv: annotation for each cluster and metacluster
scRNA-seq data:
- The raw data can be found in https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE175450
- Other files to reproduce the analysis (scRNAseq_analysis_1preprocessing.Rmd, scRNAseq_analysis_2clustering.Rmd, scRNAseq_analysis_3convalescent.Rmd):
- scRNAseq_Sawitzki_RECAST_09_2021.xlsx: Single-cell metadata.
- scRNAseq_samples.tsv: Samples metadata.
- scRNAseq_genelist_annotation.xlsx: Gene list for the annotation of T cells (Also in Mendeley, see Data and Code Availability).
- scRNAseq_GO_RESPONSE_TO_TYPE_I_INTERFERON.txt, scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt, , scRNAseq_GO_T_CELL_MEDIATED_CYTOTOXICITY.txt: Gene lists for the signatures “Response to Type I Interferon” , “Defense Response to virus” and “Cytotoxicity” used for GSEA. (Also in Table S2).
- scRNAseq_traj18_trav10.txt,scRNAseq_trbv25.txt: sequences to determine the proportion of TRAV10-TRAJ18-TRBV25 pairing T cell clones across all T cell clusters.
Files
scRNAseq_GO_DEFENSE_RESPONSE_TO_VIRUS.txt
Files
(6.2 GB)
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