EMBL-EBI scRNA Bioinformatics T cell course 2022 (RESULTS)

Repository: Final results for the projects developed during the 'Bioinformatics for T-Cell immunology' course (11-15/07/2022) at EMBL-EBI: [https://www.ebi.ac.uk/training/events/bioinformatics-t-cell-immunology-2022](https://www.ebi.ac.uk/training/events/bioinformatics-t-cell-immunology-2022) Official website: [https://elolab.github.io/Bioinfo_Tcell_projects_22](https://elolab.github.io/Bioinfo_Tcell_projects_22) Maintainer: [Elo lab](https://elolab.utu.fi) Contact: **António Sousa** ([ENLIGHT-TEN+](http://www.enlight-ten.eu) PhD student at the [Medical Bioinformatics Centre](https://elolab.utu.fi), TBC, University of Turku & Åbo Akademi) Last update: 07/07/2022 <br> --- <br> ### Projects This repository hosts the results related with three standalone/independent data analysis projects examples using publicly available data generated and published elsewhere properly referenced below: 1. _Integration of single-cell data from patients developing arthritis arAE under ICI_ + _publication_: [Kim et al., 2022](https://www.nature.com/articles/s41467-022-29539-3) + _data_: GEO [GSE173303](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE173303) + _R markdown notebook_: `01_integration_arthritis_arAE_ICI.Rmd` + _vignette_: [01_integration_arthritis_arAE_ICI.html](https://elolab.github.io/Bioinfo_Tcell_projects_22/pages/01_integration_arthritis_arAE_ICI.html) + _results_ (_in this repository_): GSE173303.tar.gz 2. _Fine-grained clustering of single-cell data of melanoma immune/stroma cells_ + _publication_: [Jerby-Arnon et al., 2018](https://www.sciencedirect.com/science/article/pii/S0092867418311784?via%3Dihub) + _data_: GEO [GSE115978](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE115978) + _R markdown notebook_: `02_clustering_seurat_vs_iloreg.Rmd` + _vignette_: [02_clustering_seurat_vs_iloreg.html](https://elolab.github.io/Bioinfo_Tcell_projects_22/pages/02_clustering_seurat_vs_iloreg.html) + _results_ (_in this repository_): GSE115978.tar.gz 3. _Differential gene expression of stimulated CD4+ T single-cell data with single-cell and pseudobulk methods_ + _publication_: [Cano-Gamez et al., 2020](https://www.nature.com/articles/s41467-020-15543-y) + _data_: [www.opentargets.org](https://www.opentargets.org/projects/effectorness) + _R markdown notebook_: `03_pseudobulks_dge_rots_cd4_act.Rmd` + _vignette_: [03_pseudobulks_dge_rots_cd4_act.html](https://elolab.github.io/Bioinfo_Tcell_projects_22/pages/03_pseudobulks_dge_rots_cd4_act.html) + _results_ (_in this repository_): CanoGamez_et_al_2020.tar.gz <br> --- <br> ### Disclaimer >All the data used along each project notebook was made public elsewhere by the respective authors and it has been properly referenced in each project (proper links were provided along each project notebook). The data and tools chosen to address the topic(s) of each project notebook reflect only my personal experience/knowledge and they were chosen to highlight particular aspects that I consider important. The results generated and explored within each project notebook have just the general purpose of give a brief introduction to the topics addressed in each project and do not aim, at any point, to reproduce or question neither the approaches taken nor the main findings published along with the data sets used herein.