{"id":24529032,"url":"https://github.com/saezlab/eccb2022_sc_funcomics","last_synced_at":"2026-01-02T17:08:08.432Z","repository":{"id":77872573,"uuid":"536934239","full_name":"saezlab/eccb2022_sc_funcomics","owner":"saezlab","description":"Functional analysis of single-cell transcriptomics","archived":false,"fork":false,"pushed_at":"2022-09-18T23:14:22.000Z","size":20216,"stargazers_count":7,"open_issues_count":0,"forks_count":5,"subscribers_count":3,"default_branch":"main","last_synced_at":"2025-01-22T07:37:37.731Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"gpl-3.0","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/saezlab.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2022-09-15T08:32:02.000Z","updated_at":"2023-07-18T20:38:33.000Z","dependencies_parsed_at":"2023-03-05T13:45:21.467Z","dependency_job_id":null,"html_url":"https://github.com/saezlab/eccb2022_sc_funcomics","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Feccb2022_sc_funcomics","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Feccb2022_sc_funcomics/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Feccb2022_sc_funcomics/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2Feccb2022_sc_funcomics/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/saezlab","download_url":"https://codeload.github.com/saezlab/eccb2022_sc_funcomics/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":243769947,"owners_count":20345215,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":[],"created_at":"2025-01-22T07:34:29.557Z","updated_at":"2026-01-02T17:08:08.386Z","avatar_url":"https://github.com/saezlab.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Functional analysis of single-cell transcriptomics\n\nRecent advances in omics technologies have led to a rapid increase in the popularity and applications of single-cell data-sets. Standard analyses and workflows solely focus on basic preprocessing steps followed by the identification of differentially expressed genes, and their subsequent use in cell-type annotation and characterization of biological processes. In this tutorial, we show how prior knowledge can be used to extend each of the aforementioned steps, as well as to extract clear biological insights. Furthermore, we provide an introduction to the state-of-the-art intercellular communication methods, as tools for systems-level hypothesis generation tools in single-cell data. We thus cover a diverse set of prior knowledge resources and show how these can be used to support and extend analysis steps ranging from quality control, cell-type annotation and transcription factor and cytokine activity inference. Finally, we show how advanced functional omics analyses can be used to refine cell-cell communication predictions.\n\n## Data Availability\nPlease download the data folder from here:\nhttps://figshare.com/articles/dataset/Tutorial_Data/21152242\n\n\n## 1. Single-cell processing, enrichment and footprint analysis\n\n### Instalation\nTo install the python dependencies run:\n\n```\n# install mamba (faster than conda)\npip install mamba\n# create an environment with the necessary packages\nmamba env create -f scanpy_env.yml --name scanpy\n# activate the environment\nconda activate scanpy\n# add environment as kernel for jupyter-lab\npython -m ipykernel install --user --name=scanpy --display-name='scanpy'\n```\n\nDownload the raw scRNA-seq data and decompress it:\n```\nwget \"https://figshare.com/articles/dataset/Tutorial_Data/21152242\"\nunzip data.zip\n```\nOr alternatively, just download the data from the link\n\n\nThen to start working run:\n```\njupyter-lab\n```\n\n## 2. Cell-cell comunication inference and linking to downstream events\n\n### Installation\n```\nconda activate base\nmamba env create -f seurat_env.yml --name seurat\n```\n\nThen to start working run:\n```\nconda activate seurat\nrstudio\n```\n\n## File Description  \n\n`1_sc_analysis.ipynb` - Jupyter Note book with Part 1  \n\n`2_cell_comm.Rmd` (`2_cell_comm.html`) - R Markdown (and corresponding html) with Part 2  \n\n`scanpy_env.yml` - A yaml file with the conda environment needed for Part 1  \n\n`/src` - directory with figures and helper functions  \n\n`NicheNet_FAQ.md` - Some frequently asked questions (and answers) concerning NicheNet\n\n`nichenet_wrapper.R` - NicheNet wrapper that will be used at the end of `2_cell_comm.Rmd`\n\n\n## Author Names (Alphabetically)\n\nPau Badia i Mompel  \n\nRobin Browaeys  \n\nDaniel Dimitrov  \n\nYvan Saeys  \n\nJulio Saez-Rodriguez  \n\nA join effort by Sae(ys|z) labs!\n\n## Contact us!\nPau - @PauBadiaM; pau.badia(at)uni-heidelberg.de  \n\nDaniel - @DanielBDimitrov; daniel.dimitrov(at)uni-heldeberg.de  \n\nRobin - @RobinBrowaeys; robin.browaeys(at)irc.vib-UGent.be  \n\n\n\n## We are also looking for people. Join us!! :)\n\nPost-doc and PhD position at UKHD\nhttps://saezlab.org/?#jobs\n\n4-year PhD position available at UGent\nyvan.saeys(at)ugent.be\n\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Feccb2022_sc_funcomics","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaezlab%2Feccb2022_sc_funcomics","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Feccb2022_sc_funcomics/lists"}