{"id":24529036,"url":"https://github.com/saezlab/footprintmethods_on_scrnaseq","last_synced_at":"2025-09-12T07:39:42.231Z","repository":{"id":77872581,"uuid":"184066673","full_name":"saezlab/FootprintMethods_on_scRNAseq","owner":"saezlab","description":"Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data","archived":false,"fork":false,"pushed_at":"2020-03-02T18:54:45.000Z","size":13024,"stargazers_count":26,"open_issues_count":0,"forks_count":8,"subscribers_count":2,"default_branch":"master","last_synced_at":"2025-04-14T17:11:24.207Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":"https://doi.org/10.1186/s13059-020-1949-z","language":"R","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}},"created_at":"2019-04-29T12:26:20.000Z","updated_at":"2024-05-22T12:21:47.000Z","dependencies_parsed_at":"2023-03-05T13:45:20.986Z","dependency_job_id":null,"html_url":"https://github.com/saezlab/FootprintMethods_on_scRNAseq","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/saezlab/FootprintMethods_on_scRNAseq","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2FFootprintMethods_on_scRNAseq","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2FFootprintMethods_on_scRNAseq/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2FFootprintMethods_on_scRNAseq/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2FFootprintMethods_on_scRNAseq/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/saezlab","download_url":"https://codeload.github.com/saezlab/FootprintMethods_on_scRNAseq/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/saezlab%2FFootprintMethods_on_scRNAseq/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":274777420,"owners_count":25347645,"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","status":"online","status_checked_at":"2025-09-12T02:00:09.324Z","response_time":60,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"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":"2025-01-22T07:34:30.774Z","updated_at":"2025-09-12T07:39:42.159Z","avatar_url":"https://github.com/saezlab.png","language":"R","funding_links":[],"categories":[],"sub_categories":[],"readme":"## Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data\n\n### Abstract\n\n**Background**\n\nMany functional analysis tools have been developed to extract functional and mechanistic insight from bulk transcriptome data. With the advent of single-cell RNA sequencing (scRNA-seq), it is in principle possible to do such an analysis for single cells. However, scRNA-seq data has characteristics such as drop-out events and low library sizes. It is thus not clear if functional TF and pathway analysis tools established for bulk sequencing can be applied to scRNA-seq in a meaningful way.\n\n**Results**\n\nTo address this question, we perform benchmark studies on simulated and real scRNA-seq data. We include the bulk-RNA tools PROGENy, GO enrichment, and DoRothEA that estimate pathway and transcription factor (TF) activities, respectively, and compare them against the tools SCENIC/AUCell and metaVIPER, designed for scRNA-seq. For the in silico study, we simulate single cells from TF/pathway perturbation bulk RNA-seq experiments. We complement the simulated data with real scRNA-seq data upon CRISPR-mediated knock-out. Our benchmarks on simulated and real data reveal comparable performance to the original bulk data. Additionally, we show that the TF and pathway activities preserve cell type-specific variability by analyzing a mixture sample sequenced with 13 scRNA-seq protocols. We also provide the benchmark data for further use by the community.\n\n**Conclusions**\n\nOur analyses suggest that bulk-based functional analysis tools that use manually curated footprint gene sets can be applied to scRNA-seq data, partially outperforming dedicated single-cell tools. Furthermore, we find that the performance of functional analysis tools is more sensitive to the gene sets than to the statistic used.\n\n\n***\n\n### Availabilty of data\nThe datasets supporting the conclusions of this publication are available at Zenodo:\n\n[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3564179.svg)](https://doi.org/10.5281/zenodo.3564179)\n\nFrom Zenodo you can download these (zipped) folders: \n\n * `data` - Contains raw data for the analyses\n * `output` - Contains intermediate and final results\n \n Please deposit the unzipped folders in the root directory of this R-project.\n \n **Exceptions:**\n \n * The raw Human Cell Atlas data ([`\"data/hca_data/expression_data/sce.all.technologies.RData\"`](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/hca_data_analysis.Rmd#L56)) are not available on Zenodo. Instead the users can work with the normalized data stored in [`\"output/hca_data/expression/norm.rds\"`](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/hca_data_analysis.Rmd#L205). The raw data are accessible on GEO: [GSE133549](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE133549).\n * Only a small fraction of the raw and normalized expression data of the simulated single cells are  avaiable. All data together exceed the limitation of Zenodo's maximal upload size.\n\n***\n\n### How to cite\n\u003e Holland CH, Tanevski J, Perales-Patón J, Gleixner J, Kumar MP, Mereu E, Joughin BA, Stegle O, Lauffenburger DA, Heyn H, Szalai B, Saez-Rodriguez, J. \"Robustness and applicability of transcription factor and pathway analysis tools on single-cell RNA-seq data.\" _Genome Biology._ 2020. DOI: [10.1186/s13059-020-1949-z](https://doi.org/10.1186/s13059-020-1949-z).\n\n***\n\n### Analyses \u0026 Scripts\n#### Testing robustness with respect to low gene coverage (Fig. 1)\nAnalysis script available [here](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/general_robustness.Rmd).\n\n#### In-silico benchmark (Fig. 2)\nAnalysis script available [here](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/in_silico_benchmark.Rmd).\n\n#### In-vitro benchmark (Fig. 3)\nAnalysis script available [here](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/in_vitro_benchmark.Rmd).\n\n#### Analysis of HCA-data (Fig. 4)\nAnalysis script available [here](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/hca_data_analysis.Rmd).\n\n#### Plotting\nWe provide also scripts for plotting [individual](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/plot_figures.Rmd) figures and [collages](https://github.com/saezlab/FootprintMethods_on_scRNAseq/blob/master/analyses/figure_arrangement.Rmd).\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Ffootprintmethods_on_scrnaseq","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fsaezlab%2Ffootprintmethods_on_scrnaseq","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fsaezlab%2Ffootprintmethods_on_scrnaseq/lists"}