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"description": "<h1>\n <picture>\n <source media=\"(prefers-color-scheme: dark)\" srcset=\"docs/images/nf-core-tfactivity_logo_dark.png\">\n <img alt=\"nf-core/tfactivity\" src=\"docs/images/nf-core-tfactivity_logo_light.png\">\n </picture>\n</h1>\n\n[](https://github.com/nf-core/tfactivity/actions/workflows/nf-test.yml)\n[](https://github.com/nf-core/tfactivity/actions/workflows/linting.yml)[](https://nf-co.re/tfactivity/results)[](https://doi.org/10.5281/zenodo.XXXXXXX)\n[](https://www.nf-test.com)\n\n[](https://www.nextflow.io/)\n[](https://github.com/nf-core/tools/releases/tag/3.3.2)\n[](https://docs.conda.io/en/latest/)\n[](https://www.docker.com/)\n[](https://sylabs.io/docs/)\n[](https://cloud.seqera.io/launch?pipeline=https://github.com/nf-core/tfactivity)\n\n[](https://nfcore.slack.com/channels/tfactivity)[](https://bsky.app/profile/nf-co.re)[](https://mstdn.science/@nf_core)[](https://www.youtube.com/c/nf-core)\n\n## Introduction\n\n**nf-core/tfactivity** is a bioinformatics pipeline that can identify the most differentially active transcription factors (TFs) between multiple conditions. It takes a count matrix and open chromatin data (ATAC-seq, DNase-seq, HM-ChIP-seq) as input. It produces a ranking of transcription factors.\n\n\n\n1. Identify accessible regions (can perform footprinting between close ChIP-seq peaks or take ATAC-seq peaks)\n2. Calculate affinity scores for combinations of transcription factors and target genes (TGs) using [STARE](https://doi.org/10.1093/bioinformatics/btad062)\n3. Identify differentially expressed genes between conditions\n4. Utilize linear regression to identify the transcription factors that are most likely to be responsible for the differential gene expression\n5. Calculate the TF-TG score based on:\n 1. Differential expression of the target genes\n 2. Affinity of the transcription factors to the target genes\n 3. The regression coefficient of the transcription factors\n6. Perform a Mann-Whitney U test and create a ranking of the transcription factors\n\nA more biological visualization of the workflow can be found here:\n\n> [!NOTE]\n> The following image was created for the TF-Prioritizer publication. Parts of the workflow have been adapted for the nf-core pipeline, but the general idea is still valid.\n\n\n\n## Usage\n\n> [!NOTE]\n> If you are new to Nextflow and nf-core, please refer to [this page](https://nf-co.re/docs/usage/installation) on how to set-up Nextflow. Make sure to [test your setup](https://nf-co.re/docs/usage/introduction#how-to-run-a-pipeline) with `-profile test` before running the workflow on actual data.\n\nThe pipeline supports processing of previously called peaks from ATAC-seq, DNase-seq, or histone modification ChIP-seq data. The peaks can then either be used as-is or be subjected to footprinting analysis. Additionally, BAM files can be provided in a separate samplesheet, which will be used to predict enhancer regions.\n\n```csv title=\"samplesheet.csv\"\nsample,condition,assay,peak_file\ncondition1_H3K27ac_1,condition1,H3K27ac,condition1_H3K27ac_1.broadPeak\ncondition1_H3K27ac_2,condition1,H3K27ac,condition1_H3K27ac_2.broadPeak\ncondition1_H3K4me3,condition1,H3K4me3,condition1_H3K4me3.broadPeak\ncondition2_H3K27ac,condition2,H3K27ac,condition2_H3K27ac.broadPeak\ncondition3_H3K27ac,condition3,H3K27ac,condition3_H3K27ac.broadPeak\ncondition3_H3K4me3,condition3,H3K4me3,condition3_H3K4me3.broadPeak\n```\n\nEach row represents a peak file. The `sample` column should contain a unique identifier for each peak file. The `peak_file` column should contain the path to the peak file. Peak files need to be in a format that is compatible with the `bed` format. Only the first three columns of the `bed` format are used.\n\n```csv title=\"samplesheet_bam.csv\"\nsample,condition,assay,signal,control\ncondition1_H3K27ac_1,condition1,H3K27ac,condition1_H3K27ac_1.bam,condition1_control.bam\ncondition1_H3K27ac_2,condition1,H3K27ac,condition1_H3K27ac_2.bam,condition1_control.bam\ncondition1_H3K4me3,condition1,H3K4me3,condition1_H3K4me3.bam,condition1_control.bam\ncondition2_H3K27ac,condition2,H3K27ac,condition2_H3K27ac.bam,condition2_control.bam\ncondition3_H3K27ac,condition3,H3K27ac,condition3_H3K27ac.bam,condition3_control.bam\ncondition3_H3K4me3,condition3,H3K4me3,condition3_H3K4me3.bam,condition3_control.bam\n```\n\nThe first three columns are the same as in the peak file samplesheet. The `signal` column should contain the path to the signal BAM file. The `control` column should contain the path to the control BAM file.\n\nSecond, you need a raw count matrix (e.g. from [nf-core/rnaseq](https://nf-co.re/rnaseq)) with gene IDs as rows and samples as columns. You also need a design matrix that specifies the conditions of the samples in the count matrix. The design matrix should look as follows:\n\n```csv title=\"design_matrix.csv\"\nsample,condition\nsample1,condition1\nsample2,condition1\nsample3,condition2\nsample4,condition3\n```\n\nThe `sample` column should match the columns in the expression matrix. The `condition` column is needs to match the `condition` column in the samplesheet. Additionally,batches can be added to the design matrix and will be considered in the differential expression analysis.\n\n:::tip\nThere is an alternative way of providing expression values. Instead of providing a single count matrix for all samples, you can provide a gene list and one count file per sample. Details can be found in the [usage documentation](https://nf-co.re/tfactivity/usage).\n:::\n\nNow, you can run the pipeline using:\n\n```bash\nnextflow run nf-core/tfactivity \\\n -profile <docker/singularity/.../institute> \\\n --input samplesheet.csv \\\n --genome GRCh38 \\\n --counts <EXPRESSION_MATRIX> \\\n --counts_design design_matrix.csv \\\n --outdir <OUTDIR>\n```\n\n> [!WARNING]\n> Please provide pipeline parameters via the CLI or Nextflow `-params-file` option. Custom config files including those provided by the `-c` Nextflow option can be used to provide any configuration _**except for parameters**_; see [docs](https://nf-co.re/docs/usage/getting_started/configuration#custom-configuration-files).\n\nFor more details and further functionality, please refer to the [usage documentation](https://nf-co.re/tfactivity/usage) and the [parameter documentation](https://nf-co.re/tfactivity/parameters).\n\n## Pipeline output\n\nTo see the results of an example test run with a full size dataset refer to the [results](https://nf-co.re/tfactivity/results) tab on the nf-core website pipeline page.\nFor more details about the output files and reports, please refer to the\n[output documentation](https://nf-co.re/tfactivity/output).\n\n## Credits\n\nnf-core/tfactivity was originally written by [Nico Trummer](https://github.com/nictru).\n\nWe thank the following people for their extensive assistance in the development of this pipeline:\n\n- [Markus Hoffmann](https://scholar.google.com/citations?user=_qXUS28AAAAJ) (project and scientific management)\n- [Leon Hafner](https://www.linkedin.com/in/leon-hafner/) (implementations)\n\n## Contributions and Support\n\nIf you would like to contribute to this pipeline, please see the [contributing guidelines](.github/CONTRIBUTING.md).\n\nFor further information or help, don't hesitate to get in touch on the [Slack `#tfactivity` channel](https://nfcore.slack.com/channels/tfactivity) (you can join with [this invite](https://nf-co.re/join/slack)).\n\n## Citations\n\nnf-core/tfactivity is based on the previously published \"TF-Prioritizer\" pipeline. As long as there is no dedicated nf-core/tfactivity publication, please cite the following paper:\n\n> **TF-Prioritizer: a Java pipeline to prioritize condition-specific transcription factors**\n>\n> Markus Hoffmann, Nico Trummer, Leon Schwartz, Jakub Jankowski, Hye Kyung Lee, Lina-Liv Willruth, Olga Lazareva, Kevin Yuan, Nina Baumgarten, Florian Schmidt, Jan Baumbach, Marcel H Schulz, David B Blumenthal, Lothar Hennighausen & Markus List\n>\n> GigaScience, Volume 12, 2023, giad026, https://doi.org/10.1093/gigascience/giad026\n\n<!-- TODO nf-core: Add citation for pipeline after first release. Uncomment lines below and update Zenodo doi and badge at the top of this file. -->\n<!-- If you use nf-core/tfactivity for your analysis, please cite it using the following doi: [10.5281/zenodo.XXXXXX](https://doi.org/10.5281/zenodo.XXXXXX) -->\n\nAn extensive list of references for the tools used by the pipeline can be found in the [`CITATIONS.md`](CITATIONS.md) file.\n\nYou can cite the `nf-core` publication as follows:\n\n> **The nf-core framework for community-curated bioinformatics pipelines.**\n>\n> Philip Ewels, Alexander Peltzer, Sven Fillinger, Harshil Patel, Johannes Alneberg, Andreas Wilm, Maxime Ulysse Garcia, Paolo Di Tommaso & Sven Nahnsen.\n>\n> _Nat Biotechnol._ 2020 Feb 13. doi: [10.1038/s41587-020-0439-x](https://dx.doi.org/10.1038/s41587-020-0439-x).\n",
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