{"id":34730774,"url":"https://github.com/showteeth/debpeak","last_synced_at":"2026-04-01T16:57:49.185Z","repository":{"id":117973250,"uuid":"509293733","full_name":"showteeth/DEbPeak","owner":"showteeth","description":"DEbPeak - Analyze and integrate multi-omics to unravel the regulation of gene expression.","archived":false,"fork":false,"pushed_at":"2025-09-11T08:47:59.000Z","size":99362,"stargazers_count":8,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-03-27T23:42:08.827Z","etag":null,"topics":["analysis","atac-seq","chip-seq","count-matrix","differential-expression","geo","integration","m6a-seq","peak-related","rna-seq","transcription-factors","visualization"],"latest_commit_sha":null,"homepage":"","language":"HTML","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"other","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/showteeth.png","metadata":{"files":{"readme":"README.md","changelog":"NEWS.md","contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":"CODE_OF_CONDUCT.md","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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2022-07-01T02:19:18.000Z","updated_at":"2025-09-09T02:19:52.000Z","dependencies_parsed_at":"2023-12-30T03:34:36.695Z","dependency_job_id":"2223fa92-9b5f-4339-9aa5-e9dd342c2cfa","html_url":"https://github.com/showteeth/DEbPeak","commit_stats":null,"previous_names":[],"tags_count":2,"template":false,"template_full_name":null,"purl":"pkg:github/showteeth/DEbPeak","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/showteeth%2FDEbPeak","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/showteeth%2FDEbPeak/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/showteeth%2FDEbPeak/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/showteeth%2FDEbPeak/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/showteeth","download_url":"https://codeload.github.com/showteeth/DEbPeak/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/showteeth%2FDEbPeak/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31290538,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-01T13:12:26.723Z","status":"ssl_error","status_checked_at":"2026-04-01T13:12:25.102Z","response_time":53,"last_error":"SSL_read: unexpected eof while reading","robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":false,"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":["analysis","atac-seq","chip-seq","count-matrix","differential-expression","geo","integration","m6a-seq","peak-related","rna-seq","transcription-factors","visualization"],"created_at":"2025-12-25T02:57:05.150Z","updated_at":"2026-04-01T16:57:49.163Z","avatar_url":"https://github.com/showteeth.png","language":"HTML","funding_links":[],"categories":[],"sub_categories":[],"readme":"# DEbPeak - Analyze and integrate multi-omics to unravel the regulation of gene expression.\n\n\u003cimg src = \"man/figures/DEbPeak.png\" align = \"right\" width = \"200\"/\u003e\n\n![License](https://img.shields.io/badge/license-GPL--3.0-blue.svg)\n[![CODE\\_SIZE](https://img.shields.io/github/languages/code-size/showteeth/DEbPeak.svg)](https://github.com/showteeth/DEbPeak)\n\n## Introduction\n`DEbPeak` aims to **explore**, **visualize**, **interpret** multi-omics data and **unravel the regulation of gene expression** by combining RNA-seq with peak-related data (eg: ChIP-seq, ATAC-seq, m6a-seq et al.). It contains **eleven functional modules**:\n\n* **Parse GEO**: Extract study information, raw count matrix and metadata from GEO database.\n* **Quality Control (QC)**: QC on count matrix and samples. \n  - QC on count matrix: Proportion of genes detected in different samples under different CPM thresholds and the saturation of the number of genes detected.\n  - QC on samples: Euclidean distance and pearson correlation coefficient of samples across different conditions, sample similarity on selected principal components (check batch information and conduct batch correction) and outlier detection with robust PCA.\n* **Principal Component Analysis (PCA)**: this module can be divided into three sub modules, basic info, loading related and 3D visualization.\n  - Basic info: scree plot (help to select the useful PCs), biplot (sample similarity with corresponding genes with larger loadings) and PC pairs plot (sample similarity under different PC combinations).\n  - Loading related: visualize genes with larger positive and negative loadings on selected PCs, conduct GO enrichment analysis on genes with larger positive and negative loadings on selected PCs.\n  - 3D visualization: visualize samples on three selected PCs.\n* **Differential Analysis and Visualization**: this module includes seven powerful visualization methods (Volcano Plot, Scatter Plot, MA Plot, Rank Plot, Gene/Peak Plot, Heatmap, Pie Plot for peak-related data).\n* **Functional Enrichment Analysis (FEA)**: GO enrichment analysis, KEGG enrichment analysis, Gene Set Enrichment Analysis (GSEA).\n  - GO (Biological Process, Molecular Function, Cellular Component) and KEGG on differential expression genes or accessible/binding peaks.\n  - GSEA on all genes (Notice: GSEA is not available for peak-related data)\n* **Predict transcription factors (PredictTFs)**: Identify transcription factors with differentially expressed genes, `DEbPeak` provides three methods ([BART](https://academic.oup.com/bioinformatics/article/34/16/2867/4956015?login=false), [ChEA3](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6602523) and [TFEA.ChIP](https://academic.oup.com/bioinformatics/article/35/24/5339/5538988)).\n* **Motif analysis**:\n  - *de novo* motif discovery\n  - motif enrichment\n* **Integrate RNA-seq with peak-related data**: \n  - Get consensus peaks: For multiple peak files, get consensus peaks; for single peak file, use it directly (used in consensus integration mode).\n  - Peak profile plots: Heatmap of peak binding to TSS regions, Average Profile of ChIP peaks binding to TSS region, Profile of ChIP peaks binding to different regions (used in consensus integration mode).\n  - Peak annotaion (used in consensus integration mode).\n  - Integrate RNA-seq with peak-related data (consensus mode): Integrate RNA-seq with peak-related data to find direct targets, including up-regulated and down-regulated.\n  - Integrate RNA-seq with peak-related data (differential mode): Integrate RNA-seq and peak-related data based on differential analysis.\n  - Integration summary: include venn diagram and quadrant diagram (differential mode).\n  - GO enrichment on integrated results.\n  - Find motif on integrated results: Due to the nature of ATAC-seq, we usually need to find motif on integrated results to obtain potential regulatory factors.\n* **Integrate RNA-seq with RNA-seq**:\n  - Integration summary: include venn diagram and quadrant diagram.\n  - GO enrichment on integrated results.\n* **Integrate peak-related data with peak-related data**:\n  - Integration summary: include venn diagram and quadrant diagram (differential mode).\n  - GO enrichment on integrated results.\n* **Utils**: useful functions, including creating enrichment plot for selected enrichment terms, gene ID conversion and count normalization(DESeq2’s median of ratios, TMM, CPM, TPM, RPKM).\n\nTo enhance the ease of use of the tool, we have also developed an **web server** for `DEbPeak` that allows users to submit files to the web page and set parameters to get the desired results. Unlike the standalone R package, *the web server has built-in `DESeq2` for differential analysis*, while the R package can accept user input results from `DESeq2` or `edgeR`, which will be **more flexible**.\n\nBy the way, all plots generated are **publication-ready** , and most of them are based on `ggplot2`, so that users can easily modify them according to their needs. We also provide **various color palettes**, including **discrete** and **continuous**, **color blind friendly** and **multiple categorical variables**.\n\n\u003chr /\u003e\n\n## Citation\n\nIf you use [DEbPeak](https://showteeth.github.io/DEbPeak/) in published research, please cite:\n\n* Hou J\u003csup\u003e\\#\u003c/sup\u003e, **Song Y**\u003csup\u003e\\#\u003c/sup\u003e, Xiao C\u003csup\u003e\\#\u003c/sup\u003e, Sun Y, Shen J, Ma X, Zhou Q, Chiu SC, Xu Y, Huang Y, Chen YG, Zhu X\u003csup\u003e\\*\u003c/sup\u003e, Wang J\u003csup\u003e\\*\u003c/sup\u003e, Xiong JW\u003csup\u003e\\*\u003c/sup\u003e. Cloche/Npas4l is a pro-regenerative platelet factor during zebrafish heart regeneration. **Dev Cell**. 2025 Jun 24:S1534-5807(25)00370-3. doi: 10.1016/j.devcel.2025.06.015. Epub ahead of print. PMID: 40602409.\n\n\u003chr /\u003e\n\n## Framework\n\n\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"man/figures/DEbPeak_outline.png\"  title=\"DEbPeak_framework\"  alt=\"DEbPeak_framework\" /\u003e\n\u003c/div\u003e\n\n\u003chr /\u003e\n\n## Application scenarios for multi-omics integration\n\n\u003cdiv align=\"center\"\u003e\n\u003cimg src=\"man/figures/DEbPeak_scenarios.png\"  title=\"DEbPeak_scenarios\"  alt=\"DEbPeak_scenarios\" /\u003e\n\u003c/div\u003e\n\n\u003chr /\u003e\n\n## Installation\n### R package\nYou can install the package via the Github repository:\n\n``` r\n# install.packages(\"devtools\")   #In case you have not installed it.\n\n# install prerequisites for enrichplot and ChIPseeker\ndevtools::install_version(\"ggfun\", version = \"0.0.6\", repos = \"https://cran.r-project.org\")\ndevtools::install_version(\"aplot\", version = \"0.1.6\", repos = \"https://cran.r-project.org\")\ndevtools::install_version(\"scatterpie\", version = \"0.1.7\", repos = \"https://cran.r-project.org\")\n\n# For mac, you may need to install xquartz: brew install --cask xquartz\n\n# install DEbPeak\ndevtools::install_github(\"showteeth/DEbPeak\")\n```\n\nIn general, it is **recommended** to install from [Github repository](https://github.com/showteeth/DEbPeak) (update more timely).\n\nFor other issues about installation, please refer [Installation](https://github.com/showteeth/scfetch/blob/main/INSTALL.md#general-solution) guide.\n\nInstall additional tools:\n```bash\n# install MSPC --- consensus peak\nwget --quiet https://github.com/Genometric/MSPC/releases/latest/download/linux-x64.zip -O MSPC_linux_x64.zip \u0026\u0026 unzip -q MSPC_linux_x64.zip -d mspc \u0026\u0026 cd mspc \u0026\u0026 chmod +x mspc\n\n# install meme --- motif anaysis\n## install from source\ncd /opt \u0026\u0026 wget --quiet https://meme-suite.org/meme/meme-software/5.5.5/meme-5.5.5.tar.gz -O meme-5.5.5.tar.gz \u0026\u0026 tar -zxf meme-5.5.5.tar.gz \u0026\u0026 cd meme-5.5.5 \u0026\u0026 ./configure --prefix=`pwd`/meme-5.5.5/meme --enable-build-libxml2 --enable-build-libxslt \u0026\u0026 make \u0026\u0026 make install\n## install from conda: conda install -c bioconda meme\n\n# install homer --- motif enrichment\n## install from source\nmkdir homer \u0026\u0026 cd homer \u0026\u0026 wget --quiet http://homer.ucsd.edu/homer/configureHomer.pl -O configureHomer.pl \u0026\u0026 chmod +x configureHomer.pl \u0026\u0026 perl configureHomer.pl -install\n## install from conda: conda install -c bioconda homer\n## Downloading Homer Packages: http://homer.ucsd.edu/homer/introduction/install.html\n\n# install deeptools and bart\npip install deeptools numpy pandas scipy tables scikit-learn matplotlib\nwget --quiet https://virginia.box.com/shared/static/031noe820hk888qzcxvw1cazol1gdhi0.gz -O bart_v2.0.tar.gz \u0026\u0026 tar -zxf bart_v2.0.tar.gz\n## Download the resources and setup the configuration file\n## https://zanglab.github.io/bart/index.htm#install\n```\n\n\u003chr /\u003e\n\n### Docker\nWe also provide a [docker image](https://hub.docker.com/repository/docker/soyabean/debpeak) to use:\n\n```bash\n# pull the image\ndocker pull soyabean/debpeak:1.2\n\n# run the image\ndocker run --rm -p 8888:8787 -e PASSWORD=passwd -e ROOT=TRUE -it soyabean/debpeak:1.2\n```\n\n**Notes**:\n\n* After running the above codes, open browser and enter `http://localhost:8888/`, the user name is `rstudio`, the password is `passwd` (set by `-e PASSWORD=passwd`)\n* If port `8888` is in use, change `-p 8888:8787`\n* The `meme suit` path: `/opt/meme-5.5.5/meme/bin`.\n* The `homer suit` path: `/opt/homer/bin`.\n* The `configureHomer.pl` path: `/opt/homer`.\n* The `bart` path: `/opt/bart_v2.0/bin`\n* You still need to **download the resources and setup the configuration file for [bart](https://zanglab.github.io/bart/index.htm#install)** and **download species packages for [homer](http://homer.ucsd.edu/homer/introduction/install.html)**.\n\n\u003chr /\u003e\n\n\n\n## Usage\n### Vignette\n\nDetailed usage is available in [here](https://showteeth.github.io/DEbPeak/). We divide these vignettes into four categories:\n\n* For parse **GEO**:\n  * [Parse GEO](https://showteeth.github.io/DEbPeak/articles/ParseGEO.html)\n\n* For analyzing **RNA-seq**:\n  * [Quality Control](https://showteeth.github.io/DEbPeak/articles/QualityControl.html)\n  * [Principal Component Analysis (RNA-seq)](https://showteeth.github.io/DEbPeak/articles/PrincipalComponentAnalysis.html)\n  * [Differential Expression Analysis (RNA-seq)](https://showteeth.github.io/DEbPeak/articles/DifferentialExpressionAnalysis.html)\n  * [Functional Enrichment Analysis (RNA-seq)](https://showteeth.github.io/DEbPeak/articles/FunctionalEnrichmentAnalysis.html)\n  * [Predict Transcription Factors (RNA-seq)](https://showteeth.github.io/DEbPeak/articles/IdentifyRegulator.html)\n  * [Utils](https://showteeth.github.io/DEbPeak/articles/Utils.html)\n\n* For analyzing **peak-related data**:\n  * [Quality Control](https://showteeth.github.io/DEbPeak/articles/QualityControl.html)\n  * [Principal Component Analysis (Peak-related)](https://showteeth.github.io/DEbPeak/articles/PrincipalComponentAnalysisPeak.html)\n  * [Differential Analysis (Peak-related)](https://showteeth.github.io/DEbPeak/articles/DifferentialExpressionAnalysisPeak.html)\n  * [Functional Enrichment Analysis (Peak-related)](https://showteeth.github.io/DEbPeak/articles/FunctionalEnrichmentAnalysisPeak.html)\n  * [Motif Analysis (Peak-related)](https://showteeth.github.io/DEbPeak/articles/MotifEnrichment.html)\n\n* **Integrating RNA-seq (differential expression analysis) with peak-related data (consensus peak)**:\n  * [Integrate RNA-seq with ChIP-seq (consensus peak)](https://showteeth.github.io/DEbPeak/articles/IntegrateChIP.html)\n  * [Integrate RNA-seq with ATAC-seq (consensus peak)](https://showteeth.github.io/DEbPeak/articles/IntegrateATAC.html)\n  * [Integrate RNA-seq ChIP-seq and ATAC-seq](https://showteeth.github.io/DEbPeak/articles/IntegrateChIPATAC.html)\n\n* **Integrating RNA-seq (differential expression analysis) with peak-related data (differential accessible/binding analysis)**:\n  * [Integrate RNA-seq with ATAC-seq (differential analysis)](https://showteeth.github.io/DEbPeak/articles/IntegrateATACDE.html)\n  * [Integrate RNA-seq with ChIP-seq (differential analysis)](https://showteeth.github.io/DEbPeak/articles/IntegrateChIPDE.html).\n\n\n### Function list\n\n\u003ctable\u003e\n\u003cthead\u003e\n  \u003ctr\u003e\n    \u003cth\u003eType\u003c/th\u003e\n    \u003cth\u003eFunction\u003c/th\u003e\n    \u003cth\u003eDescription\u003c/th\u003e\n    \u003cth\u003eKey packages\u003c/th\u003e\n  \u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eParse GEO\u003c/td\u003e\n    \u003ctd\u003eParseGEO\u003c/td\u003e\n    \u003ctd\u003eExtract study information, raw count matrix and metadata from GEO database\u003c/td\u003e\n    \u003ctd\u003eGEOquery\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd rowspan=\"4\"\u003eQuality Control\u003c/td\u003e\n    \u003ctd\u003eCountQC\u003c/td\u003e\n    \u003ctd\u003eQuality control on count matrix (gene detection sensitivity and sequencing depth saturation)\u003c/td\u003e\n    \u003ctd\u003eNOISeq\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eSampleRelation\u003c/td\u003e\n    \u003ctd\u003eQuality control on samples (sample clustering based on euclidean distance and pearson correlation coefficient)\u003c/td\u003e\n    \u003ctd\u003estats\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eOutlierDetection\u003c/td\u003e\n    \u003ctd\u003eDetect outlier with robust PCA\u003c/td\u003e\n    \u003ctd\u003errcov\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eQCPCA\u003c/td\u003e\n    \u003ctd\u003ePCA related functions used in quality control (batch detection and correction, outlier detection)\u003c/td\u003e\n    \u003ctd\u003estats, sva, rrcov\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd rowspan=\"6\"\u003ePrincipal Component Analysis\u003c/td\u003e\n    \u003ctd\u003ePCA\u003c/td\u003e\n    \u003ctd\u003eConduct principal component analysis\u003c/td\u003e\n    \u003ctd\u003estats\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003ePCABasic\u003c/td\u003e\n    \u003ctd\u003eGenerated PCA baisc plots, including screen plot, biplot and pairs plot\u003c/td\u003e\n    \u003ctd\u003ePCAtools\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eExportPCGenes\u003c/td\u003e\n    \u003ctd\u003eExport genes of selected PCs\u003c/td\u003e\n    \u003ctd\u003etidyverse\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eLoadingPlot\u003c/td\u003e\n    \u003ctd\u003ePCA loading plot, including bar plot and heatmap\u003c/td\u003e\n    \u003ctd\u003eggplot2, ComplexHeatmap\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eLoadingGO\u003c/td\u003e\n    \u003ctd\u003eGO enrichment on PC’s loading genes\u003c/td\u003e\n    \u003ctd\u003eclusterProfiler\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003ePCA3D\u003c/td\u003e\n    \u003ctd\u003eCreate 3D PCA plot\u003c/td\u003e\n    \u003ctd\u003eplot3D\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd rowspan=\"9\"\u003eDifferential Analysis\u003c/td\u003e\n    \u003ctd\u003eExtractDA\u003c/td\u003e\n    \u003ctd\u003eExtract differential analysis results\u003c/td\u003e\n    \u003ctd\u003etidyverse\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eVolcanoPlot\u003c/td\u003e\n    \u003ctd\u003eVolcanoPlot for differential analysis results\u003c/td\u003e\n    \u003ctd\u003eggplot2\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eScatterPlot\u003c/td\u003e\n    \u003ctd\u003eScatterPlot for differential analysis results\u003c/td\u003e\n    \u003ctd\u003eggplot2\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eMAPlot\u003c/td\u003e\n    \u003ctd\u003eMA-plot for differential analysis results\u003c/td\u003e\n    \u003ctd\u003eggplot2\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eRankPlot\u003c/td\u003e\n    \u003ctd\u003eRank plot for differential analysis results\u003c/td\u003e\n    \u003ctd\u003eggplot2\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eGenePlot\u003c/td\u003e\n    \u003ctd\u003eGene expresion or peak accessibility/binding plot\u003c/td\u003e\n    \u003ctd\u003eggplot2\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eDEHeatmap\u003c/td\u003e\n    \u003ctd\u003eHeatmap for differential analysis results\u003c/td\u003e\n    \u003ctd\u003eComplexHeatmap\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eDiffPeakPie\u003c/td\u003e\n    \u003ctd\u003eStat genomic regions of differential peaks with pie plot\u003c/td\u003e\n    \u003ctd\u003eggpie\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eConductDESeq2\u003c/td\u003e\n    \u003ctd\u003eConduct differential analysis with DESeq2\u003c/td\u003e\n    \u003ctd\u003eNOISeq, stats, sva, rrcov, PCAtools, DESeq2, ggplot2, ComplexHeatmap, clusterProfiler, plot3D, tidyverse\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd rowspan=\"3\"\u003eFunctional Enrichment Analysis\u003c/td\u003e\n    \u003ctd\u003eConductFE\u003c/td\u003e\n    \u003ctd\u003eConduct functional enrichment analysis (GO and KEGG)\u003c/td\u003e\n    \u003ctd\u003eclusterProfiler\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eConductGSEA\u003c/td\u003e\n    \u003ctd\u003eConduct gene set enrichment analysis (GSEA)\u003c/td\u003e\n    \u003ctd\u003eclusterProfiler\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eVisGSEA\u003c/td\u003e\n    \u003ctd\u003eVisualize GSEA results\u003c/td\u003e\n    \u003ctd\u003eenrichplot\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd rowspan=\"2\"\u003ePredict Transcription Factors\u003c/td\u003e\n    \u003ctd\u003eInferRegulator\u003c/td\u003e\n    \u003ctd\u003ePredict TFs\u0026nbsp;\u0026nbsp;\u0026nbsp;from RNA-seq data with ChEA3, BART2 and TFEA.ChIP\u003c/td\u003e\n    \u003ctd\u003eChEA3, BART2,\u0026nbsp;\u0026nbsp;\u0026nbsp;TFEA.ChIP\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eVizRegulator\u003c/td\u003e\n    \u003ctd\u003eVisualize the\u0026nbsp;\u0026nbsp;\u0026nbsp;Identified TFs\u003c/td\u003e\n    \u003ctd\u003eggplot2\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd rowspan=\"3\"\u003eMotif Analysis\u003c/td\u003e\n    \u003ctd\u003eMotifEnrich\u003c/td\u003e\n    \u003ctd\u003eMotif enrichment for differentially accessible/binding peaks\u003c/td\u003e\n    \u003ctd\u003eHOMER\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eMotifDiscovery\u003c/td\u003e\n    \u003ctd\u003ede novo motif discovery with STREME\u003c/td\u003e\n    \u003ctd\u003eMEME\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eMotifCompare\u003c/td\u003e\n    \u003ctd\u003eMap motifs against a motif database with Tomtom\u003c/td\u003e\n    \u003ctd\u003eMEME\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd rowspan=\"5\"\u003ePeak-related Analysis\u003c/td\u003e\n    \u003ctd\u003ePeakMatrix\u003c/td\u003e\n    \u003ctd\u003ePrepare count matrix and sample metadata for peak-related data\u003c/td\u003e\n    \u003ctd\u003eDiffBind, ChIPseeker\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eGetConsensusPeak\u003c/td\u003e\n    \u003ctd\u003eGet consensus peak from replicates\u003c/td\u003e\n    \u003ctd\u003eMSPC\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003ePeakProfile\u003c/td\u003e\n    \u003ctd\u003eVisualize peak accessibility/binding profile\u003c/td\u003e\n    \u003ctd\u003eChIPseeker\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eAnnoPeak\u003c/td\u003e\n    \u003ctd\u003eAssign peaks with the genomic binding region and nearby genes\u003c/td\u003e\n    \u003ctd\u003eChIPseeker\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003ePeakAnnoPie\u003c/td\u003e\n    \u003ctd\u003eVisualize peak annotation results with pie plot\u003c/td\u003e\n    \u003ctd\u003eggpie\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd rowspan=\"8\"\u003eIntegrate RNA-seq with Peak-related Data\u003c/td\u003e\n    \u003ctd\u003eDEbPeak\u003c/td\u003e\n    \u003ctd\u003eIntegrate differential expression results and peak annotation/differential analysis results.\u003c/td\u003e\n    \u003ctd\u003etidyverse\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eDEbPeakFE\u003c/td\u003e\n    \u003ctd\u003eGO enrichment on integrated results\u003c/td\u003e\n    \u003ctd\u003eclusterProfiler\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eDEbCA\u003c/td\u003e\n    \u003ctd\u003eIntegrate differential expression results and peak annotation results (two kinds of peak-related data)\u003c/td\u003e\n    \u003ctd\u003etidyverse\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eProcessEnhancer\u003c/td\u003e\n    \u003ctd\u003eGet genes near differential peaks\u003c/td\u003e\n    \u003ctd\u003eIRanges\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eInteVenn\u003c/td\u003e\n    \u003ctd\u003eCreate a Venn   diagram for integrated results (support DEbPeak, DEbDE, PeakbPeak)\u003c/td\u003e\n    \u003ctd\u003eggvenn\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eInteDiffQuad\u003c/td\u003e\n    \u003ctd\u003eCreate quadrant diagram for differential expression analysis of RNA-seq and peak-related data\u003c/td\u003e\n    \u003ctd\u003eggplot2\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eNetViz\u003c/td\u003e\n    \u003ctd\u003eVisualize\u0026nbsp;\u0026nbsp;\u0026nbsp;enhancer-gene network results\u003c/td\u003e\n    \u003ctd\u003eigraph,\u0026nbsp;\u0026nbsp;\u0026nbsp;ggnetwork\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eFindMotif\u003c/td\u003e\n    \u003ctd\u003eFind motif on integrated results\u003c/td\u003e\n    \u003ctd\u003eHOMER\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd rowspan=\"2\"\u003eIntegrate RNA-seq with RNA-seq\u003c/td\u003e\n    \u003ctd\u003eDEbDE\u003c/td\u003e\n    \u003ctd\u003eIntegrate Two Differential Expression Results\u003c/td\u003e\n    \u003ctd\u003etidyverse\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eDEbDEFE\u003c/td\u003e\n    \u003ctd\u003eGO Enrichment on Two Differential Expression Integration Results.\u003c/td\u003e\n    \u003ctd\u003eclusterProfiler\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd rowspan=\"2\"\u003eIntegrate Peak-related Data with Peak-related Data\u003c/td\u003e\n    \u003ctd\u003ePeakbPeak\u003c/td\u003e\n    \u003ctd\u003eIntegrate Two Peak Annotation/Differential Analysis Results.\u003c/td\u003e\n    \u003ctd\u003etidyverse\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003ePeakbPeakFE\u003c/td\u003e\n    \u003ctd\u003eGO Enrichment on Two Peak Annotation/Differential Analysis Integration Results.\u003c/td\u003e\n    \u003ctd\u003eclusterProfiler\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd rowspan=\"4\"\u003eUtils\u003cbr\u003e\u003cbr\u003e\u003c/td\u003e\n    \u003ctd\u003eEnrichPlot\u003c/td\u003e\n    \u003ctd\u003eCreate a bar or\u0026nbsp;\u0026nbsp;\u0026nbsp;dot plot for selected functional enrichment analysis results (GO and KEGG)\u003c/td\u003e\n    \u003ctd\u003eggplot2\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eIDConversion\u003c/td\u003e\n    \u003ctd\u003eGene ID conversion between ENSEMBL ENTREZID SYMBOL\u003c/td\u003e\n    \u003ctd\u003eclusterProfiler\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eGetGeneLength\u003c/td\u003e\n    \u003ctd\u003eGet gene length from GTF\u003c/td\u003e\n    \u003ctd\u003eGenomicFeatures, GenomicRanges\u003c/td\u003e\n  \u003c/tr\u003e\n  \u003ctr\u003e\n    \u003ctd\u003eNormalizedCount\u003c/td\u003e\n    \u003ctd\u003ePerform counts normalization (DESeq2’s median of ratios, TMM, CPM, RPKM, TPM)\u003c/td\u003e\n    \u003ctd\u003eDESeq2, edgeR, tidyverse\u003c/td\u003e\n  \u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003chr /\u003e\n\n## Notice\n\n* The **KEGG API** has changed, to perform KEGG enrichment, you'd better update `clusterProfiler` \u003e= `4.7.1`. \n\n\u003chr /\u003e\n\n## Contact\nFor any question, feature request or bug report please write an email to songyb0519@gmail.com.\n\n\u003chr /\u003e\n\n## Code of Conduct\nPlease note that the DEbPeak project is released with a [Contributor Code of Conduct](https://contributor-covenant.org/version/2/0/CODE_OF_CONDUCT.html). By contributing to this project, you agree to abide by its terms.\n\n\u003chr /\u003e\n\n\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshowteeth%2Fdebpeak","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fshowteeth%2Fdebpeak","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fshowteeth%2Fdebpeak/lists"}