{"id":48590511,"url":"https://github.com/openanalytics/davis","last_synced_at":"2026-04-08T19:03:22.550Z","repository":{"id":340223361,"uuid":"807697554","full_name":"openanalytics/daVis","owner":"openanalytics","description":null,"archived":false,"fork":false,"pushed_at":"2026-02-23T20:36:10.000Z","size":509,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":2,"default_branch":"master","last_synced_at":"2026-02-24T02:58:51.199Z","etag":null,"topics":[],"latest_commit_sha":null,"homepage":null,"language":"R","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/openanalytics.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"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,"zenodo":null,"notice":null,"maintainers":null,"copyright":null,"agents":null,"dco":null,"cla":null}},"created_at":"2024-05-29T15:47:09.000Z","updated_at":"2026-02-23T20:36:14.000Z","dependencies_parsed_at":null,"dependency_job_id":null,"html_url":"https://github.com/openanalytics/daVis","commit_stats":null,"previous_names":["openanalytics/davis"],"tags_count":null,"template":false,"template_full_name":null,"purl":"pkg:github/openanalytics/daVis","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openanalytics%2FdaVis","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openanalytics%2FdaVis/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openanalytics%2FdaVis/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openanalytics%2FdaVis/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/openanalytics","download_url":"https://codeload.github.com/openanalytics/daVis/tar.gz/refs/heads/master","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/openanalytics%2FdaVis/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":286080680,"owners_count":31569400,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-04-08T14:31:17.711Z","status":"ssl_error","status_checked_at":"2026-04-08T14:31:17.202Z","response_time":54,"last_error":"SSL_connect returned=1 errno=0 peeraddr=140.82.121.5:443 state=error: 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":[],"created_at":"2026-04-08T19:03:01.963Z","updated_at":"2026-04-08T19:03:22.544Z","avatar_url":"https://github.com/openanalytics.png","language":"R","readme":"daVis: Visualization of differential expression analysis output\n================\nKatarzyna Górczak, Laure Cougnaud\n2026-01-29\n\n# Introduction\n\nThe `daVis` package contains utility functions to visualize the output from \ndifferential expression analysis. The input data can be a model, a \nlist of top tables, or a combination of these two. The model can be of class \n`MArrayLM` (limma), `DGELRT` (edgeR), or `DESeqResults` (DESeq2).\n\n## Installation\n\n### 1\\. Download the package from Bioconductor\n\n```r\nif (!requireNamespace(\"BiocManager\", quietly = TRUE))\n  install.packages(\"BiocManager\")\n\nBiocManager::install(\"daVis\")\n```\n\nNOTE: to install development version direct from GitHub:\n```r\nif (!requireNamespace(\"devtools\", quietly = TRUE))\n  install.packages(\"devtools\")\n\ndevtools::install_github(\"openanalytics/daVis\")\n```\n\n### 2\\. Load the package into R session\n\n```r\nlibrary(daVis)\n```\n\n# Example data\n\nThe `createExampleData()` function \nwill download and create an example input data for the visualizations. \n\nMore information about the experiment can be found \n[here](https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE60450).\n\n```r\ntmpDir \u003c- tempfile(); dir.create(tmpDir)\nexampleData \u003c- createExampleData(path = tmpDir, output = c(\"limma\", \"edgeR\", \"deseq2\", \"topTable\"))\nres.limma \u003c- exampleData$limma\nres.edger \u003c- exampleData$edgeR\nres.deseq \u003c- exampleData$DESeq2\ntopTableList \u003c- exampleData$topTable\n```\n\n# Visualizations\n\n## Volcano plot\n\nThe `daVolcanoPlot()` function \nenables a quick visual identification of the size and significance of the \nfeature expression effects (top left/right). The significance of the effect is \nrepresented by the raw p-value on the y axis, so highly significant features \nare at the top of the plot. The size of the effect is represented by the log of \nthe fold change (negative/positive for down/up-regulation), so features with \nhigh effects are at the right/left side of the plot. Below, the color scale \nis used for adjusted p-values (corrected for multiple testing across genes).\n\n```r\ncoefs \u003c- c(\"B.LvsP\", \"L.LvsP\")\n\ndaVolcanoPlot(input = res.limma, \n              coef = coefs, \n              coefLabel = c(\"A\", \"B\"),\n              facetNCol = 2,\n              colorVar = \"adj.P.Val\",\n              topGenes = 5,\n              topGenesVar = \"SYMBOL\")\n```\n\n![Figure 1: daVolcanoPlot](man/figures/01_volcanoplot.png)\n\n## Log-ratio plot\n\nThe `daLogRatioPlot()` function \nrepresents the differential effect (e.g. treatment versus control) for several \nconditions (e.g., compounds or concentrations) of the experiment (log FC scale). \nThis enables to visualize a bigger subset of genes. The significance of genes\ncan be represented via colored rows, e.g., red denotes significant genes, \nwhile gray indicates non-significant genes.\n\n```r\ncoefs \u003c- c(\"B.LvsP\", \"L.LvsP\", \"B.PvsV\", \"L.PvsV\")\n\ndaLogRatioPlot(input = res.limma,\n               featuresVar = c(\"SYMBOL\", \"GENENAME\"),\n               coef = coefs,\n               coefLabel = c(\"A\", \"B\", \"C\", \"D\"),\n               facetNCol = 4,\n               errorBars = TRUE)\n```\n\n![Figure 2: daLogRatioPlot](man/figures/02_logratioplot.png)\n\n## Heatmap\n\nThe `daHeatmapLogFC()` function \nrepresents the differential effect (e.g. treatment versus control) for several \nconditions (e.g., compounds or concentrations) of the experiment. It is a \ngraphical representation of the individual values contained in a matrix as \ncolors. This enables to visualize a bigger subset of genes. The gene label can \nbe colored indicating for example the significance of genes, e.g., black color \ndenotes significant genes, while gray represents non-significant genes.\n\n```r\ncoefs \u003c- c(\"B.LvsP\", \"L.LvsP\", \"B.PvsV\", \"L.PvsV\")\n\ndaHeatmapLogFC(input = topTableList,\n               featuresIdVar = \"ENTREZID\",\n               featuresVar = c(\"SYMBOL\", \"GENENAME\"),\n               featuresMaxNChar = 35,\n               coef = coefs,\n               coefLabel = c(\"A\", \"B\", \"C\", \"D\"))\n```\n\n![Figure 3: daHeatmapLogFC](man/figures/03_heatmap.png)\n\n## Upset plot\n\nThe `daUpset()` function \nis used to represent the overlap (intersection) or difference of the sets of \nsignificant genes, down or up-regulated separately, between different \ndifferential effects. The different shades of blue are indicative for the \nnumber of differential effects (sharing these up- or down-regulated genes).\n\n```r\ncoefs \u003c- c(\"B.LvsP\", \"L.LvsP\", \"B.PvsV\", \"L.PvsV\")\n\ndaUpset(input = topTableList,\n        coef = coefs,\n        featuresIdVar = \"SYMBOL\",\n        fdr = 0.05,\n        dir = \"up\",\n        ylab = paste(\"Number of (shared) significantly \\n\", \n                     \"up-regulated genes\"),\n        xlab = paste(\"Number of significantly \\n\", \n                     \"up-regulated genes\"))\n```\n\n![Figure 4: daUpset](man/figures/04_upset.png)\n\nGet feature identifiers from each overlapping set with `returnAnalysis = TRUE`.\n\n## Scatter plot\n\nThe `daScatterPlot()` function \nvisualizes the comparison of the logFC for different differential effects.\n\n```r\ncoefs \u003c- c(\"B.LvsP\", \"L.LvsP\")\n\ndaScatterPlot(input = topTableList,\n              coef = coefs,\n              coefLabel = c(\"A\", \"B\"),\n              featuresIdVar = \"ENTREZID\",\n              topGenes = 10,\n              topGenesVar = \"SYMBOL\",\n              correlation = TRUE)\n```\n\n![Figure 5: daScatterPlot](man/figures/05_scatterplot.png)\n\n## Waterfall plot\n\nThe `daWaterfallPlot()` function \nvisualizes the logFC for different differential effects colored by adjusted \np-value.\n\n```r\ndaWaterfallPlot(input = res.limma,\n                coef = \"B.LvsP\",\n                coefLabel = \"A\",\n                featuresIdVar = \"ENTREZID\",\n                featuresVar = \"SYMBOL\",\n                colorVar = \"adj.P.Val\",\n                color = c(\"#e52323\", \"#32a6d3\"),\n                fillVar = \"adj.P.Val\",\n                fill = c(\"#e52323\", \"#32a6d3\"),\n                typePlot = \"static\")\n```\n\n![Figure 6: daWaterfallPlot](man/figures/06_waterfallplot.png)\n\n## MA plot\n\nThe `daMAplot()` function \nvisualizes the logFC versus log2 mean expression.\n\n```r\ncoefs \u003c- c(\"B.LvsP\", \"L.LvsP\")\n\ndaMAplot(input = res.limma, \n         coef = coefs, \n         coefLabel = c(\"A\", \"B\"),\n         featuresIdVar = \"ENTREZID\",\n         topGenes = 5,\n         topGenesVar = \"SYMBOL\",\n         direction = TRUE, \n         color = c(\"steelblue\", \"firebrick\", \"grey\"),\n         facetNCol = 2)\n```\n\n![Figure 7: daMAplot](man/figures/07_maplot.png)\n\n## Significant genes barplot\n\nThe `daSignificantGenesBarplot()` function \nvisualizes the number of significant genes per comparison.\n\n```r\ncoefs \u003c- c(\"B.LvsP\", \"L.LvsP\", \"B.PvsV\", \"L.PvsV\")\n\ndaSignificantGenesBarplot(input = res.limma, \n                          coef = coefs, \n                          coefLabel = c(\"A\", \"B\", \"C\", \"D\"),\n                          addPercentage = TRUE)\n```\n\n![Figure 8: daSignificantGenesBarplot](man/figures/08_barplot.png)\n","funding_links":[],"categories":[],"sub_categories":[],"project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenanalytics%2Fdavis","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fopenanalytics%2Fdavis","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fopenanalytics%2Fdavis/lists"}