
Bio Reporting Rmarkdown Reports
- 4 installs
- 1.1k repo stars
- Updated July 25, 2026
- gptomics/bioskills
Create reproducible bioinformatics reports in HTML, PDF, or Word with R Markdown combining code and results.
About
Creates reproducible bioinformatics analysis reports with R Markdown, combining code, results, and visualizations. Developers use it to generate HTML, PDF, or Word reports from R analyses.
- Combine code, results, and visualizations in one document
- Output to HTML, PDF, or Word
Bio Reporting Rmarkdown Reports by the numbers
- 4 all-time installs (skills.sh)
- Ranked #1,625 of 2,064 Data Science & ML skills by installs in the Skillselion catalog
- Data as of Aug 5, 2026 (Skillselion catalog sync)
npx skills add https://github.com/gptomics/bioskills --skill bio-reporting-rmarkdown-reportsAdd your badge
Show developers this skill is listed on Skillselion. Paste this into your README.
| Installs | 4 |
|---|---|
| repo stars | ★ 1.1k |
| Last updated | July 25, 2026 |
| Repository | gptomics/bioskills ↗ |
What it does
Create reproducible bioinformatics reports in HTML, PDF, or Word with R Markdown combining code and results.
Files
R Markdown Reports
Basic Document Structure
---
title: "RNA-seq Analysis Report"
author: "Your Name"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
theme: cosmo
---Setup Chunk
```r ``{r setup, include=FALSE} knitr::opts_chunk$set( echo = TRUE, message = FALSE, warning = FALSE, fig.width = 10, fig.height = 6, fig.align = 'center' ) library(tidyverse) library(DESeq2) library(pheatmap)
Code Chunk Options
```r ``{r analysis, echo=TRUE, results='hide'}
echo: show code
results: 'hide', 'asis', 'markup'
include: FALSE hides chunk entirely
eval: FALSE shows code but doesn't run
cache: TRUE caches results
Parameterized Reports
---
title: "Sample Report"
params:
sample_id: "sample1"
count_file: "counts.csv"
fdr_threshold: 0.05
---```r ``{r} counts <- read.csv(params$count_file) sample <- params$sample_id fdr <- params$fdr_threshold
# Render with parameters
rmarkdown::render('report.Rmd', params = list(sample_id = 'sample2', fdr_threshold = 0.01))
# Batch render
samples <- c('sample1', 'sample2', 'sample3')
for (s in samples) {
rmarkdown::render('report.Rmd', params = list(sample_id = s),
output_file = paste0(s, '_report.html'))
}Tables
```r ``{r}
Basic kable table
knitr::kable(head(results), caption = 'Top DE genes')
Interactive table with DT
library(DT) datatable(results, filter = 'top', options = list(pageLength = 10))
Formatted table with kableExtra
library(kableExtra) results %>% head(10) %>% kable() %>% kable_styling(bootstrap_options = c('striped', 'hover')) %>% row_spec(which(results$padj < 0.01), bold = TRUE, color = 'red')
Figures
```r ``{r volcano-plot, fig.cap="Volcano plot of differential expression"} ggplot(results, aes(log2FoldChange, -log10(pvalue))) + geom_point(aes(color = padj < 0.05)) + theme_minimal()
Inline Code
We identified `r sum(res$padj < 0.05, na.rm=TRUE)` significantly
DE genes (FDR < 0.05) out of `r nrow(res)` tested.Child Documents
---
title: "Main Report"
---```r ``{r child='methods.Rmd'}
PDF Output
---
output:
pdf_document:
toc: true
number_sections: true
fig_caption: true
latex_engine: xelatex
---HTML with Tabs
````r
Results {.tabset}
PCA Plot
```{r} plotPCA(vsd, intgroup = 'condition')
### Heatmappheatmap(assay(vsd)[top_genes, ])
Caching Long Computations
```r ``{r deseq-analysis, cache=TRUE, cache.extra=tools::md5sum('counts.csv')}
Cached unless counts.csv changes
dds <- DESeqDataSetFromMatrix(counts, metadata, ~ condition) dds <- DESeq(dds)
Re-runs when deseq-analysis cache changes
res <- results(dds)
Custom CSS
---
output:
html_document:
css: custom.css
---/* custom.css */
body { font-family: 'Helvetica', sans-serif; }
h1 { color: #2c3e50; }
.figure { margin: 20px auto; }Complete Report Template
```markdown --- title: "RNA-seq Analysis Report" author: "Bioinformatics Core" date: "r Sys.Date()`" output: html_document: toc: true toc_float: true code_folding: hide params: count_file: "counts.csv" metadata_file: "metadata.csv" ---
```{r setup, include=FALSE} knitr::opts_chunk$set(echo = TRUE, message = FALSE, warning = FALSE) library(DESeq2) library(tidyverse) library(pheatmap) library(DT)
## Data Overview
counts <- read.csv(params$count_file, row.names = 1) metadata <- read.csv(params$metadata_file, row.names = 1)
Loaded `r nrow(counts)` genes across `r ncol(counts)` samples.
## Differential Expression
dds <- DESeqDataSetFromMatrix(counts, metadata, ~ condition) dds <- DESeq(dds) res <- results(dds) %>% as.data.frame() %>% arrange(padj)
## Results
datatable(res %>% filter(padj < 0.05), options = list(pageLength = 10))
Related Skills
- reporting/quarto-reports - Modern alternative
- data-visualization/ggplot2-fundamentals - Figure creation
- differential-expression/de-visualization - Analysis plots