bio-reporting-rmarkdown-reports

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---

name: bio-reporting-rmarkdown-reports

description: Create reproducible bioinformatics analysis reports with R Markdown including code, results, and visualizations in HTML, PDF, or Word format. Use when generating analysis reports with RMarkdown.

tool_type: r

primary_tool: rmarkdown

measurable_outcome: Execute skill workflow successfully with valid output within 15 minutes.

allowed-tools:

• read_file
• run_shell_command

---

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

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

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
---

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

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

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"
---

PDF Output

---
output:
  pdf_document:
    toc: true
    number_sections: true
    fig_caption: true
    latex_engine: xelatex
---

HTML with Tabs

## Results {.tabset}

### PCA Plot

plotPCA(vsd, intgroup = 'condition')

### Heatmap

pheatmap(assay(vsd)[top_genes, ])

Caching Long Computations

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

---
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"
---

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

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