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