Quarto Reports
Basic Document
title: "Analysis Report" author: "Your Name" date: today format: html: toc: true code-fold: true theme: cosmo
Python Document
title: "scRNA-seq Analysis" format: html jupyter: python3
import scanpy as sc
import matplotlib.pyplot as plt
adata = sc.read_h5ad('data.h5ad')
sc.pl.umap(adata, color='leiden')
R Document
title: "DE Analysis" format: html
library(DESeq2)
dds <- DESeqDataSetFromMatrix(counts, metadata, ~ condition)
dds <- DESeq(dds)
Multiple Formats
title: "Multi-format Report" format: html: toc: true pdf: documentclass: article docx: reference-doc: template.docx
Render all formats
quarto render report.qmd
Render specific format
quarto render report.qmd --to pdf
Parameters
title: "Parameterized Report" params: sample: "sample1" threshold: 0.05
Render with parameters
quarto render report.qmd -P sample:sample2 -P threshold:0.01
Tabsets
::: {.panel-tabset}
PCA
plotPCA(vsd)
Heatmap
pheatmap(mat)
:::
Callouts
::: {.callout-note} This is an important note about the analysis. :::
::: {.callout-warning} Check your input data format before proceeding. :::
::: {.callout-tip} Use caching for long computations. :::
Cross-References
See @fig-volcano for the volcano plot.
#| label: fig-volcano
#| fig-cap: "Volcano plot showing DE genes"
ggplot(res, aes(log2FC, -log10(pvalue))) + geom_point()
Results are summarized in @tbl-summary.
#| label: tbl-summary
#| tbl-cap: "Summary statistics"
knitr::kable(summary_df)
Code Cell Options
#| echo: true
#| warning: false
#| fig-width: 10
#| fig-height: 6
#| cache: true
import scanpy as sc
sc.pl.umap(adata, color='leiden')
Inline Code
We found {python} len(sig_genes) significant genes.
We found {r} nrow(sig) significant genes.
Presentations
title: "Analysis Results" format: revealjs
Slide 1
Content here
Slide 2 {.smaller}
More content with smaller text
Quarto Projects
_quarto.yml
project: type: website output-dir: docs
website: title: "Analysis Portal" navbar: left: - href: index.qmd text: Home - href: methods.qmd text: Methods - href: results.qmd text: Results
Bibliography
bibliography: references.bib csl: nature.csl
Gene expression analysis was performed using DESeq2 [@love2014].
References
Freeze Computations
_quarto.yml
execute: freeze: auto # Only re-run when source changes
Include Files
{{< include _methods.qmd >}}
Diagrams with Mermaid
flowchart LR
A[Raw Data] --> B[QC]
B --> C[Alignment]
C --> D[Quantification]
D --> E[DE Analysis]
Multi-Language Document
title: "R + Python Analysis"
Load in R:
library(reticulate)
counts <- read.csv('counts.csv')
Process in Python:
import pandas as pd
counts_py = r.counts # Access R object
Related Skills
-
reporting/rmarkdown-reports - R-focused alternative
-
data-visualization/ggplot2-fundamentals - R visualizations
-
workflow-management/snakemake-workflows - Pipeline integration