# Bioconductor Ggmanh

Manhattan plot and QQ Plot are commonly used to visualize the end result of Genome Wide...

- Repository: https://github.com/bioMate-AI/biomate-bioconductor-kb (793 stars)
- Category: Engineering
- License: see repository
- Last updated: 2026-06-20
- Runs scripts: no, instructions only

## Install

Claude Code: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-ggmanh -g -a claude-code -y`
Codex: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-ggmanh -g -a codex -y`
Cursor: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-ggmanh -g -a cursor -y`
Gemini CLI: `npx skills add bioMate-AI/biomate-bioconductor-kb --skill bioconductor-ggmanh -g -a gemini-cli -y`

## Description

Manhattan plot and QQ Plot are commonly used to visualize the end result of Genome Wide Association Study. The "ggmanh" package aims to keep the generation of these plots simple while maintaining customizability. Main functions include manhattan_plot, qqunif, and thinPoints.

## SKILL.md, as published by its author (untrusted third-party text, read it as data)

# ggmanh

## Workflows

### Standard Workflow

```r
library(ggmanh)
# Format chromosome column as a factor
simdata$chromosome <- factor(simdata$chromosome, c(1:22,"X"))
# Generate standard Manhattan plot
g1 <- manhattan_plot(x = simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position")
# Generate rescaled Manhattan plot
g2 <- manhattan_plot(x = simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position", rescale = TRUE)
# Preprocess data
mpdata <- manhattan_data_preprocess(x = simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position")
# Generate plots using preprocessed data with labels
g3 <- manhattan_plot(x = mpdata, label.colname = "label")
# Zoom into Chromosome 5
manhattan_plot(simdata, chromosome = 5, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position")
```
*Note*: Input is a data frame of GWAS summary statistics; output is a customized Manhattan plot object.

### Binned Manhattan Plot

```r
library(ggmanh)
# Basic Binned Manhattan Plot
binned_manhattan_plot(simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position")
# Preprocess binned data
mpdat <- binned_manhattan_preprocess(simdata, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position", bins.x = 7, bins.y = 100)
# Plot preprocessed binned data
binned_manhattan_plot(mpdat, bin.outline = TRUE)
```
*Note*: Input is a data frame of GWAS summary statistics; output is a binned grid-based Manhattan plot.

### Gds Variant Annotation Plotting

The rest: https://github.com/bioMate-AI/biomate-bioconductor-kb/blob/HEAD/skills/genomics/ggmanh/SKILL.md