# Ggmanh

> ggmanh

- Skill: `biomate-ai/ggmanh` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/ggmanh`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/ggmanh/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: biomate-ai (https://skillmd.com/u/biomate-ai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/biomate-ai/ggmanh

---


# 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

```r
library(ggmanh)
# Annotate variants using gds_annotate
simdata_label$label <- gds_annotate(x = simdata_label, annot.method = "position", chr = "chromosome", pos = "position", ref = "Reference", alt = "Alternate")
# Plot annotated data
manhattan_plot(simdata_label, pval.colname = "P.value", chr.colname = "chromosome", pos.colname = "position", label.colname = "label")
```
*Note*: Inputs are a data frame of GWAS summary statistics and a GDS file; output is an annotated Manhattan plot.

## When to Use
- To visualize Genome Wide Association Study (GWAS) results using standard Manhattan plots (`manhattan_plot`).
- To rescale the y-axis of a Manhattan plot when highly significant p-values mask lower-significance patterns (`rescale = TRUE`).
- To create binned grid-based Manhattan plots for extremely large datasets to avoid plotting individual points (`binned_manhattan_plot`).
- To annotate variants with gene/consequence information from a GDS file using `gds_annotate`.

## When NOT to Use
- When using discrete palettes for continuous variables (or vice versa) in `binned_manhattan_plot`, as the plot will fail.

## Data Requirements
- **Input data frame**: Must contain at least three columns representing chromosome, position, and p-value.
- **Chromosome column**: Recommended to be formatted as a factor to avoid ambiguity in plotting order.
- **GDS file**: For variant annotation, a SeqArray-formatted GDS file containing annotations (e.g., `annotation/symbol`, `annotation/consequence`).

## Key Parameters
- **x**: A data.frame, MPdata, or GRanges object containing the GWAS results.
- **pval.colname**: Name of the column containing p-values.
- **chr.colname**: Name of the column containing chromosomes.
- **pos.colname**: Name of the column containing genomic positions.
- **rescale** (FALSE): Logical indicating whether to rescale the y-axis near the significance cutoff.
- **label.colname**: Name of the column containing labels for annotation.
- **chromosome**: Specific chromosome number/name to zoom into.
- **bins.x**: Number of horizontal bins for the widest chromosome in binned plots.

## Best Practices
- Convert the chromosome column to a factor before plotting to ensure correct ordering on the x-axis.
- Preprocess data using `manhattan_data_preprocess` or `binned_manhattan_preprocess` first if you plan to customize the plot multiple times, avoiding redundant computation.
- Set non-significant labels to `""` or `NA` to avoid overlapping labels and extremely slow plotting times.

## Common Pitfalls
- Attempting to label all points: This can cause the plotting process to take hours. Set labels for non-significant points to `""` or `NA`.
- Using incompatible palettes: Ensure continuous palettes are used for continuous variables and discrete palettes for discrete variables in `binned_manhattan_plot`.

## Alternatives
- `qqman` for basic Manhattan and QQ plots.

## References
- Homepage: bioconductor.org/packages/ggmanh
- Vignette: https://bioconductor.org/packages/release/bioc/vignettes/ggmanh/inst/doc/ggmanh.html