# Voyager

> Voyager

- Skill: `biomate-ai/voyager` (Agent Skill)
- Install (CLI): `npx skillmds@latest add biomate-ai/voyager`
- Raw SKILL.md: https://api.skillmd.com/api/skills/biomate-ai/voyager/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/voyager

---


# Voyager

## Workflows

### Standard Workflow

Perform exploratory spatial data analysis (ESDA) on spatial transcriptomics data, including univariate, bivariate, and multivariate spatial statistics, and visualize the results over tissue geometries.

```r
library(SFEData)
library(SpatialFeatureExperiment)
library(SpatialExperiment)
library(ggplot2)
library(Voyager)
library(scater)
library(scran)
library(pheatmap)

# 1. Load required libraries & 2. Read dataset
sfe <- McKellarMuscleData()

# 3. Add and mirror the tissue image
sfe <- addImg(sfe, imageSource = "tissue_lowres_5a.jpeg", sample_id = "Vis5A", image_id = "lowres", scale_fct = 1024/22208)
sfe <- mirrorImg(sfe, sample_id = "Vis5A", image_id = "lowres")

# 4. Filter spots and normalize counts
sfe <- sfe[, colData(sfe)$in_tissue]
sfe <- logNormCounts(sfe)

# 5. Construct a spatial neighborhood graph
colGraph(sfe, "visium") <- findVisiumGraph(sfe)

# 6. Run univariate spatial statistics (Moran's I and Getis-Ord Gi*)
features_use <- c("Myh1", "Myh2")
sfe <- runUnivariate(sfe, type = "moran", features = features_use, colGraphName = "visium", swap_rownames = "symbol")
sfe <- runUnivariate(sfe, type = "localG", features = features_use, colGraphName = "visium", include_self = TRUE, swap_rownames = "symbol")

# 7. Identify highly variable genes and calculate bivariate spatial correlation (Lee's L)
gs <- modelGeneVar(sfe)
hvgs <- getTopHVGs(gs, fdr.threshold = 0.01)
res <- calculateBivariate(sfe, "lee", hvgs)

# 8. Perform spatially-informed dimension reduction (MULTISPATI PCA)
hvgs2 <- getTopHVGs(gs, n = 1000)
sfe <- runMultivariate(sfe, "multispati", colGraphName = "visium", subset_row = hvgs2, nfposi = 10, nfnega = 10)

# 9. Visualize results
plotSpatialFeature(sfe, c("nCounts", "Myh1"), colGeometryName = "spotPoly", annotGeometryName = "myofiber_simplified", aes_use = "color", swap_rownames = "symbol")
plotLocalResult(sfe, "localG", features = features_use, colGeometryName = "spotPoly", divergent = TRUE, swap_rownames = "symbol")
ElbowPlot(sfe, ndims = 10, nfnega = 10, reduction = "multispati")
spatialReducedDim(sfe, "multispati", ncomponents = 2, divergent = TRUE)
```
*Input: A SpatialFeatureExperiment object and a tissue image. Output: Spatial plots of gene expression, local spatial statistics, eigenvalues, and reduced dimensions.*

## When to Use
- To perform exploratory spatial data analysis (ESDA) on spatial transcriptomics data structured as a `SpatialFeatureExperiment` (SFE) object.
- To compute univariate global and local spatial statistics such as Moran's I (using `runUnivariate` with `type = "moran"`) and Getis-Ord Gi* (using `type = "localG"`).
- To calculate bivariate spatial correlation (e.g., Lee's L via `calculateBivariate`) to identify spatially co-expressed gene modules.
- To run spatially-informed dimension reduction like MULTISPATI PCA (using `runMultivariate` with `type = "multispati"`).

## When NOT to Use
- For non-spatial single-cell RNA-seq data; use standard `SingleCellExperiment` and `scater` instead.
- If you need to perform cell type deconvolution or spatial domain segmentation directly; use specialized packages like `Seurat` or `Giotto`.

## Data Requirements
- A `SpatialFeatureExperiment` (SFE) object containing spatial coordinates, count matrices, and spot/annotation geometries (e.g., `spotPoly`).
- A spatial neighborhood graph constructed and stored in the SFE object (e.g., using `findVisiumGraph`).

## Key Parameters
- **type** ("moran" / "localG" / "lee" / "multispati"): The spatial statistic method to run.
- **colGraphName** ("visium"): The name of the spatial neighborhood graph stored in the SFE object.
- **include_self** (TRUE): For Getis-Ord Gi*, determines whether to include self-directing edges in the spatial graph.
- **swap_rownames** ("symbol"): Column in `rowData` to swap rownames with (e.g., to use human-readable gene symbols instead of Ensembl IDs).
- **nfposi** (10): Number of positive spatial autocorrelation components to retain in MULTISPATI PCA.
- **nfnega** (10): Number of negative spatial autocorrelation components to retain in MULTISPATI PCA.
- **divergent** (TRUE): Logical indicating whether to use a divergent color palette for local spatial statistics or reduced dimensions.

## Best Practices
- Always construct a spatial neighborhood graph using `findVisiumGraph` before running any spatial statistics.
- Mirror the tissue image using `mirrorImg` if the origin of the image (top-left) does not align with the origin of the spots (bottom-left).
- Filter spots to keep only those in tissue (`colData(sfe)$in_tissue`) and normalize counts using `logNormCounts` before analysis.

## Common Pitfalls
- Mismatch between image and spot coordinates: Ensure you call `mirrorImg` to flip the image if coordinates are inverted.
- Missing spatial graph: Forgetting to assign the output of `findVisiumGraph` to `colGraph(sfe, "visium")` will cause downstream spatial functions to fail.

## Alternatives
- `spdep` for classic geospatial analysis.
- `adespatial` for multivariate spatial analysis (MULTISPATI PCA).
- `scater` for non-spatial single-cell exploratory data analysis.

## Citations
- Moses L, Pachter L (2026). Voyager: Exploratory Spatial Data Analysis for Spatial Bioconductor. R package.

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