# Despace

> DESpace

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

---


# DESpace

## Workflows

### Standard Workflow

Identify spatially variable genes (SVGs) across tissue regions globally or within individual spatial clusters for single or multiple biological replicates.

```r
library(DESpace)
library(SpatialExperiment)

# Assuming spe3 is a SpatialExperiment object with spatial clusters in 'layer_guess_reordered'
results <- svg_test(spe = spe3, cluster_col = "layer_guess_reordered", verbose = TRUE)

# Test individual clusters using pre-computed dispersions
cluster_results <- individual_svg(spe3, edgeR_y = results$estimated_y, cluster_col = "layer_guess_reordered")

# Combine results
merge_res <- top_results(results$gene_results, cluster_results)
```
*Inputs are a SpatialExperiment object with annotated spatial clusters; outputs are gene-level and cluster-level spatial variability statistics.*

### Differential Spatial Patterns

Identify genes with differential spatial expression patterns (DSPs) across multiple experimental conditions or time points.

```r
library(DESpace)
library(SpatialExperiment)

# Assuming spe.combined is a combined SpatialExperiment object with multiple samples
multi_results <- svg_test(spe = spe.combined, 
                          cluster_col = "layer_guess_reordered", 
                          sample_col = "sample_id", 
                          replicates = TRUE)

# Test individual clusters across replicates
cluster_results <- individual_svg(spe.combined, 
                                  edgeR_y = multi_results$estimated_y, 
                                  replicates = TRUE, 
                                  cluster_col = "layer_guess_reordered")

# Combine results
merge_res <- top_results(multi_results$gene_results, cluster_results, select = "FDR")
```
*Inputs are a combined multi-sample SpatialExperiment object; outputs are joint gene-level and cluster-level spatially variable gene statistics across replicates.*

## When to Use
- Identifying spatially variable genes (SVGs) from spatial transcriptomics data using `svg_test`.
- Testing spatial variability within individual spatial clusters using `individual_svg`.
- Jointly modeling multiple biological replicates to find consistent spatial patterns using `replicates = TRUE` in `svg_test`.
- Combining gene-level and cluster-level results to filter for highly or lowly abundant SVGs using `top_results`.

## When NOT to Use
- For spatial clustering itself, use `BayesSpace` or `stLearn` because `DESpace` requires pre-annotated spatial clusters as input.
- For single-cell RNA-seq differential expression without spatial coordinates, use standard `edgeR` or `DESeq2` because `DESpace` is designed for spatially resolved transcriptomics.

## Data Requirements
- **Input Format**: A `SpatialExperiment` or `SingleCellExperiment` object.
- **Structure**: Must contain spatial coordinates (e.g., `array_row`, `array_col`) and pre-annotated spatial clusters (e.g., `layer_guess_reordered`) in `colData`.
- **Normalization**: Raw count matrix in the `counts` assay (modeled via negative binomial in `edgeR`).

## Key Parameters
- **spe**: The input `SpatialExperiment` or `SingleCellExperiment` object.
- **cluster_col**: Column name in `colData(spe)` containing spatial clusters.
- **sample_col**: Column name in `colData(spe)` containing sample IDs (for multi-sample).
- **replicates** (`FALSE`): Logical indicating whether to fit the multi-sample model.
- **edgeR_y**: Pre-computed `DGEList` object containing dispersion estimates to speed up `individual_svg`.
- **verbose** (`TRUE`): Logical to print progress and statistics.
- **high_low** (`"both"`): Filter in `top_results` to select `"high"`, `"low"`, or `"both"` abundant genes.

## Best Practices
- Perform quality control filtering to remove low-quality spots (e.g., low library size, high mitochondrial ratio) and lowly abundant genes (e.g., detected in < 20 spots) before testing.
- Use pre-computed gene-level dispersion estimates (`estimated_y`) in `individual_svg` to significantly speed up computation.
- Ensure cluster labels are consistent across multiple samples when performing multi-sample joint testing.
- Visualize the spatial expression of top SVGs using `FeaturePlot` with cluster outlines.

## Common Pitfalls
- **Inconsistent cluster labels across samples**: Ensure that cluster names refer to the same spatial region across all samples before running multi-sample joint testing.
- **Slow computation in individual cluster tests**: Pass the `estimated_y` from `svg_test` to the `edgeR_y` parameter in `individual_svg` to avoid re-estimating dispersions.
- **Including low-quality spots**: Apply spot-level QC (e.g., using `scuttle::addPerCellQC`) and filter out genes with low counts before running `svg_test`.

## Alternatives
- `edgeR` for non-spatial differential expression analysis.
- `BayesSpace` for subspot-resolution spatial clustering.
- `SpatialLIBD` for interactive visualization and exploration of DLPFC spatial data.

## Citations
- Robinson, M. D., McCarthy, D. J., & Smyth, G. K. (2010). edgeR: a Bioconductor package for differential expression analysis of digital gene expression data. Bioinformatics, 26(1), 139-140.
- Maynard, K. R. et al. (2021). Transcriptome-scale spatial gene expression in the human dorsolateral prefrontal cortex. Nature Neuroscience, 24(3), 425-436.

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