DESpace

Workflows

Standard Workflow

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

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.

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