nnsvg

name: bioconductor-nnsvgdescription: Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data. The method is based on nearest-neighbor Gaussian processes and uses the BRISC algorithm for model fitting and parameter estimation. Allows identification and ranking of SVGs with flexible length scales across a tissue slide or within spatial domains defined by covariates. Scales linearly with the number of spatial locations and can be applied to datasets containing thousands or morewhen_to_use: Use when: Method for scalable identification of spatially variable genes (SVGs) in spatially-resolved transcriptomics data; Single-cell RNA-seq analysis (nnSVG). Not for: Requires R ≥ 4.2 and Bioconductor ≥ 3.16user-invocable: false

nnSVG

Workflows

Standard Workflow

library(SpatialExperiment)
library(nnSVG)

# Filter low-expressed and mitochondrial genes
spe <- filter_genes(spe)

# Calculate logcounts using library size factors
spe <- computeLibraryFactors(spe)
spe <- logNormCounts(spe)

# Run nnSVG to identify spatially variable genes
spe <- nnSVG(spe)

Input: A SpatialExperiment object containing raw counts and spatial coordinates. Output: A SpatialExperiment object with SVG ranking and statistics stored in rowData.

When to Use

• Identifying spatially variable genes (SVGs) in spatially-resolved transcriptomics datasets (e.g., Visium, Slide-seq).
• Identifying SVGs within spatial domains or cell types by incorporating covariates using the X parameter in nnSVG.
• Scaling SVG identification to large datasets with thousands of spatial locations.

When NOT to Use

• For non-spatial single-cell RNA-seq data, use standard highly variable gene (HVG) selection in scran or Seurat.
• For simple differential expression between pre-defined clusters without spatial modeling, use findMarkers from scran.

Data Requirements

• A SpatialExperiment object containing a counts assay and spatial coordinates (accessible via spatialCoords).
• Alternatively, a numeric matrix of log-transformed normalized counts and a matrix of spatial coordinates.

Key Parameters

• X (NULL): A model matrix of covariates (e.g., cell types or spatial domains) to include in the model.
• filter_genes_ncounts (3): Minimum number of counts for gene filtering in filter_genes.
• filter_genes_pcspots (0.5): Minimum percentage of spots with counts for gene filtering in filter_genes.
• filter_mito (TRUE): Whether to filter out mitochondrial genes in filter_genes.

Best Practices

• Filter out low-expressed genes using filter_genes before running nnSVG to avoid errors and reduce runtime.
• Re-calculate library size factors and log-normalized counts after filtering genes.
• For multi-sample datasets, run nnSVG individually per sample and combine results by averaging ranks.

Common Pitfalls

• Running nnSVG on rows (genes) or columns (spots) containing all zero counts; ensure proper filtering using filter_genes or manual subsetting.
• Empty levels in factor covariates when creating the design matrix; use droplevels() to remove them.

Alternatives

• SpatialDE for spatial variable gene identification.
• Spark / SPARK-G for statistical modeling of SVGs.
• scran for non-spatial highly variable gene selection.

Citations

• Weber and Hicks 2026 (or Weber et al., Nature Communications)
• Datta et al., 2016 (Nearest-neighbor Gaussian processes)
• Saha and Datta, 2018 (BRISC algorithm)

References

• Homepage: bioconductor.org/packages/nnSVG
• Vignette: https://bioconductor.org/packages/release/bioc/vignettes/nnSVG/inst/doc/nnSVG.html