omicverse.space.svg

omicverse.space.svg(adata, mode='prost', n_svgs=3000, target_sum=500000.0, platform='visium', mt_startwith='MT-', **kwargs)[source]

Identify spatially variable genes using multiple methods.

This function identifies genes that show significant spatial variation in their expression patterns across the tissue. It supports multiple methods including PROST (Pattern RecognitiOn of Spatial Transcriptomics), Pearson correlation, and Spateo-based analysis.

Parameters:

  • adata (AnnData) – Spatial AnnData containing expression matrix and coordinates in adata.obsm['spatial'].

  • mode ({'prost', 'pearsonr', 'spateo', 'somde', 'spatialde'}, default='prost') – SVG detection backend.

  • n_svgs (int, default=3000) – Number of spatially variable genes to select.

  • target_sum (float, default=50*1e4) – Target-sum used during normalization.

  • platform (str, default='visium') – Platform identifier used by PROST preprocessing.

  • mt_startwith (str, default='MT-') – Mitochondrial gene prefix excluded by default.

  • **kwargs – Additional method-specific options. For somde: k, qval_threshold, retrain_epoch. For spatialde: qval_threshold, min_total_count, regress_formula, n_jobs, kernel_space, approx_rank, approx_seed, approx_models, show_progress.

Returns:

  • AnnData – Updated AnnData with SVG flags in adata.var['space_variable_features'] and adata.var['highly_variable'].

  • Notes –

    • PROST mode requires opencv-python package

    • Different modes use different statistical approaches:

      • PROST: Pattern recognition and spatial autocorrelation

      • pearsonr: Correlation between gene expression and spatial coordinates

      • spateo: Wasserstein distance-based spatial variation

      • somde: SOM-compressed SpatialDE GP test (fast for large datasets)

    • SOMDE kwargs: k (cells/node, default 20), qval_threshold (default 0.05), retrain_epoch (extra SOM epochs, default 0)

    • SOMDE stores adata.var['somde_LLR'], somde_pval, somde_qval, somde_FSV

    • SOMDE requires somoclu and patsy

    • spatialde: GP-based test directly on single-cell coordinates (no SOM compression), uses bundled NaiveDE + SpatialDE packages from omicverse/external/

    • SpatialDE kwargs: qval_threshold (default 0.05), min_total_count (default 3), regress_formula (default 'np.log(total_counts)'), n_jobs (default 1), kernel_space (optional custom covariance search space), approx_rank (optional Nyström low-rank approximation rank), approx_seed (landmark sampling seed), approx_models (kernel list for approximation, default ('SE',)), show_progress (whether to show tqdm progress bars, default True)

    • SpatialDE stores adata.var['spatialde_LLR'], spatialde_pval, spatialde_qval, spatialde_FSV, spatialde_l

    • SpatialDE requires patsy and tqdm

    • Mitochondrial genes are excluded by default

    • Results are normalized and log-transformed

  • Examples – >>> import scanpy as sc >>> import omicverse as ov >>> # Load spatial data >>> adata = sc.read_visium(…) >>> # Find SVGs using PROST >>> adata = ov.space.svg( … adata, … mode=’prost’, … n_svgs=2000, … platform=’visium’ … ) >>> # Access SVGs >>> svgs = adata.var_names[adata.var[‘space_variable_features’]]