SpaNorm() gains a BPPARAM argument to parallelise normalisation across workers via BiocParallel, accelerating large datasets. It defaults to BiocParallel::SerialParam() (no parallelisation), and results are identical regardless of the backend used.
SpaNorm() now normalises DelayedArray-backed count assays (e.g. disk-backed via HDF5Array) block-wise, so out-of-core datasets are processed without ever loading the full matrix into memory. Results match the in-memory path.
Exported fitNB(), which fits a per-gene negative binomial GLM over an arbitrary design matrix using SpaNorm’s IRLS engine (with optional ridge regularisation and adjustable outlier winsorisation). This exposes the model-fitting machinery for reuse independently of SpaNorm’s spatial model.
fitNB() and calculateMu() gain an offset argument: a genes x cells matrix added to the linear predictor with its coefficient fixed at 1, so log(mu) = gmean + tcrossprod(alpha, W) + offset. Use it for an effect that is already known rather than adding a column to the design, whose coefficient would float and could absorb signal correlated with that effect. The offset is subset alongside the counts, applied to the dispersion estimation as well as the mean, and sliced in step with the counts when the fit is carved into gene-blocks. It defaults to NULL, which is a strict no-op, so all existing behaviour is unchanged. The GPU path is implemented backend-agnostically but has not yet been executed on an accelerator.
fitNB() also gains a psi argument for supplying per-gene NB dispersions instead of estimating them. Supplied dispersions are used as-is (no re-estimation, no winsorisation) and the outer dispersion loop is bypassed: the coefficients come from a single IRLS fit at the given psi, so edgeR::estimateDisp is never called and the returned sampling factor carries no "dispersion" level. The dispersions need not come from an identical design – values estimated on a design nested in (or equal to) the fitting design are appropriate, e.g. pooled across a coarser model, which errs conservative. psi composes with offset, and psi = NULL (the default) is a strict no-op.
Improvements
The optional GPU backend now uses the torch package instead of TensorFlow, adding native support for NVIDIA CUDA and Apple Silicon (Metal/MPS) devices and removing the Python/reticulate dependency. Users of backend = "gpu" should install torch in place of tensorflow.
The GPU backend now automatically detects available accelerator memory and fits large datasets in gene-blocks so peak GPU memory stays bounded, avoiding out-of-memory failures on GPUs with limited VRAM. This requires no additional arguments; the detected budget can be overridden via the new gpu.mem.budget parameter, and results match the CPU backend within a small numerical tolerance.
The dispersion winsorisation used during normalisation now clamps at 4 MAD (previously 3), matching the coefficient and mean winsorisation, and is configurable via the winsorisation controls on the fitting/normalisation helpers.
Fixed getGPUMemoryBudget()’s CUDA auto-detection reporting the whole physical GPU’s free memory rather than the process’s assigned MIG instance’s, causing a many-fold budget overestimate (and downstream out-of-memory errors) on MIG-partitioned GPUs; it now resolves the correct device via CUDA_VISIBLE_DEVICES. Added setGPUMemoryBudget() to explicitly set (and cache) the budget for the session, for cases where auto-detection remains unreliable.
Added spatial visualisation funciton plotSpatial to visualise colData, gene expression, and reduced dimensions.
Added spatial visualisation function plotCovariate to visualise the biolgy, batch, and library size functions estimated by SpaNorm.
Dynamic calculation of df.tps for rectangular tissue sections.
Allow separate specification of df.tps for biology and library size.
Added GLM-PCA approximation through the SpaNormPCA function. The null model is considered to consist of the library size effects, batch effects, and the gene mean.