Fig. 1. Nonnegative factorizations recover a parts-based representation in ‘quilt’ simulated multivariate spatial count data.

a, Each of 200 features was randomly assigned to one of four nonnegative spatial factors. b, Negative binomial count data used for model fitting. c, Real-valued factors learned from unsupervised (nonspatial) dimension reduction. d, As c but using nonnegative components. e, Real-valued, spatially aware factors with EQ kernel. f, As e but with a Matérn kernel and without a sparsity-inducing prior. g, Nonnegative, spatially-aware factors. h, Unsupervised clustering of observations. Spatial models used all observations as IPs. Gray indicates observations held out for validation.