Extended Data Fig. 1: Generative Models used in FICTURE.

(a) The generative model for pixel-level inference used in FICTURE. Shaded circles represent observed data. (\({x}_{j},{x}_{i}\)) represent spatial locations of anchors and pixels; \(\widetilde{{y}_{i}}\) represents gene counts for pixel i. The black-outlined circles (\({{\eta }},\,{\rm{\alpha }}\)) represent hyperparameters for the Dirichlet priors. The orange-outlined circles (\({{{\beta }}}_{k}\)) represent factor level expression distribution and the green-outlined circles (\({{\rm{\theta }}}_{j}\)) represent the anchor level factor proportions. The blue-outlined circles (\({z}_{i},{c}_{i}\)) represent latent factor and latent anchor assignments for each pixel. We provide the typical range of the number of factors (K), anchor points (n), and pixels (N) next to the corresponding boxes. (b) Latent Dirichlet Allocation model used in the fully unsupervised FICTURE. The standard LDA model is used to infer factors (\({{{\beta }}}_{k}\)) to model gene counts for each ‘spot’ and the learned factors are used as input to part (A). Each ‘spot’ level gene count (\(\widetilde{{y}_{i,j}}\)) is generated using a fixed-sized hexagonal grid. \({\Theta }_{j}\) represents the probabilistic distribution over K factors for spot j. \({z}_{{ij}}\) represents the latent factor of pixel i in spot j.