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Outputs from function SpaTopic_inference. A list contains the following members:

  • $Perplexity. The perplexity is for the training data. Let N be the total number of cells across all images. \(Perplexity = exp(-loglikelihood/N)\)

  • $Deviance. \(Deviance = -2loglikelihood\).

  • $loglikelihood. The model log-likelihood.

  • $loglike.trace. The log-likelihood for every collected posterior sample. NULL if trace = FALSE.

  • $Beta. Topic content matrix with rows as celltypes and columns as topics

  • $Theta. Topic prevalent matrix with rows as regions and columns as topics

  • $Ndk. Number of cells per topic (col) per region (row).

  • $Nwk. Number of cells per topic (col) per celltype (row).

  • $Z.trace. Number of times cell being assigned to each topic across all posterior samples. We can further compute the posterior distributions of Z (topic assignment) for individual cells.

  • $doc.trace. Ndk for every collected posterior sample. NULL if trace = FALSE.

  • $word.trace. Nwk for every collected posterior sample. NULL if trace = FALSE.