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 iftrace
= 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).$Z.trace
. Number of times cell being assigned to each topic across all posterior samples. We can further compute the posterior distributions ofZ
(topic assignment) for individual cells.$doc.trace
.Ndk
for every collected posterior sample. NULL iftrace
= FALSE.$word.trace
.Nwk
for every collected posterior sample. NULL iftrace
= FALSE.