Patient-Specific Data Fusion Defines Prognostic Cancer Subtypes
Figure 7
Graphical representation of the PSDF model presented in this paper.
The indicator variables allow the model to perform data fusion on a sample-by-sample basis, defining the states fused (
) and unfused (
). The prior probability of fusion is defined by
and is set in all cases to
for the results in this paper. The
parameters are binary switches that select individual features in each data set. The number of clusters is given by the number of unique values assigned to the
variables, which denote cluster membership in a given context. The
parameters are mixture weights for the Dirichlet Processes and are marginalised analytically.
and
are concentration hyperparameters for the Dirichlet Processes and are sampled as part of the MCMC procedure.