This package implememnts a Bayesian hierarchical model that represents observations as aggregation of a few unobserved binary machines where the aggregation varies by subjects. Our approach is to specify the model likelihood via factorization into two latent binary matrices: machine profiles and individual factors. Given latent factorization, we account for inherent errors in measurement using sensitivities and specificities of protein detection. We use a prior for the individual factor matrix (Indian Buffet Process for binary matrices) to encourage a small number of subject clusters each with distinct patterns of active machines. The posterior distribution for the numbers of patient clusters and machines are estimated from data and by design tend to concentrate on smaller values. The posterior distributions of model parameters are estimated via Markov chain Monte Carlo which makes a list of molecular machine profiles with uncertainty quantification as well as patient-specific posterior probability of having each machine.
|Maintainer||Zhenke Wu <[email protected]>|
|License||MIT + file LICENSE|
|Package repository||View on GitHub|
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