Automated Learning of Subcellular Variation among Punctate Protein Patterns and a Generative Model of Their Relation to Microtubules
Fig 4
Graphical model representation for the Bayesian hierarchical framework of generative model of puncta conditioned on cell geometry and microtubules.
A nuclear shape is drawn from dn, a cell shape is drawn from dc, dependent on the nuclear shape [8]. A microtubule pattern is synthesized from dm dependent on the generated cell and nuclear shape [9]. The distribution of shape and positions of puncta, dp, is modeled with components pp, which models the position of puncta dependent on the cell, nucleus and microtubule pattern, and np, sp and ip, which independently model the number, size and intensity of puncta. The background pattern is similarly generated dependent on the cell, nucleus and microtubule pattern with pb, and its intensity is determined with ib.