Journal
CYTOMETRY PART A
Volume 79A, Issue 5, Pages 383-391Publisher
WILEY
DOI: 10.1002/cyto.a.21066
Keywords
generative models; machine learning; subcellular location; microscope image analysis; cell shape; nuclear shape; location proteomics
Categories
Funding
- NIGMS NIH HHS [R01 GM075205, R01 GM075205-02, R01 GM075205-03S2, R01 GM 075205, R01 GM075205-03] Funding Source: Medline
Ask authors/readers for more resources
Given the importance of subcellular location to protein function, computational simulations of cell behaviors will ultimately require the ability to model the distributions of proteins within organelles and other structures. Toward this end, statistical learning methods have previously been used to build models of sets of two-dimensional microscope images, where each set contains multiple images for a single subcellular location pattern. The model learned from each set of images not only represents the pattern but also captures the variation in that pattern from cell to cell. The models consist of sub-models for nuclear shape, cell shape, organelle size and shape, and organelle distribution relative to nuclear and cell boundaries, and allow synthesis of images with the expectation that they are drawn from the same underlying statistical distribution as the images used to train them. Here we extend this generative models approach to three dimensions using a similar framework, permitting protein subcellular locations to be described more accurately. Models of different patterns can be combined to yield a synthetic multi-channel image containing as many proteins as desired, something that is difficult to obtain by direct microscope imaging for more than a few proteins. In addition, the model parameters represent a more compact and interpretable way of communicating subcellular patterns than descriptive image features and may be particularly effective for automated identification of changes in subcellular organization caused by perturbagens. (C) 2011 International Society for Advancement of Cytometry
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available