Biomedical Data Analysis with Prior Knowledge : Modeling and Learning

Modern research in biology and medicine is experiencing a data explosion in quantity and particularly in complexity. Efficient and accurate processing of these datasets demands state-of-the-art computational methods such as probabilistic graphical models, graph-based image analysis and many inference/optimization algorithms. However, the underlying complexity of biomedical experiments rules out direct out-of-the-box applications of these methods and requires novel formulation and enhancement to make them amendable to specific problems. This thesis explores novel approaches for incorporating prior knowledge into the data analysis workflow that leads to quantitative and meaningful interpretation of the datasets and also allows for sufficient user involvement. As discussed in Chapter 1, depending on the complexity of the prior knowledge, these approaches can be categorized as constrained modeling and learning. The first part of the thesis focuses on constrained modeling where the prior is normally explicitly represented as additional potential terms in the problem formulation. These terms prevent or discourage the downstream optimization of the formulation from yielding solutions that contradict the prior knowledge. In Chapter 2, we present a robust method for estimating and tracking the deuterium incorporation in the time-resolved hydrogen exchange (HX) mass spectrometry (MS) experiments with priors such as sparsity and sequential ordering. In Chapter 3, we introduce how to extend a classic Markov random field (MRF) model with a shape prior for cell nucleus segmentation. The second part of the thesis explores learning which addresses problems where the prior varies between different datasets or is too difficult to express explicitly. In this case, the prior is first abstracted as a parametric model and then its optimum parametrization is estimated from a training set using machine learning techniques. In Chapter 4, we extend the popular Rand Index in a cost-sensitive fashion and the problem-specific costs can be learned from manual scorings. This set of approaches becomes more interesting when the input/output becomes structured such as matrices or graphs. In Chapter 5, we present structured learning for cell tracking, a novel approach that learns optimum parameters automatically from a training set and allows for the use of a richer set of features which in turn affords improved tracking performance. Finally, conclusions and outlook are provided in Chapter 6

A Rapid and Efficient 2D/3D Nuclear Segmentation Method for Analysis of Early Mouse Embryo and Stem Cell Image Data

SummarySegmentation is a fundamental problem that dominates the success of microscopic image analysis. In almost 25 years of cell detection software development, there is still no single piece of commercial software that works well in practice when applied to early mouse embryo or stem cell image data. To address this need, we developed MINS (modular interactive nuclear segmentation) as a MATLAB/C++-based segmentation tool tailored for counting cells and fluorescent intensity measurements of 2D and 3D image data. Our aim was to develop a tool that is accurate and efficient yet straightforward and user friendly. The MINS pipeline comprises three major cascaded modules: detection, segmentation, and cell position classification. An extensive evaluation of MINS on both 2D and 3D images, and comparison to related tools, reveals improvements in segmentation accuracy and usability. Thus, its accuracy and ease of use will allow MINS to be implemented for routine single-cell-level image analyses

Oct4 is required for lineage priming in the developing inner cell mass of the mouse blastocyst

The transcription factor Oct4 is required in vitro for establishment and maintenance of embryonic stem cells and for reprogramming somatic cells to pluripotency. In vivo, it prevents the ectopic differentiation of early embryos into trophoblast. Here, we further explore the role of Oct4 in blastocyst formation and specification of epiblast versus primitive endoderm lineages using conditional genetic deletion. Experiments involving mouse embryos deficient for both maternal and zygotic Oct4 suggest that it is dispensable for zygote formation, early cleavage and activation of Nanog expression. Nanog protein is significantly elevated in the presumptive inner cell mass of Oct4 null embryos, suggesting an unexpected role for Oct4 in attenuating the level of Nanog, which might be significant for priming differentiation during epiblast maturation. Induced deletion of Oct4 during the morula to blastocyst transition disrupts the ability of inner cell mass cells to adopt lineage-specific identity and acquire the molecular profile characteristic of either epiblast or primitive endoderm. Sox17, a marker of primitive endoderm, is not detected following prolonged culture of such embryos, but can be rescued by provision of exogenous FGF4. Interestingly, functional primitive endoderm can be rescued in Oct4-deficient embryos in embryonic stem cell complementation assays, but only if the host embryos are at the pre-blastocyst stage. We conclude that cell fate decisions within the inner cell mass are dependent upon Oct4 and that Oct4 is not cell-autonomously required for the differentiation of primitive endoderm derivatives, as long as an appropriate developmental environment is established