MicroarrayDesigner: an online search tool and repository for near-optimal microarray experimental designs

<p>Abstract</p> <p>Background</p> <p>Dual-channel microarray experiments are commonly employed for inference of differential gene expressions across varying organisms and experimental conditions. The design of dual-channel microarray experiments that can help minimize the errors in the resulting inferences has recently received increasing attention. However, a general and scalable search tool and a corresponding database of optimal designs were still missing.</p> <p>Description</p> <p>An efficient and scalable search method for finding near-optimal dual-channel microarray designs, based on a greedy hill-climbing optimization strategy, has been developed. It is empirically shown that this method can successfully and efficiently find near-optimal designs. Additionally, an improved interwoven loop design construction algorithm has been developed to provide an easily computable general class of near-optimal designs. Finally, in order to make the best results readily available to biologists, a continuously evolving catalog of near-optimal designs is provided.</p> <p>Conclusion</p> <p>A new search algorithm and database for near-optimal microarray designs have been developed. The search tool and the database are accessible via the World Wide Web at <url>http://db.cse.ohio-state.edu/MicroarrayDesigner</url>. Source code and binary distributions are available for academic use upon request.</p

Optimal factorial designs for cDNA microarray experiments

We consider cDNA microarray experiments when the cell populations have a factorial structure, and investigate the problem of their optimal designing under a baseline parametrization where the objects of interest differ from those under the more common orthogonal parametrization. First, analytical results are given for the $2\times 2$ factorial. Since practical applications often involve a more complex factorial structure, we next explore general factorials and obtain a collection of optimal designs in the saturated, that is, most economic, case. This, in turn, is seen to yield an approach for finding optimal or efficient designs in the practically more important nearly saturated cases. Thereafter, the findings are extended to the more intricate situation where the underlying model incorporates dye-coloring effects, and the role of dye-swapping is critically examined.Comment: Published in at http://dx.doi.org/10.1214/07-AOAS144 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Efficiency and Robustness Issues in Complex Statistical Designs for Two-Color Microarray Experiments

Identifikation unterschiedlich exprimierter Gene ist eines der wichtigsten Ziele eines Microarray-Experimentes. Die Verwendung eines effizienten Designs in einem Microarray-Experiment kann die Power des statistischen Verfahrens erhöhen. Neben der Effizienz ist auch die Robustheit eine wichtige Frage bei der Auswahl guter Microarray-Designs vor allem aufgrund der vielen fehlenden Werte, die bei Microarray-Exprimenten auftreten. In dieser Dissertation wird das $E$-Optimalitaetskriterium als Effizienz-Ma\ss genutzt und drei weitere Kriterien werden vorgestellt, anhand derer die Robustheit eines Microarray-Designs quantifiziert werden soll.Fuer eine gegebene Anzahl von vorhandenen Arrays und Behandlungsmodalitaeten koennen verschiedene Microarray-Designs betrachtet werden. Die Zahl moeglicher Designs kann sehr gross sein. Deshalb ist eine vollstaendige Analyse der Effizienz und Robustheit rechentechnisch undurchführbar. Aus diesem Grund wird eine Methode vorgeschlagen, die auf einem genetischen Algorithmus basiert. Damit werden gute Microarray-Designs fuer eine gegebene Anzahl von Fragen ausgewaehlt. Diese Methode kann zur Auswahl guter Designs sowohl für das ein- als auch zwei-faktorielle Experiment verwendet werden. Zudem wird die Anwendung beider Kriterien, des Effizienz- und des Robustheitskriteriums, bei der Design-Auswahl demonstriert. Effiziente und robuste Designs werden fuer ein faktorielles Experiment mit verschiedenen Array Anzahlen beispielhaft durchgerechnet

Level-Screening Designs for factors with many levels

We consider designs for f factors each at m levels, where f is small but m is large. Main effect designs with mf experimental points are presented. For two factors, two types of designs are investigated, termed sawtooth and dumbbell designs, based on a graphical representation. For three factors, cyclic sawtooth designs are considered. The paper seeks optimal and near optimal designs which involve factors with many levels but few observations. It also investigates issues of robustness when as much as one third of the data is structurally missing. An important area of application is in screening for drug discovery and we compare our designs with others using a published data set with two factors each with fifty levels, where the dumbbell design outperforms others and is an example of an inherently unbalanced design dominating more balanced designs

On Solving Selected Nonlinear Integer Programming Problems in Data Mining, Computational Biology, and Sustainability

This thesis consists of three essays concerning the use of optimization techniques to solve four problems in the fields of data mining, computational biology, and sustainable energy devices. To the best of our knowledge, the particular problems we discuss have not been previously addressed using optimization, which is a specific contribution of this dissertation. In particular, we analyze each of the problems to capture their underlying essence, subsequently demonstrating that each problem can be modeled as a nonlinear (mixed) integer program. We then discuss the design and implementation of solution techniques to locate optimal solutions to the aforementioned problems. Running throughout this dissertation is the theme of using mixed-integer programming techniques in conjunction with context-dependent algorithms to identify optimal and previously undiscovered underlying structure