Robust test method for time-course microarray experiments.

BACKGROUND: In a time-course microarray experiment, the expression level for each gene is observed across a number of time-points in order to characterize the temporal trajectories of the gene-expression profiles. For many of these experiments, the scientific aim is the identification of genes for which the trajectories depend on an experimental or phenotypic factor. There is an extensive recent body of literature on statistical methodology for addressing this analytical problem. Most of the existing methods are based on estimating the time-course trajectories using parametric or non-parametric mean regression methods. The sensitivity of these regression methods to outliers, an issue that is well documented in the statistical literature, should be of concern when analyzing microarray data. RESULTS: In this paper, we propose a robust testing method for identifying genes whose expression time profiles depend on a factor. Furthermore, we propose a multiple testing procedure to adjust for multiplicity. CONCLUSIONS: Through an extensive simulation study, we will illustrate the performance of our method. Finally, we will report the results from applying our method to a case study and discussing potential extensions.

Computational Journalism: from Answering Question to Questioning Answers and Raising Good Questions

Our media is saturated with claims of ``facts'' made from data. Database research has in the past focused on how to answer queries, but has not devoted much attention to discerning more subtle qualities of the resulting claims, e.g., is a claim ``cherry-picking''? This paper proposes a Query Response Surface (QRS) based framework that models claims based on structured data as parameterized queries. A key insight is that we can learn a lot about a claim by perturbing its parameters and seeing how its conclusion changes. This framework lets us formulate and tackle practical fact-checking tasks --- reverse-engineering vague claims, and countering questionable claims --- as computational problems. Within the QRS based framework, we take one step further, and propose a problem along with efficient algorithms for finding high-quality claims of a given form from data, i.e. raising good questions, in the first place. This is achieved to using a limited number of high-valued claims to represent high-valued regions of the QRS. Besides the general purpose high-quality claim finding problem, lead-finding can be tailored towards specific claim quality measures, also defined within the QRS framework. An example of uniqueness-based lead-finding is presented for ``one-of-the-few'' claims, landing in interpretable high-quality claims, and an adjustable mechanism for ranking objects, e.g. NBA players, based on what claims can be made for them. Finally, we study the use of visualization as a powerful way of conveying results of a large number of claims. An efficient two stage sampling algorithm is proposed for generating input of 2d scatter plot with heatmap, evalutaing a limited amount of data, while preserving the two essential visual features, namely outliers and clusters. For all the problems, we present real-world examples and experiments that demonstrate the power of our model, efficiency of our algorithms, and usefulness of their results.

Computational Methods for RNA Structure Validation and Improvement.

With increasing recognition of the roles RNA molecules and RNA/protein complexes play in an unexpected variety of biological processes, understanding of RNA structure-function relationships is of high current importance. To make clean biological interpretations from three-dimensional structures, it is imperative to have high-quality, accurate RNA crystal structures available, and the community has thoroughly embraced that goal. However, due to the many degrees of freedom inherent in RNA structure (especially for the backbone), it is a significant challenge to succeed in building accurate experimental models for RNA structures. This chapter describes the tools and techniques our research group and our collaborators have developed over the years to help RNA structural biologists both evaluate and achieve better accuracy. Expert analysis of large, high-resolution, quality-conscious RNA datasets provides the fundamental information that enables automated methods for robust and efficient error diagnosis in validating RNA structures at all resolutions. The even more crucial goal of correcting the diagnosed outliers has steadily developed toward highly effective, computationally based techniques. Automation enables solving complex issues in large RNA structures, but cannot circumvent the need for thoughtful examination of local details, and so we also provide some guidance for interpreting and acting on the results of current structure validation for RNA.

Soundscape Ecology of Hawaiian Spinner Dolphin Resting Bays

Sound is a key sensory modality for Hawaiian spinner dolphins. Like many other marine animals, these dolphins rely on sound and their acoustic environment for many aspects of their daily lives, making it is essential to understand soundscape in areas that are critical to their survival. Hawaiian spinner dolphins rest during the day in shallow coastal areas and forage offshore at night. In my dissertation I focus on the soundscape of the bays where Hawaiian spinner dolphins rest taking a soundscape ecology approach. I primarily relied on passive acoustic monitoring using four DSG-Ocean acoustic loggers in four Hawaiian spinner dolphin resting bays on the Kona Coast of Hawai‛i Island. 30-second recordings were made every four minutes in each of the bays for 20 to 27 months between January 8, 2011 and March 30, 2013. I also utilized concomitant vessel-based visual surveys in the four bays to provide context for these recordings. In my first chapter I used the contributions of the dolphins to the soundscape to monitor presence in the bays and found the degree of presence varied greatly from less than 40% to nearly 90% of days monitored with dolphins present. Having established these bays as important to the animals, in my second chapter I explored the many components of their resting bay soundscape and evaluated the influence of natural and human events on the soundscape. I characterized the overall soundscape in each of the four bays, used the tsunami event of March 2011 to approximate a natural soundscape and identified all loud daytime outliers. Overall, sound levels were consistently louder at night and quieter during the daytime due to the sounds from snapping shrimp. In fact, peak Hawaiian spinner dolphin resting time co-occurs with the quietest part of the day. However, I also found that humans drastically alter this daytime soundscape with sound from offshore aquaculture, vessel sound and military mid-frequency active sonar. During one recorded mid-frequency active sonar event in August 2011, sound pressure levels in the 3.15 kHz 1/3rd-octave band were as high as 45.8 dB above median ambient noise levels. Human activity both inside (vessels) and outside (sonar and aquaculture) the bays significantly altered the resting bay soundscape. Inside the bays there are high levels of human activity including vessel-based tourism directly targeting the dolphins. The interactions between humans and dolphins in their resting bays are of concern; therefore, my third chapter aimed to assess the acoustic response of the dolphins to human activity. Using days where acoustic recordings overlapped with visual surveys I found the greatest response in a bay with dolphin-centric activities, not in the bay with the most vessel activity, indicating that it is not the magnitude that elicits a response but the focus of the activity. In my fourth chapter I summarize the key results from my first three chapters to illustrate the power of multiple site design to prioritize action to protect Hawaiian spinner dolphins in their resting bays, a chapter I hope will be useful for managers should they take further action to protect the dolphins.