Fall 2009
Section: Fall 2009
This seminar series meets at 10:00 am on the 2nd and 4th Fridays of every month. Refreshments are served at 9:45am.
Friday, September 11, 2009
Warwick Evans Conference Room, Building D, 4000 Reservoir Rd, Washington, DC 20057-1484
“Improving differential expression analysis with the consideration of genome-wide co-expression information”
Speaker: Yinglei Lai, PhD, Assistant Professor of Statistics, Department of Statistics, George Washington University, Washington D.C.
Abstract:
Microarrays have been widely used in biomedical studies. The differential expression analysis of microarray data is still an interesting topic. The control of false positives in differential expression analysis remains a major challenge although many statistical methods have been proposed for its improvement. Since genes interact with each other during cellular and molecular processes, an efficient incorporation of genome-wide co-expression information may significantly improve the detection of differential expression. We will address our recent research progress in this direction.
Friday, September 25, 2009
Lombardi Comprehensive Cancer Center, 3900 Reservoir Rd., NW, Research Building, Conference Room E501
“Some lessons from our collaborative studies in esophageal cancer, prostate cancer, HIV, and breast cancer”
Speaker: George E. Bonney, PhD, Professor/Director, Statistical Genetics and Bioinformatics Unit, National Human Genome Center at Howard University, Washington D.C.
Abstract:
The work of the Statistical Genetics and Bioinformatics Unit of the National Human Genome Center at Howard University involves the use of high level mathematical and statistical computing skills in biomedicine. Here I briefly discuss questions and results from some of our collaborative studies:
Esophageal cancer in Chinese families: Is alcohol really protective?
Multiple cancers in Texas families. Is the association with the p53 mutation causal?
Prostate Cancer in African American Men: Where are the genes?
HIV Prevalence and Incidence among Blacks in Washington DC:
Does it make sense to talk of estimation for the whole city using
only the data from Howard University Hospital?
A Molecular Index for Breast Cancer Risk Assessment?
Can we really construct such an index for risk of invasive breast cancer?
Friday, October 9, 2009
Warwick Evans Conference Room, Building D, 4000 Reservoir Rd, Washington, DC 20057-1484
“Modeling and inference of the hazard ratio function”
Speaker: Song Yang, PhD, Office of Biostatistics Research, National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland
Abstract:
The hazard ratio provides a valuable tool for assessing a treatment effect with survival data, with the proportional hazards special case of the Cox model as a widely used example. In general, the hazard ratio is a function of time, and provides a visual display of the temporal pattern of the treatment effect. The proportional hazards assumption is often too restrictive, at least for the initial exploration of a treatment effect, while a nonparametric estimate of the hazard ratio function requires a bandwidth selection, and may result in increase in variance or bias. On the other hand, most semiparametric hazards models proposed so far imply certain restrictions on the hazard ratio that limit their utility. We investigate a model that allows monotone increasing or decreasing hazard ratio functions, including crossing hazards. This model provides a sufficient level of flexibility for many applications. The point estimates, point-wise confidence intervals, and simultaneous confidence intervals, or confidence bands, of the hazard ratio, are proposed under this model. We demonstrate the inference procedures in several examples, including the coronary hear disease data from the Women’s Health Initiative estrogen plus progestin clinical trial. These examples, with a diverse range of time dependence of the hazard ratio from mild to severe, suggest that the hazard ratio under this class of models, its confidence intervals and confidence bands, provide very useful visual display tools for assessing the treatment effect with survival data.
Friday, October 23, 2009
Warwick Evans Conference Room, Building D, 4000 Reservoir Rd, Washington, DC 20057-1484
"Impact of q-RT-PCR analytical methods on multi-center biomarker trials in colorectal cancer"
Speaker: Terry Hyslop, PhD, Director, Division of Biostatistics, Director, Kimmel Cancer Center Biostatistics Core Facility Thomas Jefferson University, Philadelphia, Pennsylvania
Abstract:
Guanylyl cyclase 2C (GUYC2C), an emergent intestinal tumor suppressor, is the receptor for the paracrine hormones guanylin and uroguanylin, gene products frequently lost early in colon carcinogenesis [1,2]. Lymph nodes and tumor specimens were dissected from patients with AJCC stage I and II colon and rectal resections performed in the surgical departments of 7 academic medical centers and 2 community hospitals in the U.S. and Canada between January 2003 and June 2007. Follow-up, based on periodic evaluations, was confirmed for all patients through December 2007. GCC and beta-actin expression were measured based on standard curves formed from serial dilutions. Gene expression was also estimated by logistic regression analysis of amplification profiles from individual q-RT-PCR reactions, providing an efficiency-adjusted relative quantification based on parameter estimates from the fitted models [3]. We show that the measurement techniques developed impact the analysis and interpretation of this large multi-center prospective trial by reducing the measurement error in q-RT-PCR. In multivariable Cox models of n=257 early stage colorectal cancer patients [4], controlling for T stage, tumor location, lympho-vascular invasion, and tumor differentiation, GCC q-RT-PCR remains an independent predictor of recurrence (adjusted Hazard Ratio (AHR)=4.66, p=0.04, 95%CI=1.11, 19.57). Moreover, GCC q-RT-PCR is an independent predictor of disease free survival (AHR=3.27, p=0.03, 95% CI=1.15, 9.29). We also present findings in this population based on recursive partitioning analysis, where homogeneous risk sets are identified. Finally, preliminary analysis indicates that GUYC2C may also be used to identify subpopulations of early stage patients who may benefit from chemotherapy.
Friday, November 13, 2009
Warwick Evans Conference Room, Building D, 4000 Reservoir Rd, Washington, DC 20057-1484
"Computer-Intensive Statistical Methodology with Applications to Translational Cancer Research"
Speaker: Kim-Anh Do, PhD, Professor, Department of Biostatistics,
University of Texas M. D. Anderson Cancer Center, Houston, Texas
Abstract:
Early detection is critical in disease control and prevention. Biomarkers provide valuable information about the status of a cell at any given time point. Biomarker research has benefited from recent advances in technologies such as gene expression microarrays, and more recently, proteomics. The long term translational research goal is that if drugs can be targeted to specific tissues in the body, then dosage can be altered to achieve the desired effect while minimizing side effects such as toxicity. Motivated by specific problems involving such high throughput data, I have developed computer-intensive statistical methods based on nonparametric and semiparametric mixture model assumptions for real-time analysis in the context of biomarker discovery. Most biomarker-discovery projects aim at identifying features in the biomarker profiles (gene expression, phage, SAGE, mass spectrometry proteins) that distinguish cancers from normals, between different stages of disease development, or between experimental conditions (such as different treatment arms or different tissue types). Novel statistical methodology development will be highlighted with direct applications to cancer research challenges that address our long term translational goal.
Friday, December 11, 2009
Warwick Evans Conference Room, Building D, 4000 Reservoir Rd, Washington, DC 20057-1484
"A unified approach to non-negative matrix factorization with application to large-scale biological data analysis and text mining"
Speaker: Karthik Devarajan, PhD, Associate Member, Biostatistics & Bioinformatics, Fox Chase Cancer Center, Philadelphia, Pennsylvania.
Abstract:
Non-negative matrix factorization (NMF) by the multiplicative updates algorithm is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into two matrices, W and H, each with nonnegative entries, V ~ WH. NMF has been shown to have a unique parts-based, sparse representation of the data. The non-negativity constraints in NMF allow only additive combinations of the data which enables it to learn parts that have distinct physical representations in reality. Over the past decade, NMF has found successful applications in such diverse areas as natural language processing, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. In this talk, we present a generalized approach to NMF based on Renyi's divergence between two non-negative matrices related to the Poisson likelihood. Our approach unifies various competing models and provides a unique framework for NMF. We demonstrate a link between NMF and some well-known statistical models. In addition, we describe an unsupervised clustering algorithm that utilizes this unified approach and discuss a parallel implementation of the algorithm using high-performance computing clusters. The applicability of our methods to molecular pattern discovery and text mining are illustrated using real-life and simulated data.