Statistical Bioinformatics Center

Congratulations 2007-2008 Graduates!

Professor Rebecca Doerge with Dr. Riyan Cheng Dr. Riyan Cheng and Professor Rebecca W. Doerge

Professor Bruce Craig with Dr. Shannon Knapp Professor Bruce A. Craig and Dr. Shannon Knapp
Congratulations to our recent Ph.D. graduates Riyan Cheng (Dec 2007) and Shannon Knapp (Dec 2007) on their achievements.

Dr. Riyan Cheng performed his Ph.D. research in the area of statisical bioinformatics under the direction of Professors Rebecca W. Doerge and Jun Xie. The title of his thesis was: "Statistical Methods for Mapping Multiple Complex Traits."

Dr. Shannon Knapp performed her Ph.D. research in the area of statistical bioinformatics under the direction of Professor Bruce A. Craig. The title of her dissertation was: "Incorporating Uncertainty into Non-Invasive DNA-Based Mark-Recapture Population Estimates."

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Professor Rebecca W. Doerge Elected Fellow of the American Association for the Advancement of Science

Professor Rebecca W. Doerge Rebecca W. Doerge
Rebecca W. Doerge, Professor of Statistics and Agronomy, was elected Fellow of the American Association for the Advancement of Science (AAAS) in October 2007. She, along with 470 other elected Fellows, will be recognized for her contributions to science and technology at the Fellows Forum to be held on February 16, 2008 during the AAAS Annual Meeting in Boston. She is one of six elected Fellows this year in the Section on Statistics. Other current Department of Statistics faculty members who have received this honor include Professor and Department Head Mary Ellen Bock, Professor William S. Cleveland, and Professor Herman Rubin.

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The cycle of theory, experiment, and information is nowhere more important than in the life sciences, where we are learning how to piece together various levels of expertise into a global or systems-level understanding of biology. Statistical Bioinformatics is involved at each level: accumulation, organization, and analysis of biological data. Hypotheses that are initiated and tested can be refined, and new experiments formulated for the purpose of supplying more information.

Statistical Bioinformatics

Statistical Bioinformatics Center News

  • Estimating the Proportion of True Null Hypotheses for Multiple Comparisons

    Cancer Informatics. 2008: 4 25-32

    Fig. 1 Figure 1 (Click on the picture to see a larger image.)



    Abstract
    Whole genome microarray investigations (e.g. differential expression, differential methylation, ChIP-Chip) provide opportunities to test millions of features in a genome. Traditional multiple comparison procedures such as familywise error rate (FWER) controlling procedures are too conservative. Although false discovery rate (FDR) procedures have been suggested as having greater power, the control itself is not exact and depends on the proportion of true null hypotheses. Because this proportion is unknown, it has to be accurately (small bias, small variance) estimated, preferably using a simple calculation that can be made accessible to the general scientific community. We propose an easy-to-implement method and make the R code available, for estimating the proportion of true null hypotheses. This estimate has relatively small bias and small variance as demonstrated by (simulated and real data) comparing it with four existing procedures. Although presented here in the context of microarrays, this estimate is applicable for many multiple comparison situations.

    Hongmei Jiang and R.W. Doerge