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Biostatistics Bioinformatics Core

Biostatistics Bioinformatics Core. Personnel Elizabeth Garrett, PhD Biostatistician Giovanni Parmigiani, PhD Biostatistician Data analysis and System support staff Hardware DELL server; linux OS Linux and Windows workstations Software GeneX Database; R-based analysis tools

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Biostatistics Bioinformatics Core

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  1. Biostatistics Bioinformatics Core Personnel Elizabeth Garrett, PhD Biostatistician Giovanni Parmigiani, PhD Biostatistician Data analysis and System support staff Hardware DELL server; linux OS Linux and Windows workstations Software GeneX Database; R-based analysis tools Labs: Affy Suite, others TBA

  2. Contact Information Elizabeth S. Garrett esg@jhu.edu Suite 1103, 550 Building 410-614-2588 Giovanni Parmigiani gp@jhu.edu Suite 1103 550 Building 410-614-3426

  3. Aims of the Biostatistics Core Specific Aim 1: To provide biostatistical consultation and support to projects in the program. Special emphasis will be to assist in visualization, analysis, quantitative modeling and interpretation of results.

  4. Aims of the Biostatistics Core Specific Aim 2: To help in identifying the appropriate data structures; ensuring data quality and data confidentiality; and developing efficient data transferring and interfacing for data analysis and data visualization under different platforms.

  5. Two important stages where we get involved Before the study: How can I best address my hypothesis using minimal resources to get maximal information? • Planning Stage: • Experimental Design • How many samples? • How many replicates? • Housekeeping genes? • Dye swapping? • What’s the big deal? You could spend a lot of time and money and not able to answer your questions due to experimental errors, etc. After the study: Now that I have this enormous amount of data, how do I summarize it and answer my questions? • Analysis Stage: • Visualization • Data Exploration • Analytic Tools and Models

  6. What we do • One-on-one consultations with investigators for planning experiments • One-on-one consultations with investigators for visualization, dataexploration, and analysis. • Tutorials for helping investigators use some of the software for exploration and visualization independently. • Tutorials on basic statistical concepts, including experimental design in gene expression studies and basic analytic tools.

  7. GeneX • Web based database, data mining, and data analysis tool • Supports * multiple users * multiple species * multiple microarray platforms Common Denominator for data analysis

  8. GeneX Components • Curation Tool (imports data) • Database (OpenSource SQL) • XML Data Exchange Protocol • Query and analytic routines -- mining -- biostatistics in R

  9. Analytical Tools and Applications Included or Co-developed with GeneX • Clustering • Visualization • Principle Component Analysisand Multi-Dimensional Scaling • Significance testing with R • Integration with other databases

  10. Regulation of extracellular matrix changes and fibrosis in inflammatory bowel disease. Shukti Chakravarti Feng Wu Department of Medicine Johns Hopkins University

  11. Control TNBS TNBS-colon

  12. TNBS-induced colitis model TNBS dose time points (weeks) 8 0 2 4 6 12 inflammation Disease initiation fibrosis Harvest • RNA • Protein • Histology • Intestinal fibroblasts

  13. ECM/fibrosis activity inflammation time

  14. Analysis Plan • Expression estimates using dChip • Additional normalization for scanner effect • Two-level regression model • Identification of reliably estimable time trends in gene expression • Grouping genes by patterns

  15. Normalization

  16. Empirical Bayes Ranking versus Statistical Significance FDR< 1/2 P-value < .05

  17. Patterns of gene expression over time Red: positive slope, low fdr Orange and Brown: low p-value Green: negative slope, low fdr

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