Biostatistics Bioinformatics Core

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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

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Contact Information

• Elizabeth S. Garrett
• esg_at_jhu.edu
• Suite 1103, 550 Building
• 410-614-2588
• Giovanni Parmigiani
• gp_at_jhu.edu
• Suite 1103 550 Building
• 410-614-3426

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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.

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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.

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Two important stages where we get involved

• Planning Stage
• Experimental Design
• How many samples?
• How many replicates?
• Housekeeping genes?
• Dye swapping?
• Whats the big deal? You could spend a lot of
  time and money and not able to answer your
  questions due to experimental errors, etc.

Before the study How can I best address my
hypothesis using minimal resources to get
maximal information?
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

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What we do

• One-on-one consultations with investigators for
  planning experiments
• One-on-one consultations with investigators for
  visualization, data exploration, 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.

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GeneX

• Web based database, data mining,
• and data analysis tool
• Supports
• multiple users
• multiple species
• multiple microarray platforms

Common Denominator for data analysis
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GeneX Components

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

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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

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Regulation of extracellular matrix changes and
fibrosis in inflammatory bowel disease.

• Shukti Chakravarti
• Feng Wu
• Department of Medicine
• Johns Hopkins University

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TNBS-colon
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ECM/fibrosis
activity
inflammation
time
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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

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Normalization
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Empirical Bayes Ranking versus Statistical
Significance

• FDR lt 1/2

P-value lt .05
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Patterns of gene expression over time
Red positive slope, low fdr
Orange and Brown low p-value
Green negative slope, low fdr