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 Labs: Affy Suite, others TBA

Contact Information

Elizabeth S. Garrettesg@jhu.eduSuite 1103, 550 Building410-614-2588

Giovanni Parmigianigp@jhu.eduSuite 1103 550 Building410-614-3426

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.

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.

Two important stages where we get involved

• 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.

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 summarizeit and answer my questions?

• Analysis Stage:– Visualization– Data Exploration– Analytic Tools and Models

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.

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

Common Denominator for data analysis

GeneX Components

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

Analytical Tools and Applications Included or Co-developed with GeneX

• Clustering• Visualization• Principle Component Analysis

and Multi-Dimensional Scaling• Significance testing with R• Integration with other databases

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

Shukti ChakravartiFeng Wu

Department of MedicineJohns Hopkins University

TNBS-colon

Control

TNBS

TNBS-induced colitis modelTNBS dose time points (weeks)

Harvest

0 2 4 6 12

• RNA • Protein • Histology • Intestinal fibroblasts

Disease initiation

fibrosis

8

inflammation

acti

vity

time

inflammation

ECM/fibrosis

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

Normalization

FDR < 1/2

Empirical Bayes Ranking versus Statistical Significance

P-value < .05

Patterns of gene expression over time

Red: positive slope, low fdrGreen: negative slope, low fdr Orange and Brown: low p-value