# Presentation: 555973

## ArrayTrack – Supporting Microarray Studies Through Data Integration

***Weida Tong, Ph.D***
**Center for Toxicoinformatics**
**National Center for Toxicological Research (NCTR), FDA**
**e-mail: wtong@nctr.fda.gov**
**Tel: (870)543-7142**

## Acknowledgement

- CENTER FOR TOXICOINFORMATICS

_**Team**_
**Roger Perkins**
**Huixiao Hong**
**Megan Cao**
**Steve Harris**
**Hongmei Sun**
**Hong Fang**
**Qian Xie**

_**Collaborators**_
**Jim Fuscoe**
**Varsha Desai**
**Yvonne Dragan**
**Angela Harris**
**Bob Delongchamp**
**John Bowyer**

**Acknowledgement**

**Ralph Kodell, Division Director**
**Dan Casciano, NCTR Director**

## ArrayTrack

**Microarray**
**Database**

**GeneLib**

**ProteinLib**

**PathwayLib**

**ToxicantLib**

**INTERFACE**

**Tools**

**Public**

**Commercial**

**In-house**

- Functional
- components

**ArrayTrack**

**User**

## Bioinformatics Issues in Microarray Exp

- How to manage data
- Which data should be stored ?
- Validated database or just data storage ?
- How to integrate in-house data with public repositories
- Which databases ?
- Local mirror DBs versus web link ?
- How to analyze and visualize data
- Which analysis should be standardized ?
- Buy versus develop ?
- How to integrated microarray data with other omics data

## Bioinformatics Issues in Microarray Exp

**How to manage data**
**Which data should be stored ?**
**Validated database or just data storage ?**
- How to integrate in-house data with public repositories
- Which databases ?
- Local mirror DBs versus web link ?
- How to analyze and visualize data
- Which analysis should be standardized ?
- Buy versus develop ?
- How to integrated microarray data with other omics data

## Microarray Database

- Validated database
- Data been entered only if they meet prescribed standards for completeness, accuracy (QA/QC), ontology and normalization
- Rich resources for data mining and experiment comparison
- MIAME standards: How minimum is minimum
- MIAME (Minimum Information About a Microarray Experiment)
- Results can be interpreted
- Experiment can be independently verified
- Standard normalization and QC approaches

## Slide 7

## Bioinformatics Issues in Microarray Exp

- How to manage data
- Which data should be stored ?
- Validated database or just data storage ?
**How to integrate in-house data with public repositories**
**Which databases ?**
**Local mirror DBs versus web link ?**
- How to analyze and visualize data
- Which analysis should be standardized ?
- Buy versus develop ?
- How to integrated microarray data with other omics data

## Public Databases  

- Many public databases (over 500)
- Gene: GenBank, UniGene, Genome Database ...
- Gene annotation: GeneCards, LocusLink, OMIM, GO...
- Protein: SWISS-PROT, PROSITE, PDB, PIR ...
- Pathway: KEGG, PathDB, ...
- Species-oriented: RGD, RATMAP, MGI ...
- Many committee and consortium to define standard nomenclature, annotation, terminology and ontology

## Integration of Microarray Results with Public Data

- Link or Download ?
- Link: low maintenance, but one gene at time
- Download: high maintenance, but many genes for comparison
- Both approaches have been used in ArrayTrack
- Constructing local mirror databases (Libraries)
- Present: LocusLink, SWISS-PROT, RATMAP, UniGene, KEGG, GO
- Web link: GeneCards, GenBank, OMIM, PubMed...

## Slide 11

## GeneLib

**GeneLib**

## Detail Info

**Detail Info**

**Additional links**

## Link to the chips
Select a proper chip 
Design a customized chip
Compare the information between chips

- Link to the chips
- Select a proper chip
- Design a customized chip
- Compare the information between chips

- Import gene lists

## Bioinformatics Issues in Microarray Exp

- How to manage data
- Which data should be stored ?
- Validated database or just data storage ?
- How to integrate in-house data with public repositories
- Which databases ?
- Local mirror DBs versus web link ?
**How to analyze and visualize data**
**Which analysis should be standardized ?**
**Buy versus develop ?**
- How to integrated microarray data with other omics data

## Analysis and Visualization

**Analysis and Visualization**
**Functions**

**GeneLib**

**Link**

**PathwayLib**

**ProteinLib**

## ArrayTrack - ScatterPlot Viewer

## Fold change

**Fold change**
**Intensity cutoff**

**Artificial dye flip**

**Link to:**
**- GeneLib **
**- ProteinLib **
**- PathwayLib**

## Decision Forest- A novel pattern recognition approach for genomics, proteomics and SAR data analysis

- 	Assumption: A better classification can be reached by combining the results from several individual models.

- Key points
- Combining several identical models produce no gain
- Combining several highly correct models that disagree as much as possible** **

## Decision Forest - Highlights

- It produce correct classification for the samples that are likely missed by a single model
- Results are reproducible
- Computational inexpensive
- Easy to be operated by the non-expert users
- No gene selection and significance analysis are required

**Decision Forest - Highlights**

- Tong et al. Decision Forest – Combining multiple independent models for prediction, JCICS (in press)
- Tong et al. From Decision Tree to Decision Forest – A novel chemometrics method for structure activity relationship modeling, Chemometrics and Chemoinformatics, ACS Book

## Data Analysis

**& QC**

**Significance**
**Analysis**

**Classification**

**Clustering**

**Data Analysis**

**Gene Selection**

## Classification of Leukemia Subtypes

**Data Source:** E. J. Yeoh, et al, Cancer Cell: March 2002, Vol. 1, 133-143
**Objective:** Classification and prediction for subtype of pediatric acute lymphoblastic leukemia from microarray data
**Array**: AffyChip (HG_U95Av2)
**Genes**: 12625 genes
**Samples**: 327 diagnostic bone marrow samples

**Classification of Leukemia Subtypes**

## Decision Forest - Training Results

**Decision Forest - Training Results**

**Fitting results**

## Validation

**327 Samples**

**293 for training**

**30  for testing**

**Misclassification**

**prediction**

**Randomize**

**20 times**

## Decision Forest - Validation Results

**Decision Forest - Validation Results**

**Prediction results**

## Bioinformatics Issues in Microarray Exp

- How to manage data
- Which data should be stored ?
- Validated database or just data storage ?
- How to integrate in-house data with public repositories
- Which databases ?
- Local mirror DBs versus web link ?
- How to analyze and visualize data
- Which analysis should be standardized ?
- Buy versus develop ?
**How to integrated microarray data with other omics data**

## ArrayTrack: DB-Lib-Tool Structure

**DB**

**GeneLib**

**GeneTools**

**ArrayTrack: DB-Lib-Tool Structure**

**SNPs or SAGE**
**DBs**

**Genomics**
**DBs**

## Proteomics Data

**SpotFetch**

**ProteinLib**

**ProteinTools**

**Proteomics Data**

## Toxicoinformatics Integrated System (TIS) 

**ProteinTools**

**Toxicoinformatics Integrated System (TIS) **

## Genes  Pathway  Chemicals 

**Genes ******** Pathway ******** Chemicals **

## Slide 31