# Presentation: 949679

## Presented by 

**John Quackenbush, Ph.D.**
**at the **
**June 10, 2003**
**meeting of the**
**Pharmacology Toxicology Subcommittee**
**of  the**
**Advisory Committee for Pharmaceutical Science**

## Challenges in Data Management and Analysis for Microarrays

**FDA**
**10 June 2003**

## Selecting the Appropriate Platform

## Affymetrix GeneChipTM Expression Analysis

**Design and **
**synthesize chips**

_**Affymetrix GeneChipTM Expression Analysis**_

**Generate DNA**
**Sequence**

**ACGTAGCTAGC****TGATCGTAGC****TAGCTAGCTAGCTGATC**

## Affymetrix GeneChipTM Expression Analysis

## Microarray Overview I

_**Microarray Overview I**_

## Microarray Gene Chip Overview II

## Microarray Expression Analysis

**Gene**

_**Microarray Expression Analysis**_

## Lack of standardization makes direct comparison of results a challenge
Lot-to-log variation in arrays can introduce artifacts – are the results dependent on the biology or on the arrays (or technician or reagent lots or ....)
Commercial arrays provide a standard and remove some design considerations (one sample, one array), but cost up to 10x (or greater) more than in-house arrays
Arrays demand good LIMS systems for sample tracking

**Lot-to-log variation in arrays can introduce artifacts – are the results dependent on the biology or on the arrays (or technician or reagent lots or ....)**
**Commercial arrays provide a standard and remove some design considerations (one sample, one array), but cost up to 10x (or greater) more than in-house arrays**
**Arrays demand good LIMS systems for sample tracking**

_**Platform-related issues**_

## Microarray Analysis

## Choose an experimentally interesting and tractable model system
Design an experiment with comparisons between related variants 
Include sufficient biological replication to make good estimates
Hybridize and collect data
Normalize and filter
Mine data for biological patterns of expression
Integrate expression data with other ancillary data such, including genotype, phenotype, the genome, and its annotation

**Design an experiment with comparisons between related variants **
**Include sufficient biological replication to make good estimates**
**Hybridize and collect data**
**Normalize and filter**
**Mine data for biological patterns of expression**
**Integrate expression data with other ancillary data such, including genotype, phenotype, the genome, and its annotation**

_**General Microarray Strategy**_

## Annotating andComparing Arrays

## TIGR Gene Indices home page 

_**www.tigr.org/tdb/tgi**_

**~60 species**
**>16,000,000 sequences**

**Notes:**
There databases now represent more than 50 species and include over 10,000,000 sequences.

## The Mouse Gene Index <http://www.tigr.org/tdb/mgi>

_**The Mouse Gene Index**_** ****<http://www.tigr.org/tdb/mgi>**

## A TC Example

**A TC Example**

## GO Terms and EC Numbers

**Babak Parvizi**

_**GO Terms **__**and EC Numbers**_

## The TIGR Gene Indices <http://www.tigr.org.tdb/tdb/tgi>

**Dan Lee, Ingeborg Holt**

## Building TOGs: Reflexive, Transitive Closure

_**Building TOGs: Reflexive, Transitive Closure**_

**Thanks to Woytek Makałowski and Mark Boguski **

## TOGA: An Sample Alignment: bithoraxoid-like protein 

## Slide 20

## Gene Finding in Humans

_**Gene Finding in Humans**_

_**is easy!**_

**Razvan Sultana**

## Gene Finding in Humans

_**Gene Finding in Humans**_

_**is easy?**_

**Razvan Sultana**

## Gene Finding in Humans

_**Gene Finding in Humans**_

_**is difficult?**_

**Razvan Sultana**

## Gene Finding in Humans

_**Gene Finding in Humans**_

_**is difficult?**_

**Razvan Sultana**

**A genome and its annotation is *****only***** a hypothesis that must be tested.**

## RESOURCERER 

_**RESOURCERER **_

**Jennifer Tsai**

## RESOURCERER: An Example

## RESOURCERER: Using Genetic Markers

_**RESOURCERER: Using Genetic Markers**_

**Next step: Integrate QTLs**

## The “complete” genome is incomplete
Gene names are not yet well defined
One gene may have many names
One gene may have many sequences
One sequence may have many names
Analysis and interpretation depends on well annotated gene sets
Gene names, Gene Ontology Assignments, and pathway information
Cross-species comparisons require good knowledge of orthologues and paralogues

**Gene names are not yet well defined**
**One gene may have many names**
**One gene may have many sequences**
**One sequence may have many names**
**Analysis and interpretation depends on well annotated gene sets**
**Gene names, Gene Ontology Assignments, and pathway information**
**Cross-species comparisons require good knowledge of orthologues and paralogues**

_**Annotation Issues**_

## Tools and Techniques

_**for Array Analysis**_

## Design the experiment
Perform the hybridizations and generate images
Analyze images to identify genes and expression levels (hybridization intensities)
Normalize expression levels to facilitate comparisons
Analyze expression data to find biologically relevant patterns

**Perform the hybridizations and generate images**
**Analyze images to identify genes and expression levels (hybridization intensities)**
**Normalize expression levels to facilitate comparisons**
**Analyze expression data to find biologically relevant patterns**

_**Analysis steps**_

## MADAM: Microarray Data Manager

_**MADAM: Microarray Data Manager**_

****** ****Available with OSI source and MySQL**

**Joseph White**
**Jerry Li**
**Alexander Saeed**
**Vasily Sharov**
**Syntek Inc.**

**MAGE-ML export****by June**

## Why Normalize Data?

**In a self-self hybridization, we would expect all ratios to be equal to one:****	**** R*****i*****/G*****i***** = 1 for all *****i*****.   But they may not be.**
**Why not?**
** ****Unequal labeling efficiencies for Cy3/Cy5**
** ****Noise in the system**
** ****Differential expression**
**Normalization brings (appropriate) ratios back to one.**

_**Why Normalize Data?**_

## The Starting Point: The R-I Plot

**Uncertainty in measurements is greater at lower intensities**
**Uncertainty in ratio measurements generally greater at lower intensities**
**Plot log****2****(R/G) *****vs.***** log****2****(R*G)**
**[variation: Terry Speed’s M-A plot with**
**	****(1⁄2 )*log****2****(R*G)]**

_**The Starting Point: The R-I Plot**_

## Lowess Normalization

** ****Why LOWESS?**

_**Observations**_
**Intensity-dependent structure**
**Data not mean centered at log****2****(ratio) = 0**

## LOWESS (Cont’d)

** ****Local linear regression model **
** ****Tri-cube weight function **
** ****Least Squares**

## LOWESS Results

## MIDAS: Data Analysis

**Wei Liang**

****** ****Available with source**

**Variance Stabilization,**
**Adding Error Models,**
**MAANOVA,**
**Automated Reporting**

## MeV: Data Mining Tools

**Alexander Saeed**
**Alexander Sturn**
**Nirmal Bhagabati**
**John Braisted**
**Syntek Inc.**
** ****Datanaut, Inc.**

****** ****Available with OSI source**

## Multiple Experiments?

**This is a problem in data mining**
**“****Clustering Algorithms” are most widely used**
**Types**
** ****Agglomerative: Hierarchical**
** ****Divisive: *****k*****-means, SOMs**
** ****Others: Principal Component Analysis (PCA)**
**All depend on how one measures distance**

_**Multiple Experiments?**_

## Expression Vectors

**Crucial concept for understanding clustering**
**Each gene is represented by a vector where coordinates are its values log(ratio) in each experiment**
**  *****x***** = log(ratio)****expt1**
**  *****y***** = log(ratio)****expt2**
**  *****z***** = log(ratio)****expt3**
**  ****etc.**

_**Expression Vectors**_

## Expression Vectors

**Each gene is represented by a vector where coordinates are its values log(ratio) in each experiment**
**  *****x***** = log(ratio)****expt1**
**  *****y***** = log(ratio)****expt2**
**  *****z***** = log(ratio)****expt3**
**  ****etc.**
**For example, if we do six experiments, **
** ****Gene****1**** = (-1.2, -0.5, 0, 0.25, 0.75, 1.4) **
** ****Gene****2**** = (0.2, -0.5, 1.2, -0.25, -1.0, 1.5) **
** ****Gene****3**** = (1.2, 0.5, 0, -0.25, -0.75, -1.4) **
**  ****etc.**

_**Expression Vectors**_

## Expression Matrix

_**Expression Matrix**_

**Expt 1**
**Expt 2**
**Expt 3**
**Expt 4**
**Expt 5**
**Expt 6**

**Gene****1****     -1.2    -0.5          0       0.25     0.75        1.4 **
**Gene****2****      0.2    -0.5       1.2      -0.25      -1.0        1.5 **
**Gene****3****      1.2     0.5          0      -0.25    -0.75       -1.4**
**etc.**
**This is often represented as a red/green colored matrix**

## Expression Matrix

**The Expression Matrix is a representation of data from multiple microarray experiments.**

**Each element is a log ratio, usually ****    log ****2 ****(Cy5/Cy3) **

**Red indicates a **
**positive log ratio, i.e, Cy5 > Cy3 **

**Green indicates a**
**negative log ratio , i.e.,**
**Cy5 < Cy3  **

**Black indicates a log**
**ratio of zero, i. e., **
**Cy5 and Cy3 are very **
**close in value **

**Gray indicates missing data **

_**Expression Matrix**_

## Expression Vectors As Points in‘Expression Space’

**Experiment 1**

**Experiment 2**

**Experiment 3**

**Similar Expression**

## There is no standard method for data analysis
The same algorithm with a small change in parameters (such as distance metric) can produce very different results
Data normalization plays a big role in identifying “differentially expressed” genes
Much of the apparent disparity in microarray datasets can be attributed to differences in data analysis methods, from image processing to normalization to data mining

**The same algorithm with a small change in parameters (such as distance metric) can produce very different results**
**Data normalization plays a big role in identifying “differentially expressed” genes**
**Much of the apparent disparity in microarray datasets can be attributed to differences in data analysis methods, from image processing to normalization to data mining**

_**Analysis Issues**_

## Data Reporting Standards

## What data should we collect?

_**What data should we collect?**_

***Nature Genetics***** 29, December 2001**

**<http://www.mged.org>**

**MAGE-ML – XML-based data exchange format**

_**EVERYTHING**_

## Publications on Microarray Data Exchange Standards

_**Publications on Microarray Data Exchange Standards**_

_**MIAME Standards:**_
**Nature family, Cell family, EMBO reports, Bioinformatics,****Genome Research, Genome Biology, Science, The Lancet,****Science, and others....**

## MIAME Standards are a start, but still evolving
Implementation will require further development of ontologies to create standard descriptors
MIAME-Tox <http://www.mged.org/MIAME1.1-DenverDraft.DOC> represents an attempt to extend this to toxicology
Software must be developed to read/write MAGE-ML
Public databases need to be extended to meet Tox needs

**Implementation will require further development of ontologies to create standard descriptors**
**MIAME-Tox ****<http://www.mged.org/MIAME1.1-DenverDraft.DOC>**** represents an attempt to extend this to toxicology**
**Software must be developed to read/write MAGE-ML**
**Public databases need to be extended to meet Tox needs**

_**Standardization Issues**_

## Science

## Integrating Expression

_**with other data**_

## Innate Immunity

_**Innate Immunity**_

**Adaptive Immunity**

**Pathophysiologic Conditions**

**Immunomodulatory Genes**

**Sepsis**
**ARDS**
**Asthma**

**Antigen Presentation**

**Cytokines and**
**Adhesion Proteins**

**CD14**

**LPS**

**TLR Proteins**

**NF-********B**

**I********B**

**Inflammatory**
**Cell Recruitment**

**LBP**

**Degradation**

**NIK**

**TRAF-6**

**MyD88**

**IRAK2**

**BPI**

**Adapted from Godowski.  *****NEJM*****  1999; 340:1835**

**MD-2**

**David Schwartz**

## Result: ~425 “significant” genes

_**Examples**_

**Result: ~425 “significant” genes**

## Examples

_**Examples**_

_**IDEA**_**: Build QTL Maps and use those****to filter expression data**

**Goal: Find differentially expressed genes genetically linked to response**

## Microarray Expression-QTL Consensus Candidate Genes

_**Microarray Expression-QTL Consensus Candidate Genes**_

## Candidate Gene Set for LPS response

**BG085317****	**** arginase type II (Arg2)****	**
**BG064781****	**** cytidine 5'-triphosphate  synthase (Ctps)****	**
**BG085740****	**** ets-related transcription  facto****	**
**BG063515****	**** ferritin heavy chain  (Fth)****	**
**BG078398****	**** MARCKS-like protein (Mlp)**
**AW556835****	**** protein tyrosine  phosphatase, non-receptor type 2 (Ptpn2)**
**BG077485****	**** ring finger protein (C3HC4  type) 19 (Rnf19)****	**
**BG085186****	**** surfactant protein-D  gene****	**
**AW550270****	**** tenascin C (Tnc)****	**
**BG065761****	**** tumor necrosis factor,  alpha-induced protein 2 (Tnfaip2)**
**BG074379****	****co-chaperone mt-GrpE#2 precursor putative ****	**
**BG080688****	****CSF-1****	**
**BG067349****	****C-type lectin Mincle****	**
**BG073439****	****DKFZp564O1763****	**
**AW551388****	****E2F-like  transcriptional repressor protein****	**
**BG076460****	****glutamate-cysteine  ligase catalytic subunit (GLCLC) **
**BG080666****	****gly96****	**
**BG067921****	****GTP binding protein****	**
**BG072974****	****DKFZp547B146****	**
**BG070296****	****DKFZp566F164****	**
**BG074109****	****Hsp86-1****	**
**BG077487****	****hypoxia inducible factor 1****	**

**BG078274****	****I kappa B alpha gene****	**
**BG084405****	****IAP-1****	**
**BG069214****	****inhibitor of apoptosis  protein 1**
**BG067127****	****interferon regulatory factor  1**
**BG080268****	****KC****	**
**BG070106****	****lipocalin****	**
**BG064651****	****MAIL****	**
**BG063925****	****metallothionein II****	**
**BG077818****	****metallothionein-I****	**
**BG073108****	****MHC class III region RD****	**
**BG064928****	****mitogen-responsive 96****	**
**BG072801****	****S100A9****	**
**BG086320****	****SDF-1-beta****	**
**BG072793****	****T-cell activating protein****	**
**BG073446****	****TH1 protein****	**
**BG072227****	****TNFa****	**
**BG068491****	****	**
**BG071081****	****	**
**BG067341****	****	**
**BG067620****	****	**
**BG067670****	****	**
**BG066678****	****	**
**BG071169****	****	**

_**Candidate Gene Set for LPS response**_

## Sleep Deprivation Studies in Mouse

**    ****z**
**  ****z**
**z**

_**Sleep Deprivation Studies in Mouse**_

_**Experimental Paradigm**_

**Compare gene expression between sleeping and ****sleep-deprived mice in cortex and hypothalamus**
**Perform 3 biological replicates**
**Normalize and filter data and use data mining techniques to select distinct patterns of gene expression**
**Use Gene Ontology (GO) assignments to classify genes by cellular localization, molecular function, biological process**
**Use GO analysis to develop an understanding of response**

## Differential Expression in Cortex

_**Differential Expression in Cortex**_

## Differential Expression in Hypothalamus

**Sleep signaling**

## Predicting Outcome

## The problem

**Histology can provide some information, but these have little predictive power.**
**Microarrays provide a “fingerprint” that can serve as a phenotypic measure that may be linked to outcome.**
**This is a huge problem in data mining.**

_**The problem**_

## The problem in pictures: Adenocarcinomas

_**The problem in pictures: Adenocarcinomas**_

## 32k Human Arrays

_**32k Human Arrays**_

## cDNA Multi-Organ Cancer Classifier

**77 tumor samples; 144 hybridization assays**

## Neural Networks and Cancer

_**Neural Networks and Cancer**_

## Neural Networks and Cancer

_**Training:**_
**Adjusts weights****and connections**

_**Neural Networks and Cancer**_

## 543 tumor samples

| Tumor Type | Number of Samples | Array Platform |
| --- | --- | --- |
| Bladder | 19 | U95, HU6800 |
| Breast | 42 | U95, HU6800, TIGR 32k |
| Central Nervous – Atypical Teratoid/Rhandoid | 10 | HU6800 |
| Central Nervous Glioma | 10 | HU6800 |
| Central Nervous - Medulloblastoma | 70 | HU6800 |
| Colon | 41 | U95, HU6800, TIGR 32k |
| Stomach/EG Junction | 30 | U95, TIGR 32k |
| Kidney | 31 | U95, HU6800, TIGR 32k |
| Leukemia – Acute Lymphocyite B Cell | 10 | HU6800 |
| Leukemia – Acute Lymphocyite T Cell | 10 | HU6800 |
| Leukemia – Acute Myelogenous | 10 | HU6800 |
| Lung – Adenocarcinoma | 71 | U95, HU6800, TIGR 32k |
| Lung – Squamous Cell Carcinoma | 21 | U95 |
| Lymphoma - Follicular | 11 | HU6800 |
| Lymphoma – Large B Cell | 11 | HU6800 |
| Melanoma | 10 | HU6800 |
| Mesothelioma | 10 | HU6800 |
| Ovary | 44 | U95, HU6800, TIGR 32k |
| Pancreas | 26 | U95, HU6800, TIGR 32k |
| Prostate | 42 | U95, HU6800 |
| Uterus | 10 | HU6800 |

_**Tumors in the Universal Classifier**_

**543 tumor samples**
**21 tumor types**
**95% of all cancers**

## Data Acquisition

**Microarray Database**

## Summary

** ****21 tumor types**
** ****95% of all cancers**
**10 Independent Classifiers**
** ****75% of data for training, 25% for test**
*** *****Average ~88% accuracy **
**Web based Classifier available**
** ****So far, 7 of 8********* in classification**
** ****84% accuracy in classifying primary source**
**	**** ****of mets**
**	****	***** Bad RNA**

_**Summary**_

## Further challenges in analysis?

**If you perturb a system, *****many***** genes change their expression levels**
**Multiple pathways and features in the data can be revealed through different analysis methods**
**Genes which are good for classification or prognostics may not be biologically relevant**
**Extracting meaning from microarrays will require new software and tools**
**The most important thing we need is *****more***** data collected and stored in a standard fashion**

_**Further challenges in analysis?**_

## Barriers to Toxicology Applications

**Many of the signatures we see on arrays do not have immediate biological implications**
**Most often genes are included on the arrays that are used solely for normalization**
**Larger datasets may reveal diagnostic or prognostic patterns that are not obvious at present**
**Reported “variation” in the assays must be understood**
** ****Differences in laboratory and analysis protocols are****	**** likely sources**
** ****There is a need to define QC and analysis standards**
**There is clearly a need for a large database of expression profiles linked to other relevant ancillary information**

_**Barriers to Toxicology Applications**_

## Science is built with facts as a house is with stones – but a collection of facts is no more a science than a heap of stones is a house.

** ****	****	****	****	****	****– ****Jules Henri Poincare**

## Nobody in the game of football 

**should be called a genius. **
**A genius is somebody like Norman Einstein. **
**-Joe Theisman, Former quarterback**

## Acknowledgments

**Foo Cheung**
**Svetlana Karamycheva**
**Yudan Lee**
**Babak Parvizi**
**Geo Pertea**
**Razvan Sultana**
**Jennifer Tsai**
**John Quackenbush**
**Joseph White**

**Funding provided by the Department of Energy****and the National Science Foundation**

_**TIGR Human/Mouse/Arabidopsis Expression Team**_** **
**Emily Chen**
**Bryan Frank**
**Renee Gaspard**
**Jeremy Hasseman**
**Heenam Kim**
**Lara Linford**
**Simon Kwong**
**John Quackenbush**
**Shuibang Wang**
**Yonghong Wang**
**Ivana Yang**
**Yan Yu**
_**Array Software Hit Team**_
**Nirmal Bhagabati**
**John Braisted**
**Tracey Currier**
**Jerry Li**
**Wei Liang**
**John Quackenbush**
**Alexander I. Saeed**
**Vasily Sharov**
**Mathangi Thaiagarjian**
**Joseph White**
_**Assistant**_
**Sue Mineo**

**Funding provided by the National Cancer Institute,****the National Heart, Lung, Blood Institute,****and the National Science Foundation**

_**H. Lee Moffitt Center/USF**_
**Timothy J. Yeatman**
**Greg Bloom**

_**TIGR PGA Collaborators**_
**Norman Lee**
**Renae Malek**
**Hong-Ying Wang**
**Truong Luu**
**Bobby Behbahani**** **

**TIGR Faculty, IT Group, and Staff**

**<johnq@tigr.org>**

_**PGA Collaborators**_
**Gary Churchill (TJL)**
**Greg Evans (NHLBI)**
**Harry Gavaras (BU)**
**Howard Jacob (MCW)**
**Anne Kwitek (MCW)**
**Allan Pack (Penn)**
**Beverly Paigen (TJL)**
**Luanne Peters (TJL)**
**David Schwartz (Duke)**

_***Emeritus***_
**Jennifer Cho (TGI)**
**Ingeborg Holt  (TGI)**
**Feng Liang (TGI)**
**Kristie Abernathy (mA)****Sonia Dharap(mA)****Julie Earle-Hughes (mA)****Cheryl Gay (mA)****Priti Hegde (mA)****Rong Qi (mA)**
**Erik Snesrud  (mA)**