ArrayTrack

--- Data management, analysis and interpretation

tool for DNA microarray and beyond

ArrayTrack – A brief history in the 5 years Development Cycle

• AT version 1 (2001) – Filter array; data management tool;

• AT version 2 (2002): in-house microarray core facility– Customized two color arrays; data management, analysis

and interpretation; – Open to public (late of 2003)

• AT version 3.1 (2004): VGDS– Affymetrix; analysis capability enhanced;

• AT version 3.2 (2005): MAQC– Tested on 7 commercial platforms (Affy, Agilent one- and

two-color arrays, ABI, CodeLink, Illumina …); – Integrated with other software (IPA, MetaCore, DrugMatrix,

CEBS, SAS/JMP …) • AT version 4 (2006 – present)

– CDISC/SEND standard;– VGDS VXDS

Pub data(Gene annotation,

Pathways …)

Study data(Clinical and

non-clinical data)

ArrayTrack: Client-Server Architecture

Analysis Tools

MicroarrayProteomics

MetabolomicsSERVER

CLIENT

CDISC/SEND MIAME NCBI, KEGG, GO …

Microarray data

Proteomics data

Metabolomics data

Chemical data

Clinical and non-clinical

data

Public data

ArrayTrack

ArrayTrack: An Integrated Solution

ArrayTrack Websitehttp://www.fda.gov/nctr/science/centers/toxicoinformatics/ArrayTrack/

MicroarrayDB

LIB

TOOL

ArrayTrack: MicroarrayDB-LIB-TOOL- An integrated environment for microarray data management, analysis

and interpretation

uploading

Geneselection

Exploring

Interpretation• pathways• GO

Data interpretation

Data management

Data analysis

Exp Design

Microarray Exp

ArrayTrack for Microarray Data Management and Analysis

Hypothesis

MicroarrayDB

GeneLib

GeneTools

ArrayTrackComponents

MicroarrayDB

MicroarrayDB – Storing data associated with a microarray exp

Microarray database:

• Handling both one- and two-channel data, including affy data

• Only the CEL file is required for affy data

• Supporting toxicogenomics research by storing tox parameters, e.g., dose schedule and treatment, sacrifice time

• MIAME supportive to capture the key data of a microarray experiment

• Will be MAGE-ML compliant to ensure inter- exchangeability between ArrayTrack and other public databases

MicroarrayDB

LIBPublic Databases

HumanGenomeProject

HumanGenomeProject

HumanGenomeProject

HumanGenomeProject

MirroredDatabases

LIB Component – Containing functional information for microarray data interpretation

Functional data:• Individual gene analysis• Pathway-based analysis• Gene Ontology – based analysis• Linking expression data to the

traditional toxicological data

MicroarrayDB

LIB

TOOL

TOOL Component- Containing functionality for microarray data analysis

Analysis tools:• Four normalization methods

– Mean/median scaling for affy data – LOWESS for 2-color array

• Gene selection method– T-test, permutation t-test, …– Filtering using fold changes, intensity, flag

inf …– Volcano plot, p-value plot …

• Data exploring (e.g., HCA, PCA)• Many visualization tools (e.g., flexible

scatter plot, Bar chart viewer,…

Importing data

Normalization

Gene Selection

Interpretation

Data exploring

Apply to

Apply to

Supporting Eight Platforms • Affy, Agilent, ABI, Combimatrix,

Eppendorf, GE Healthcare, Illumina and customized arrays

• Affy data– Probe data (.cel file)– Probe-set data

Batch import

Individual hyb import

MicroarrayDB

LIB

TOOL

Data uploading and QC

Four normalization methods, including LOWESS

Significant genes can be identified based on:Cut-off of p-value (with or without Banferroni correction), fold-change, intensity or combinations thereofVolcano Plot (considering both p and fold-change)P-Value Plot (considering false positives/negatives)

Importing data

Normalization

Gene Selection

Interpretation

Data exploring

Apply to

Apply to

Pathway analysis

Gene Ontology analysis

Individual gene analysis

PCA

2-way HCA

Scatter Plot

Expression pattern using the bar chart plot

Data Interpretation- GO-based analysis using GOFFA

• GOFFA – Gene Ontology For Functional Analysis• It is developed based on Gene Ontology (GO) database• Important for grouping the genes into functional classes • GO – Three ontologies

– Molecular function: activities performed by individual gene products at the molecular level, such as catalytic activity, transporter activity, binding

– Biological process: broad biological goals accomplished by ordered assemblies of molecular functions, such as cell growth, signal transduction, metabolism

– Cellular component: the place in the cell where a gene product is found, such as nucleus, ribosome, proteasome

Study DB

TOOL

Study domain

MicroarrayDB

TOOL

Array domain

LIB

Importing data

Normalization

Gene Selection

Interpretation

Data exploring

Apply to

Apply to

Data InterpretationGOFFA: Gene Ontology-based tool

Pathway-based tools:• Ingenuity Pathways Analysis• KEGG• PathArt

Gene Annotation

Ingenuity Pathways Analysis (IPA)

• KEGG and PathArt provide canonical pathways• IPA provides both canonical and de-novo pathways

               Interrogate genes

or proteins on “omics” scale

Conduct statistical analysis

Elucidate functional pathways

Understand markers of efficacy

and safety

Ingenuity Pathways Analysis

Review Tool for Pharmacogenomics Data Submission: ArrayTrack

Receive the data; support future

regulatory policy

Verify the biological

interpretationAnalyze the

data

MicroarrayDB LibTool

ArrayTrack Components

Data repository Analysis Interpretation

ProteinLib PathwayLib

ProteinTools

ProteomicsDB

PathwayTools

MetabonomicsDB

ToxicantLib

Future Direction - Toxicoinformatics Integrated System (TIS)

MicroarrayDB

GeneLib

GeneTools

Data uploading and QC

Four normalization methods, including LOWESS

Significant genes can be identified based on:Cut-off of p-value (with or without Banferroni correction), fold-change, intensity or combinations thereofVolcano Plot (considering both p and fold-change)P-Value Plot (considering false positives/negatives)

Importing data

Normalization

Gene Selection

Interpretation

Data exploring

Apply to

Apply to

Pathway analysis

Gene Ontology analysis

Individual gene analysis

PCA

2-way HCA

Scatter Plot

Expression pattern using the bar chart plot

ArrayTrack – Summary

• An integrated solution for microarray data management, analysis and interpretation

• Review tool for FDA pharmacogenomics data submission– Training course is provided to the FDA reviewers every two months

– At present, ~40 reviewers has been trained

• Freely available to public (http://edkb.fda.gov/webstart/arraytrack)

• Users at big Pharma, academic and government institutions; U.S., Europe & Asia

ArrayTrack TutorialTopics Contents

1. (Basic)

Comparing two groups (e.g., treated vs control groups) Statistical methods (t-test, permutation t-test, ANOVA) for group comparison. Differentially Expressed Genes (DEGs) identification Biological interpretation (individual gene analysis) using LIB Pathway analysis (KEGG, PathArt, IPA, MetaCore, Key Molnet) Gene Ontology analysis using GOFFA

2. Comparing multiple groups (e.g., multiple doses, time points)

3

VennDiagram Determine the common genes/pathways/functions shared by two or three gene lists (extended to cross-experiment and –platform comparison and systems biology) Apply VennDiagram to the external files

4

Data exploring tools: Principal Component Analysis (PCA) Hierarchical Cluster Analysis (HCA) Apply HCA and PCA to the external files Extensive features in HCA

Topics Contents

5

Assessing gene expression profiles using BarChart Access BarChart from the TOOL box Access BarChart from the t-test result table Access BarChart from ChipLib and other Libs How to use BarChart for cross-experiment comparison Assign group by color

6

GeneList – An important concept in ArrayTrack Create a gene list through data filtering and statistical analysis Import/export a gene list Conduct normalization filtered by a gene lists Conduct statistical analysis (t-test/ANOVA, PCA, HCA and others)

based on a gene list Export a dataset by specifying the gene list (extended for cross-

platform and cross-experiment comparison)

7Normalization methods For Affymetrix platform: MAS5, RMA, DChip, Plier, Plier+16 For other platforms: 7 methods (e.g., LOWESS)

8 How to create your own workspace Copy/Paste/duplicate an experiment

9

Import/Export Manual import and batch importOptions of data exporting Export a selected dataset with specifying a sub list of geneExport multiple experiments and/or platforms using selected geneID types (e.g., RefSeq)

10

Other useful functionsCorrelation matrixIDConverter – converting one gene ID to another (e.g., from AffyID to AgilentID or GeneBank#, or LocusLinkID or vice verse)ScatterPlot – pair-wise plotJoinTable – Combine two tablesSplitTable – If a table contains multiple hybridization data in column with genes in row, the function split the table into individual tables with single hybridization data.GetUniqueID – If a table contains duplicated IDs, the function pick out the unique IDs

11

Basic scripting for querying (raw and normalized) data and table Query data from the database tree (How to use *)

e.g., *EST, EST*, *EST*=ESTQuery data in tables e.g., contain, like (%) and inlist