![](media/image1.png){width="5.933333333333334in"
height="2.6534722222222222in"}

**User's Manual**

Version 3.1.5

> ![](media/image2.png){width="1.788888888888889in"
> height="1.788888888888889in"}

Center for Toxicoinformatics

National Center of Toxicological Research

U.S. Food and Drug Administration

**User's Manual**

An Integrated Software System for the Support of Toxicogenomics Research
through Managing, Mining, Visualizing, and Interpreting DNA Microarray
Gene Expression Data

Center for Toxicoinformatics

National Center for Toxicological Research

U.S. Food and Drug Administration

3900 NCTR Road

Jefferson, Arkansas 72079

U.S.A.

Te: +1-870-543-7142, +1-870-543-7387

Fax: +1-870-543-7662

<NCTRBioinformaticsSupport@nctr.fda.gov>

<Weida.Tong@fda.hhs.gov>

<http://edkb.fda.gov/webstart/arraytrack/>

August 1st, 2005

![](media/image3.png){width="2.6041666666666665in"
height="1.1458333333333333in"}

Notice

ArrayTrack software is constantly evolving with both major and minor
features, improvements, and inevitable bug fixes. This manual is for use
with ArrayTrack major release version 3.1.5. While we strive for
consistency between the manual and version 3.1.5, you might observe some
slight differences. For updated information, please check the ArrayTrack
web site at <http://edkb.fda.gov/webstart/arraytrack/>, where, for
example, updated manuals for version 3.1.5 will be periodically
provided.

TIPS

> Many functions in ArrayTrack are accessible from multiple paths, for
> example, left-side window panels, pull-down menus and right
> mouse-click options.

1.  Right-click on a (set of) selected object(s) under the Database
    Contents tree to access the applicable TOOL functions.

2.  Multiple sets of arrays can be selected by a combinations
    mouse-click and SHIFT-CTRL keys.

3.  Most functions come with default parameter settings. If you do not
    know a better setting, use the default.

4.  All Spreadsheet viewers share similar functions, e.g. Copy/Paste of
    selected table content.

**Table of Contents**

[Chapter 1 Introduction 8](#introduction)

[What Is ArrayTrack? 8](#what-is-arraytrack)

[Center for Toxicoinformatics of the NCTR/FDA
8](#center-for-toxicoinformatics-of-the-nctrfda)

[Toxicoinformatics Integrated System (TIS)
8](#toxicoinformatics-integrated-system-tis)

[ArrayTrack - A Prototype of TIS 10](#arraytrack---a-prototype-of-tis)

[MicroarrayDB 10](#__RefHeading___Toc112231260)

[LIB 10](#__RefHeading___Toc112231261)

[TOOL 11](#__RefHeading___Toc112231262)

[Availability of ArrayTrack 11](#availability-of-arraytrack)

[Online Version 11](#__RefHeading___Toc112231264)

[Local Installation 11](#__RefHeading___Toc112231265)

[Installing and Running Online Version of ArrayTrack
11](#installing-and-running-online-version-of-arraytrack)

[System Requirements 11](#__RefHeading___Toc112231267)

[For Windows Users 12](#__RefHeading___Toc112231268)

[For Mac OS X Users 12](#__RefHeading___Toc112231269)

[For Linux/Unix Users 12](#__RefHeading___Toc112231270)

[Installing and Running Local Version of ArrayTrack
12](#installing-and-running-local-version-of-arraytrack)

[Chapter 2 Working with Database: MicroarrayDB
14](#working-with-database-microarraydb)

[Input Form
14](#input-form-inputting-experimental-information-and-gene-expression-data-including-creating-a-new-experiment)

[Overview 14](#__RefHeading___Toc112231274)

[Experiment Design 15](#__RefHeading___Toc112231275)

[Hybridization and Data 15](#__RefHeading___Toc112231276)

[Data Import 17](#__RefHeading___Toc112231277)

[Data Export: for information about exporting data please refer
23](#__RefHeading___Toc112231278)

[ArrayType Information 23](#arraytype-information)

[Overview 23](#__RefHeading___Toc112231280)

[Activate ArrayType Information 23](#activate-arraytype-information)

[Export ArrayType Information 23](#__RefHeading___Toc112231282)

[Create New ArrayType 24](#__RefHeading___Toc112231283)

[Data Sharing and Security Protection
26](#data-sharing-and-security-protection)

[Exploring and Viewing Data in MicroarrayDB from Tree View
27](#exploring-and-viewing-data-in-microarraydb-from-tree-view)

[Structure of Database Contents 27](#__RefHeading___Toc112231286)

[Exploring Database Contents 27](#__RefHeading___Toc112231287)

[Operating on Experiment: 28](#__RefHeading___Toc112231288)

[Operating on Hybridization 29](#__RefHeading___Toc112231289)

[Operating on Array Data 29](#__RefHeading___Toc112231290)

[Significant Gene List 29](#significant-gene-list)

[Display Significant Gene List 30](#__RefHeading___Toc112231292)

[Import Significant Gene List 32](#__RefHeading___Toc112231293)

[Export Significant Gene List 33](#__RefHeading___Toc112231294)

[Delete Significant Gene List 33](#__RefHeading___Toc112231295)

[To create significant gene list 33](#__RefHeading___Toc112231296)

[VennDiagram 33](#venndiagram)

[Chapter 3 Working with Libraries 37](#working-with-libraries)

[Gene Library 38](#gene-library)

[Working with the Table 38](#working-with-the-table)

[Filter Genes: 38](#__RefHeading___Toc112231301)

[Sort Table by Column 39](#__RefHeading___Toc112231302)

[Rearrange Table Columns 39](#__RefHeading___Toc112231303)

[Searching Functional Information for a List of Genes
39](#searching-functional-information-for-a-list-of-genes)

[More Information about a Gene 40](#__RefHeading___Toc112231305)

[Link to Other Public Databases 41](#__RefHeading___Toc112231306)

[Sort Genes Based on Their Common Pathway and Proteins
41](#__RefHeading___Toc112231307)

[Customize Table 41](#__RefHeading___Toc112231308)

[Export Table Contents to a Local File 41](#__RefHeading___Toc112231309)

[Copy/Paste selected rows/columns 42](#__RefHeading___Toc112231310)

[Stop a Search Session 42](#__RefHeading___Toc112231311)

[Launch Bar Chart for Selected Genes 42](#__RefHeading___Toc112231312)

[Pathway Library 42](#pathway-library)

[Ingenuity Library 49](#ingenuity-library)

[Protein Library 51](#protein-library)

[IPI Library 53](#ipi-library)

[Orthologene Library 53](#orthologene-library)

[GOFFA Library 54](#goffa-library)

[Chip Library 60](#chip-library)

[Display ArrayType Information 60](#__RefHeading___Toc112231322)

[Compare Different ArrayTypes 61](#__RefHeading___Toc112231323)

[Summary: 62](#__RefHeading___Toc112231324)

[Toxicant Library 62](#toxicant-library)

[EDKB Library 64](#edkb-library)

[ID Converter 66](#id-converter)

[Chapter 4 Working with Tools: Quality Control
68](#working-with-tools-quality-control)

[Overview of TOOL 68](#overview-of-tool)

[Overview of Quality Control 69](#overview-of-quality-control)

[Launch of Quality Control 69](#launch-of-quality-control)

[From the TOOL Panel 69](#__RefHeading___Toc112231332)

[From the TOOL Pull-down Menu 69](#__RefHeading___Toc112231333)

[From Right-click on Selected Arrays in the
69](#__RefHeading___Toc112231334)

[Contents of Quality Control View 70](#contents-of-quality-control-view)

[Preview of Scatterplot 70](#__RefHeading___Toc112231336)

[Preview of Rank Intensity Plot 70](#__RefHeading___Toc112231337)

[Quality Control Parameters 70](#__RefHeading___Toc112231338)

[Overall Judgment on Array Quality 70](#__RefHeading___Toc112231339)

[Function Buttons for Quality Control 71](#__RefHeading___Toc112231340)

[Overview of Quality Filtering 71](#overview-of-quality-filtering)

[Launch of Quality Filtering 72](#__RefHeading___Toc112231342)

[Contents of Quality Filtering 72](#contents-of-quality-filtering)

[Functions for Quality Filtering 73](#__RefHeading___Toc112231344)

[Chapter 5 Working with Tools: Normalization
74](#working-with-tools-normalization)

[Overview 74](#overview)

[Lowess 76](#lowess)

[Smoothing Factor 77](#__RefHeading___Toc112231348)

[Robustness Iterations 77](#__RefHeading___Toc112231349)

[Delta 77](#__RefHeading___Toc112231350)

[Total Intensity Normalization 78](#total-intensity-normalization)

[Channel Scaling 78](#channel-scaling)

[GenePix Mean Log Ratio Normalization
79](#genepix-mean-log-ratio-normalization)

[Linear and Lowess 80](#linear-and-lowess)

[Warning 80](#__RefHeading___Toc112231355)

[Chapter 6 Working with Tools: Analysis Tools
81](#working-with-tools-analysis-tools)

[Overview 81](#__RefHeading___Toc112231357)

[T-test and ANOVA 81](#t-test-and-anova)

[P-value Plot 87](#p-value-plot)

[Clustering 89](#clustering)

[PCA 91](#pca)

[Chapter 7 Working with Tools: Visualization
93](#working-with-tools-visualization)

[Overview 93](#overview-1)

[Scatter Plot 94](#scatter-plot)

[Default Plot 94](#__RefHeading___Toc112231367)

[Selected Spots 94](#__RefHeading___Toc112231368)

[Search for Genes in This Viewer 95](#__RefHeading___Toc112231369)

[Axes Options 95](#__RefHeading___Toc112231370)

[Show Lowess curve 95](#__RefHeading___Toc112231371)

[Adjust Lowess parameters 95](#__RefHeading___Toc112231372)

[Save as image 96](#__RefHeading___Toc112231373)

[MA Plot 96](#ma-plot)

[Mixed Scatter Plot 97](#mixed-scatter-plot)

[Rank Intensity Plot 98](#rank-intensity-plot)

[Choosing Data Source for Plotting
98](#choosing-data-source-for-plotting)

[From MicroarrayDB Arrays 98](#__RefHeading___Toc112231378)

[From Local Data Files 98](#__RefHeading___Toc112231379)

[Virtual Array Viewer 99](#virtual-array-viewer)

[Overview 99](#__RefHeading___Toc112231381)

[Adjust Brightness of Virtual Array Image
99](#__RefHeading___Toc112231382)

[Zoom In and Out of Virtual Array Image
99](#__RefHeading___Toc112231383)

[Filter Spots 100](#__RefHeading___Toc112231384)

[Pull-Down Functions on Spots 101](#__RefHeading___Toc112231385)

[Pull-down Options 101](#__RefHeading___Toc112231386)

[Select and Deselect Current Spot 101](#__RefHeading___Toc112231387)

[Mark Spots 102](#__RefHeading___Toc112231388)

[Right-click Accessible Functions 102](#__RefHeading___Toc112231389)

[Functions Applicable to Marked Spots 102](#__RefHeading___Toc112231390)

[Actual Array Viewer 102](#actual-array-viewer)

[Bar Chart 103](#bar-chart)

[Overview 103](#__RefHeading___Toc112231393)

[Query Specification 103](#__RefHeading___Toc112231394)

[Bar Chart Display 103](#__RefHeading___Toc112231395)

[Toolbars 104](#__RefHeading___Toc112231397)

[Table Display 106](#__RefHeading___Toc112231400)

[Links between Bar Chart and Table 106](#__RefHeading___Toc112231401)

[Summary 107](#summary)

[Chapter 8 Working with Tools: DMVS 108](#working-with-tools-dmvs)

[Chapter 9 Data Export 109](#data-export)

[How to Access Data Export Functions
109](#how-to-access-data-export-functions)

[Options for Data Export 109](#options-for-data-export)

[Export selected datasets as spreadsheet
111](#export-selected-datasets-as-spreadsheet)

[Preview selected datasets as spreadsheet
111](#preview-selected-datasets-as-spreadsheet)

[Export original files for selected datasets
112](#export-original-files-for-selected-datasets)

[Export data in a narrow format 112](#export-data-in-a-narrow-format)

[Export in TIGR MeV format 112](#export-in-tigr-mev-format)

[Export Gene ID matches 112](#export-gene-id-matches)

[Chapter 10 What to Expect from ArrayTrack 4.0
114](#what-to-expect-from-arraytrack-4.0)

[References 115](#references)

[ArrayTrack Team 115](#arraytrack-team)

[Acknowledgments 115](#acknowledgments)

#  Introduction

## What Is ArrayTrack?

ArrayTrack is an integrated software system for managing, mining,
visualizing, and interpreting microarray gene expression data. This
software has been developed by the Center for Toxicoinformatics at the
National Center for Toxicological Research (NCTR) of the U.S. Food and
Drug Administration (FDA) and is continuously updated. ArrayTrack is a
module of a comprehensive software system described below, the
Toxicoinformatics Integrated System (TIS), that is being developed to
integrate analysis of genomic, proteomic, metabonomic data and
toxicology data.

## Center for Toxicoinformatics of the NCTR/FDA

The mapping of the human genome and the determination of corresponding
gene functions, pathways, and biological mechanisms are driving the
emergence of the new research fields of toxicogenomics and systems
toxicology. Many technological advances such as microarrays are enabling
this paradigm shift that portends an unprecedented advancement in the
methods of understanding the expression of toxicity at the molecular
level. At the NCTR/FDA, core facilities for genomic, proteomic, and
metabonomic technologies have been established that utilize standardized
experimental procedures to support center-wide toxicogenomic research.
Collectively, these facilities are continuously generating an
unprecedented volume of data.

To effectively meet the challenges of modern toxicological research, the
NCTR/FDA established the Center for Toxicoinformatics on June, 2002.
Toxicoinformatics is an emerging scientific discipline that integrates
approaches from multidisciplinary fields of bioinformatics,
chemoinformatics, computational toxicology, informatics technologies,
and physiologically-based pharmacokinetic modeling with the objectives
of knowledge discovery and the elucidation of mechanisms of toxicity.
The primary function of the Center for Toxicoinformatics is to apply and
develop toxicoinformatics approaches for omics research and traditional
toxicological studies at NCTR and beyond to FDA. More information about
the Center for Toxicoinformatics can be found at
<http://www.fda.gov/nctr/science/centers/toxicoinformatics/index.htm>.

## Toxicoinformatics Integrated System (TIS)

The NCTR's Center for Toxicoinformatics has been developing a
Toxicoinformatics Integrated System (TIS) for the purpose of fully
integrating genomic, proteomic, and metabonomic data with data in the
public repositories, as well as conventional in vitro and in vivo
toxicology data. Figure 1-1 illustrates the TIS architecture organized
around three major components: central in-house data archives (DB), a
set of libraries with highly relevant information downloaded from public
databases (LIB), and analysis and visualization functions (TOOL). The DB
component contains a set of relational databases, each storing
experimental platform-specific data (e.g. microarray data for genomics,
MS data for proteomics, and NMR data for metabonomics) together with
annotation information about the experiments and samples. The TOOL
component provides the ability to query, visualize, mine, analyze, and
correlate diverse data from both local and public resources. The LIB
component hosts information from public databases on genes, proteins,
pathways, and small chemicals involved in the pathways or toxicological
experiments. Through integration of different data types with analysis
capabilities, TIS will be able to extract a tailored dataset for data
interpretation, hypothesis generation, and hypothesis testing to aid
toxicogenomics studies.

Below the TIS component level is the software module level. Software
modules are associated with a particular class of data that, in turn, is
associated with a particular type of experimental platform (e.g.,
microarray, *in vitro* endpoint, *in vivo* endpoint, or protein gel).
Each software module for each data type/experimental platform can be
constructed independently, and the overall system can be developed in
accordance with extant priorities and experiment progress.

TIS will integrate microarray gene expression data, proteomics data, and
metabolite profiling data with classic in vivo or in vitro toxicology
data. Expression profiles of a suspected toxicant may provide unique
signatures that can be readily compared with expression patterns of
known toxicants stored in the TIS. In addition, TIS will consolidate
data in a manner conducive to development of models to predict toxic
endpoints based on chemical structures and/or gene expression profiles.
With TIS, expression data from mechanistic-based assays and *in vivo*
pathology or clinical observations can be readily compared, possibly
leading to development of less expensive and more timely assays for risk
assessment. Finally, expression profiling may become an important
component for diagnostics in medicine and TIS could be a significant
benefit to aid FDA regulators in evaluating new diagnostic tools that
are based on "omics" technologies.

![](media/image4.png){width="6.020833333333333in" height="4.6875in"}

Figure 1‑1: An overall system architecture of the Toxicoinformatics
Integrated System (TIS).

The system is based on a DB-TOOL-LIB integrated structure. (1) The DB is
a central data archive for in-house data storage and management; (2) The
TOOL provides data visualization and analysis functions; and (3) The LIB
a set of libraries that contain data both from online public databases
as well as NCTR in-house databases that together integrate information,
for example, on sequence, gene annotation, gene and protein function,
pathways, and toxicant profiles and chemical structure.

## ArrayTrack - A Prototype of TIS

ArrayTrack is a prototype version of TIS and has been developed to
effectively handle and analyze DNA microarray data. ArrayTrack is
logically constructed of three linked components: (1) MicroarrayDB, a
database that stores microarray experiment information and gene
expression data in accordance with MIAME (Minimum Information About a
Microarray Experiment); (2) LIB, a set of libraries that mirror critical
data in a number of public databases; and (3) TOOL, a set of tools that
operate on experimental and public data for data analysis,
visualization, and knowledge discovery purposes. An overview of
ArrayTrack is shown in Figure 1-2 and more detailed descriptions of the
three components follows.

![](media/image5.png){width="4.626388888888889in"
height="3.5965277777777778in"}

Figure 1‑2: ArrayTrack's three main components: MicroarrayDB, LIB, and
TOOL.

[]{#__RefHeading___Toc112231260 .anchor}**MicroarrayDB**: MicroarrayDB
(DB) is part of ArrayTrack's ORACLE-based relational database that
stores microarray experimental data. As implemented at NCTR, DB data
complies with rigorous criteria to assure a validated microarray
repository that will be a rich resource for data mining and experiment
comparison. Data may be entered if and only if it meets prescribed
standards for completeness, accuracy, normalization as well as
conformance to the applicable ontology. Such disciplined data curation
requires toxicoinformaticians and biologists work closely together to
understand both the structure of the database and the structure of the
data to be stored. An NCTR microarray data submission form was developed
that specifies the required essential information from both microarray
and toxicology perspectives. The format is adheres to the MIAME/Tox
([M]{.underline}inimum [I]{.underline}nformation [A]{.underline}bout a
[M]{.underline}icroarray [E]{.underline}xperiment for Toxicogenomics)
guideline for toxicogenomic research.

[]{#__RefHeading___Toc112231261 .anchor}**LIB**: LIB is part of
ArrayTrack's ORACLE-based relational database that comprises nine
libraries: Gene Library, Pathway Library, Protein Library, IPI Library,
Orthologene Librrary, GOFFA Library, Chip Library, Toxicant Library, and
EDKB Library, that mirror the most essential information in public
databases related to genes, proteins, pathways, toxicants, and as well
as detailed data for various microarray systems (chips). Library data
sources include UniGene, LocusLink, GeneCards, SWISS-PROT, and KEGG from
which information on gene annotation, protein function, and metabolic
pathways are obtained. Efforts are underway to incorporate other types
of information (e.g. PubMed) from public databases into LIB. The
libraries are highly interlinked within ArrayTrack, and are frequently
updated to reflect the ever-increasing and refined data within the
public repositories. LIB and its use are described in detail in Chapter
3. Scientists not analyzing microarray data will nonetheless find much
utility in using ArrayTrack to query public data owing to the nature of
the integration of information from disparate data sources.

[]{#__RefHeading___Toc112231262 .anchor}**TOOL**: The TOOL section of
ArrayTrack provides functions for microarray data visualization,
normalization, significance analysis, clustering, classification and,
importantly data quality assessment (QA) and control (QC). Several
standard or common methods for data normalization, analysis,
visualization, and QA/QC of data are available. Novel or essential tools
to enhance our capabilities that are tailored to toxicology-specific
problems are implemented or in development. A number of data
visualization capabilities have already been developed. Additionally,
given the ever-increasing commercial and public software packages
providing common or specialized data analysis capabilities for
microarrays, we have developed interfaces/interface formats for other
software packages to conveniently access and analyze data stored in
ArrayTrack.

It is important to point out that many of the various functions in
ArrayTrack are accessible from either the pull-down menus or the
left-side panels, and thus a particular function may be invoked by
different sequences of operations.

To illustrate the logical operation of ArrayTrack, the user can select
an analysis method from the TOOL, apply the method to selected
microarray data stored in the MicroarrayDB, and the analysis results can
be directly linked to information in the LIB. The user can also
hyperlink from data within ArrayTrack to the corresponding detailed data
in the many supported public data repositories. In this way, ArrayTrack
can be very helpful to scientists in interpreting the biological meaning
of microarray results by convenient access to information on genes,
proteins, and pathways, etc.

## Availability of ArrayTrack

Currently, ArrayTrack is being distributed free of charge by the
NCTR/FDA to the research community.

[]{#__RefHeading___Toc112231264 .anchor}**Online Version**: Users within
the fda.gov domain can download and run ArrayTrack at
<http://weblaunch.nctr.fda.gov/jnlp/arraytrack/index.html>, and have
access to ORACLE-based DB to store microarray data. For the users
outside of the fda.gov domain, they can download and run ArrayTrack at
<http://edkb.fda.gov/webstart/arraytrack/>. Users outside the fda.gov
domain can access all of the functions of ArrayTrack except for
uploading microarray gene expression data since at the time of this
writing a decision has been made not to use the online version of
ArrayTrack as a public repository for microarray data.

[]{#__RefHeading___Toc112231265 .anchor}**Local Installation**: For
those who are seeking to have the entire client-server system installed
at their local sites for independent use, please contact
<NCTRBioinformaticsSupport@nctr.fda.gov> to request the CD or DVD. In
this case, you will need Oracle license to run ArrayTrack locally.

## Installing and Running Online Version of ArrayTrack

[]{#__RefHeading___Toc112231267 .anchor}**System Requirements**:
ArrayTrack is a client-server system. To date, the software has been
tested on the Windows operating systems (98/NT/2000/XP), Linux/Unix, and
Mac OS X. If you have a problem running ArrayTrack on your system,
please contact <NCTRBioinformaticsSupport@nctr.fda.gov>. The application
screens are best viewed at 1024x768 (or higher) resolution. For Windows
users, we suggest a Pentium 266 MHz or faster processor with at least
256 MB of physical RAM to support the graphical applications.

[]{#__RefHeading___Toc112231268 .anchor}**For Windows Users**: To use
the online version of ArrayTrack, go to
<http://edkb.fda.gov/webstart/arraytrack/> and follow two simple steps:
(1) Install Java if it is not already installed on your machine, using
the provided link to the Sun Microsystems web site in step one (The
program will automatically detect whether Java is already installed on
your machine. If so, you can skip this step). (2) Install and run
ArrayTrack.

[]{#__RefHeading___Toc112231269 .anchor}**For Mac OS X Users**: If you
have kept up to date with your updates from Apple, then you should
already have Java 1.4(+) available on your machine, in which case you
should be able to either (1) just click the link in the second step to
start the application, or (2) failing that, save the jnlp file which is
the target of the second step's link
(<http://edkb.fda.gov/webstart/arraytrack/arraytrack_ext.jnlp>) to your
hard drive, then double click it to run the application.

[]{#__RefHeading___Toc112231270 .anchor}**For Linux/Unix Users**:
Install the Java Runtime Environment for your platform from
<http://java.sun.com/getjava/>, then execute the javaws command which
should be contained within the Java installation directory (exact path
to it will vary), like so:

javaws http://edkb.fda.gov/webstart/arraytrack/arraytrack_ext.jnlp #
(for users external to FDA), or

javaws
http://weblaunch.nctr.fda.gov/jnlp/arraytrack/arraytrack_internal.jnlp
\# (for users within FDA).

You can also use this link (<http://edkb.fda.gov/webstart/arraytrack/>)
in the future to run ArrayTrack, or let ArrayTrack place icons (Figure
1-4) on your desktop and start menu when prompted. You will notice some
delay the first time you run ArrayTrack due to the need to download the
entire ArrayTrack application (Figure 1-5). You may also be prompted to
update your version of Java before ArrayTrack itself is started. Future
uses of the software will only download parts of the application that
have been changed, if any, and should start much quicker. For the
non-FDA users, due to the FDA firewall, you may experience delays when
using this online version of ArrayTrack. Interested users may request a
CD or DVD for local installation, which will greatly increase speed.

When you activate the ArrayTrack, you will see the login window (see
Figure 1-3). If you don't have an account you can just click cancel
button or leave the fields blank then click OK button to login and view
the demo data.

![](media/image6.png){width="3.442361111111111in"
height="2.428472222222222in"}

Figure 1‑3: Login to ArrayTrack

## Installing and Running Local Version of ArrayTrack

If you requested a CD or DVD for local installation of ArrayTrack, you
will receive detailed instructions for installation and how to configure
your local microarray database(s). Please note that for local
installation, your systems must have ORACLE 9i or higher installed.
Updates for the various libraries in LIB are available for download at
<http://edkb.fda.gov/webstart/arraytrack/>.

Enjoy yourself by following the instructions to be discussed in the
following chapters!

![](media/image7.png){width="0.6694444444444444in"
height="0.7493055555555556in"}

Figure 1‑4: The ArrayTrack icon appears on the desktop of a computer
after installation.

![](media/image8.png){width="2.6805555555555554in"
height="1.2416666666666667in"}

Figure 1‑5: Downloading and running the online version of ArrayTrack.

#  Working with Database: MicroarrayDB

MicroarrayDB has been designed to store DNA microarray gene expression
experimental data together with essential annotation information about
an experiment, its protocol and the samples. MicroarrayDB supports data
from both one-channel (e.g. filter arrays, Affymetrix GeneChip^®^
arrays, and Amersham CodeLink^®^ arrays) and two-channel (e.g. spotted
cDNA or oligo arrays) microarray platforms and adheres to the MIAME
guideline for microarray experiments
(<http://www.mged.org/Workgroups/MIAME/miame.html>). A set of tools have
been developed for managing, normalizing, visualizing, analyzing, and,
importantly, for performing QA/QC of data stored in the database.

## Input Form: Inputting Experimental Information and Gene Expression Data including Creating a New Experiment

*NOTE: In the publicly accessible online version of ArrayTrack, the data
uploading function in Input Form is disabled.*

[]{#__RefHeading___Toc112231274 .anchor}**Overview**: According to
MIAME, an experiment consists of a set of hybridizations. The database
structure in ArrayTrack has been arranged in an
ExperimentHybridizationArray Data hierarchical, tree-like format (for
details, see Figure 2-27).

![](media/image9.png){width="5.998611111111111in"
height="4.401388888888889in"}

> Figure 2‑6: Input Form: Data entry form of MicroarrayDB**.**

Under the Database window panel of functions (Figure 2-6), the user can
select the ![](media/image10.png){width="0.7493055555555556in"
height="0.25277777777777777in"} button to activate Input Form (Figure
2-6) that allows the user to enter all the MIAME-specified information
about the experiment. Before any information may be input into Input
Form, the user is prompted to specify a unique title for the experiment
and experiment group name, as shown in Figure 2-7.

![](media/image11.png){width="4.460416666666666in"
height="2.3020833333333335in"}

Figure 2‑7: The user needs to specify an Experiment ID and Experiment
Group Name for the new experiment.

The Owner ID will automatically show the name of the user whoever logged
into the Windows system, and is grayed out. After the owner created an
experiment, he can click
![](media/image12.png){width="0.8743055555555556in"
height="0.18333333333333332in"} (see Figure 2-6) to assign different
privileges (view or/and write) to the other users; otherwise no body can
view/edit the experiment except the owner. The Exp Group Name is
arbitrary.

The information items on the Input Form are straightforward and
reasonably self-explanatory. The Input Form consists of two sequential
sections: (1) Experiment Design, and (2) Hybridization and Data. Just
follow the Input Form to enter all required (highlighted in red) and the
remaining optional information items.

[]{#__RefHeading___Toc112231275 .anchor}**Experiment Design**: The
description of an experiment and its associated experimental protocols
are input in the Experiment Design section. The information in
Experiment Design only needs to be filled in and saved once for an
entire experiment, since such experiment design information remains the
same for all hybridizations of the same experiment. However, t[he user
first must click]{.underline}
![](media/image13.png){width="0.7770833333333333in"
height="0.18194444444444444in"} [before moving on to the Hybridization
and Data section]{.underline}. An existing Experiment can be deleted by
clicking on ![](media/image14.png){width="0.7215277777777778in"
height="0.18055555555555555in"} (see Figure 2-6).

[]{#__RefHeading___Toc112231276 .anchor}**Hybridization and Data**: This
section is used for input and/or update of data associated with all
hybridizations, arrays and samples included in the entire experiment.
From a logical perspective, it is best to consider an experiment to
consist of a set of hybridizations, each of which has an associated
microarray type, sample and label (e.g., florescent dye or radioisotope.
The amount and types of data to be entered are determined by many
factors of the experiment design. For example, single versus two channel
experiment, dye flip experiment and cDNA- versus oligonucleotide-based
experiments are major determinates of specifically what data must be
entered.

Data describing a hybridization can be input in the lower portion of
Input Form that is divided into three areas: 1) Hybridization; 2) Array
Information; and 3) Sample and Label Information (i.e., fluorescent
dye). The bright green IMPORT button
![](media/image15.png){width="0.3888888888888889in"
height="0.15069444444444444in"} in the lower right of the Input Form
enables importation from a file of the actual microarray data for each
hybridization. By the end of the data input, every hybridization in an
experiment will be linked with its appropriate array information, sample
and label information and its experiment data.

**[Hybridization]{.underline}**: The hybridization information is first
input in the section of the input form titled HYBRIDIZATION. The user
inputs the name of the hybridization associated with a specific sample.
The name is arbitrary and determined by the user, and would normally
provide some descriptive information. Multiple hybridization names can
be input and will then all appear in the pull-down menu. A hybridization
can only be saved after the array information and sample labeling
information for a sample has been entered. If the user later wants to
change the name of the hybridization, this is done by right clicking the
hybridization name in the database window and choosing the option "Edit
Hyb" name and editing the existing name (see Figure 2-8A).

**[Array Information]{.underline}**: Information for each array is input
in the section titled Array Information. There are more than 70 array
types predefined within ArrayTrack. If the array type the user is using
is one of those predefined in ArrayTrack, it can be selected from the
pull-down menu as shown in Figure 2-8B. Instructions for creating a new
array type is given further below on Page [23](#arraytype-information).

![](media/image16.png){width="2.94375in" height="1.8243055555555556in"}
![](media/image17.png){width="2.7215277777777778in"
height="1.9479166666666667in"}

> Figure 2‑8: Hybridization name editing (A) and pull-down selection of
> array type (B)

**[Sample and Label Information]{.underline}**: Information for each
sample and how it is labeled is input in the section titled "Sample and
Label Information". Clicking the
![](media/image18.png){width="0.5694444444444444in" height="0.15in"}
button (see Figure 2-9B) brings up the Sample Form that is shown in
Figure 2-10.

![](media/image19.png){width="2.998611111111111in"
height="0.9847222222222223in"}
![](media/image20.png){width="2.8756944444444446in"
height="1.104861111111111in"}

> Figure 2‑9: Sample ID pull-down selection.

[The user must click]{.underline}
![](media/image21.png){width="0.8472222222222222in"
height="0.18333333333333332in"} [to save sample information to the
database]{.underline}. An existing Sample can be deleted by clicking on
![](media/image22.png){width="0.8743055555555556in"
height="0.17430555555555555in"} (see Figure 2-10).

If a sample (e.g. A-2HZN00A) is involved in several hybridization
records, the user needs to enter information only once. The remaining
hybridization records can be chosen from the Hybridization pull down
list (see Figure 2-4A). Similarly, to save time during input, the user
can load a similar sample already saved in the database, make minor
changes (e.g. SampleID or Sex) to the Sample Form, and then save the new
sample with a new name.

Notes: (1) A Sample is associated with only one Experiment and is not
shared across different Experiments. However, a Sample can be shared by
multiple Hybridizations within the same Experiment (e.g., technical
replication). The user can choose to finish inputting all samples
altogether before inputting any hybridization information as long as the
experiment is saved. When the user inputs hybridization information, all
the saved sample IDs will become selectable from the sample ID pull-down
list (Figure 2-9B).

The hybridization information is only saved when the user clicks the
![](media/image23.png){width="0.6659722222222222in"
height="0.2013888888888889in"} button, at which point the current
section of the form the user is in will be permanently saved into
database (an existing Hybridization can be deleted by clicking on
![](media/image24.png){width="0.7777777777777778in"
height="0.18888888888888888in"}).

![](media/image25.png){width="5.625in" height="4.554166666666666in"}

> Figure 2‑10: Sample information entry form (Sample Form)

[]{#__RefHeading___Toc112231277 .anchor}**[Data Import]{.underline}**:
User can import each dataset one by one or import multiple dataset in
one shot using the "Batch import" function. The "Batch import" function
is very convenient if you have many hybridizations of the same array
type. The two options are addressed as following:

***1. Individual import***

Microarray gene expression data is loaded into the database by first
clicking the ![](media/image15.png){width="0.3888888888888889in"
height="0.15069444444444444in"} button in the lower, right panel of the
**Input Form**. [The user must save the data in each section as the data
entry proceeds:]{.underline} The user must save the experiment
![](media/image13.png){width="0.6944444444444444in"
height="0.16041666666666668in"} before continuing to Hybridization and
Data section, and Data Import is not accessible until the Hybridization
has been saved ![](media/image23.png){width="0.5416666666666666in"
height="0.16944444444444445in"}.

Clicking the import button
![](media/image15.png){width="0.3888888888888889in"
height="0.15069444444444444in"} brings up the Select Files window that
prompts the user to select the Data File, up to two Image Files
(optional) and the scanner Settings File (optional) corresponding to the
current hybridization/array. Browse buttons
![](media/image26.png){width="0.5in" height="0.18333333333333332in"} are
provided to locate and select the file. After pushing the OK button, the
Choose Columns panel pops up that allows the user to map data columns in
the Data File to corresponding MicroarrayDB data fields (Figure 2-12).
Data columns already mapped will be shown in yellow and their
corresponding MicroarrayDB data fields will be marked with
![](media/image27.png){width="0.15833333333333333in"
height="0.14166666666666666in"}.

![](media/image28.png){width="4.625694444444444in"
height="1.4930555555555556in"}

Figure 2‑11: Select Files dialog for Data Import.

*Tip*: According to the data structure shown in Figure 2-8, one
hybridization can be associated with multiple arrays that could be a
result from scanning the same hybridized slide under different scanning
parameters or at different times. The "Data set description" field
(Figure 2-11) allows the user to input such descriptive information
(e.g. 33% scanning power) that will be shown within the "\<\>" under the
tree structure view.

*Tip*: The Data Import can be done separately from Input Form. The user
can input all hybridization information into the Input Form without
importing any data. Data import can be started later from the database
tree view by selecting the hybridization (right-click), select "Import
data".

The Data File must be in text format (Caution: Very large Excel files
could cause "out-of-memory" errors; it is recommended that large Excel
files be converted to text format prior to import). For a GenePix GPR
Data File, the mapping is automatically determined by ArrayTrack. For
other data files other than the GenePix GPR format, the user can easily
set the mapping by clicking on a column header and then clicking on its
corresponding MicroarrayDB data field (see Figure 2-12).

![](media/image29.png){width="2.6875in" height="2.9159722222222224in"}

Figure 2‑12: Choose Columns for loading array data file (GenePix GPR
file).

After the first successful import from any file format that has
recognizable column names at the top, the next time the same file format
that is used for the column choices should default to the previous
choices, with preference to the most recent import done by the same
user.

Clicking on OK at the bottom of Figure 2-12 will save the data for the
current hybridization/array to MicroarrayDB. If the Data File does not
match the ArrayType definition (e.g. unequal number of spot numbers),
ArrayTrack will give a warning and the user should take extreme care to
double check data consistency, such as array type or sample label, etc.
When the data is successfully loaded into MicroarrayDB, the Database
Contents tree will be updated to show the new hybridization/array.

For Affymetrix data, the user can directly import .CEL file. ArrayTrack
will automatically convert .CEL file to probe set file. This function
only works for FDA users. For non-FDA users they can not import .CEL
file because the Bioconductor server which is for the converting is not
accessible from out side of FDA firewall. But they can import probe set
file (in .txt format) instead of the .CEL file.

The Select Files dialogue (Figure 2-13B) provides 4 types of data file:
1).CEL file, 2).txt file (probe set file, optional), 3) .DAT file (image
file), 4) .CHP file. .CEL file and probe set file are the data files, so
the user needs to have either of the two formats of the file for data
import. If the user has probe set file then the .CEL is optional and
vice versa. DAT file and CHP file are image files and optional for data
importing. Since the image files are not used in calculations, any
format is acceptable.

![](media/image30.png){width="3.0833333333333335in"
height="1.7104166666666667in"}

![](media/image31.png){width="5.75in" height="1.4368055555555554in"}

Figure 2‑13: Importing Affymetrix data

***2. Batch import***

If the user needs to import multiple arrays (e.g. 10, or 50
hybridizations) of the same array type, s/he can use the "Batch import"
function. Before using the "Batch Import" function, the user needs to
prepare a hybridization file (in Excel format) containing all the
information about the data (like sample, label, the file names, etc),
see Figure 2-14.

The following is an example of batch importing 20 hybridizations.

After the hybridization file is ready, the user can start batch import.
There are several ways to activate "Batch Import" function: 1) from
"Database" pull-down menu, 2) if experiment has already been created,
right-clicking the experiment name and choose "Batch Import". See Figure
2-15. After choosing "Batch Import", the "Batch Import" window shows up
(Figure 2-16).

![](media/image32.png){width="3.196527777777778in"
height="2.265277777777778in"}

Figure 2‑14: Hybridization file for batch import

![](media/image33.png){width="1.94375in" height="1.34375in"}
![](media/image34.png){width="2.19375in" height="1.4784722222222222in"}

Figure 2‑15: Activate "Batch Import" function

![](media/image35.png){width="3.125in" height="3.6277777777777778in"}

Figure 2‑16: Batch import interface

In Figure 2-16, the user can fill the form by following step 1 through
step 4.

Step 1: choose the experiment name from the pull-down list. If
experiment is not created yet, the user can choose "New" from the
pull-down list and type a new experiment name (refer page
[14](#input-form-inputting-experimental-information-and-gene-expression-data-including-creating-a-new-experiment)
for creating new experiment). Then click
![](media/image36.png){width="0.5833333333333334in"
height="0.14583333333333334in"} button to locate the file root
directory.

![](media/image37.png){width="3.5in" height="1.4770833333333333in"}
![](media/image38.png){width="1.9166666666666667in"
height="1.4861111111111112in"}

Figure 2‑17: specifying experiment name and data file directory

Step 2: Load hybridization file which is the file you created before
batch-import. Click ![](media/image39.png){width="0.875in"
height="0.15625in"} button and choose the hybridization file. The
contents of the file will show up below the
![](media/image39.png){width="0.8645833333333334in"
height="0.15486111111111112in"} button (see Figure 2-18).

Step 3: Map hybridization file columns to database fields. First choose
array type from the pull-down list, if the list doesn't include the
array type you need, then you have to create a new array type (see page
[23](#arraytype-information) - ArrayType Information part for creating
new array type). Then choose species (Human, Rat, or Mouse, etc) and
assay (In vivo or In vitro). Next map the columns to database fields.

![](media/image35.png){width="3.7645833333333334in"
height="4.378472222222222in"}![](media/image40.wmf){width="0.16458333333333333in"
height="5.555555555555555e-2in"}![](media/image40.wmf){width="0.16458333333333333in"
height="5.555555555555555e-2in"}![](media/image40.wmf){width="0.16458333333333333in"
height="5.555555555555555e-2in"}![](media/image40.wmf){width="0.16458333333333333in"
height="5.555555555555555e-2in"}![](media/image40.wmf){width="0.16458333333333333in"
height="5.555555555555555e-2in"}![](media/image40.wmf){width="0.16458333333333333in"
height="5.555555555555555e-2in"}

Figure 2‑18: mapping the hybridization columns to database fields

In Figure 2-18, the blue arrows at the left side of the field buttons
means this field is a required field (the right-side empty box has to be
filled), all the others are optional. The user can click the cross sign
button ![](media/image41.png){width="0.1375in"
height="0.12430555555555556in"} at the left side to remove the database
button, the ![](media/image41.png){width="0.15625in"
height="0.14097222222222222in"} button at the right side is used to
clear the contents in the text box. To map the column to the database
field, click the column title then click the database field button. The
column title will show in the text box right to the button. The mapped
column will be highlighted in yellow. The user can save this configure
after mapping all the columns by clicking the button
![](media/image42.png){width="0.7604166666666666in"
height="0.13194444444444445in"} and open this configure file next time
for batch import.

If the user wants to map columns to the other database fields, he can
click ![](media/image43.png){width="0.875in"
height="0.16319444444444445in"} to bring up more database fields (Figure
2-19).

![](media/image44.png){width="4.040277777777778in"
height="3.7958333333333334in"}

Figure 2‑19: more database fields for choosing

In Figure 2-19, the database fields in the right panel are the fields
that will be shown in "Batch Import" interface (see Figure 2-16) while
the database fields in the left panel are more options available for
choosing. The user can move the database fields from left to right side
or vice versa. Some of the fields in the right panel are required fields
and can not be moved to the left side. The user can select any optional
field and bring it to the right side by click the arrow button.

Step 4: After mapping the columns to the database field, the user can
click ![](media/image45.png){width="0.90625in"
height="0.17847222222222223in"} button to preview the data information
before importing.

![](media/image46.png){width="4.332638888888889in"
height="1.7020833333333334in"}

Figure 2‑20: batch import preview window

In Figure 2-20, the first column lists the hybridization names that will
show in ArrayTrack Database panel. The user can click any column title
to sort the columns. If the user are not satisfied with the preview, he
can click "cancel" button and re-do the Step 1\~ 4. After previewing it,
click ![](media/image47.png){width="0.53125in"
height="0.18958333333333333in"} button to start importing. If the data
are imported successfully the user will see the message "The data are
imported without error".

The batch import function is very efficient and error-preventing when
importing data in large scale.

[]{#__RefHeading___Toc112231278 .anchor}**[Data Export]{.underline}:**
for information about exporting data please refer Chapter 9 Data Export

## ArrayType Information

[]{#__RefHeading___Toc112231280 .anchor}**Overview**: One essential item
that needs to be input in Input Form is ArrayType. ArrayType defines
basic information about the arrays with which an experiment is being
conducted. In ArrayTrack 3.1.3 release, more than 70 "standard" array
types have been pre-defined, including \~30 array types manufactured by
Affymetrix and many other cDNA or oligonucleotides arrays (e.g. from
Agilent, MWG, and ClonTech) used in gene expression studies by NCTR
scientists or their collaborators. If in your new experiment you are
using an array type shown on the pre-defined list, you can simply select
it as the array type for your current experiment. Otherwise, you'll need
to define your new array type. Fortunately, we have made it relatively
easy for the user to define a new array type.

The ArrayType Information for a particular array can be conveniently
viewed from the Chip Library (see Figure 3-89 of Chapter 3).

## Activate ArrayType Information

By clicking on ![](media/image48.png){width="1.0555555555555556in"
height="0.21736111111111112in"}, the user can create new array type
(Figure 2-21).

![](media/image49.png){width="2.7604166666666665in" height="1.21875in"}

Figure 2‑21: Array Type Information - Create new array type.

[]{#__RefHeading___Toc112231282 .anchor}**Export ArrayType
Information**: To export an array type, the user can open the Chip
Library, highlight the array type record then click "Export" button. See
Chapter 3 Chip Library for detail.

[]{#__RefHeading___Toc112231283 .anchor}**Create New ArrayType**: To
create a new array type, the user should at first define an ArrayType
Information File, which specifies the essential data fields needed for
defining the array elements (adherence with the MIAME guideline is
recommended). The process is best explained by using examples, as
presented below.

Figure 2-22 shows an example of an information file (NCTR_MWG_Mouse
20K.txt) for defining the NCTR_MWG_Mouse 20K array that consists of 48
blocks, and each block consists of 21 rows and 20 columns, resulting in
a total of 20,160 spots. Each spot (gene) has its corresponding block#,
row#, column#, GenBank accession #, description, etc. This ArrayType
Information File can be easily generated from the information about your
probes and the way they are printed on the microarray slides.

When the user click New Array Type button
![](media/image48.png){width="1.0555555555555556in"
height="0.21736111111111112in"} (Figure 2-21), the dialog shown in
Figure 2-23 pops up and allows the user to specify the file name for the
ArrayType Information File, the way spot location (coordinates) is
defined in the file, and the type of array elements (oligo or cDNA).
ArrayTrack accepts several types of coordinate definition systems
including MIAME's MetaCol/MetaRow/Col/Row notation, Block/Col/Row,
Row/Col, and Without coordinates. If no coordinate information is
available, the user must specify the number of columns and rows for the
array; while this is arbitrary, a square or nearly square arrangement
will result in the best graphical displays. In each case, ArrayTrack
converts the coordinate information into the MIAME-compliant format:
MetaCol/MetaRow/Col/Row. For some cases (e.g. Row/Col and Without
coordinates), the MetaCol/MetaRow ratio is automatically set to 1/1,
i.e. one MetaCol and one MetaRow.

![](media/image50.png){width="2.623611111111111in"
height="1.7159722222222222in"}

Figure 2‑22: ArrayType Information File.

![](media/image51.png){width="2.529166666666667in"
height="1.676388888888889in"}

> Figure 2‑23: Loading ArrayType Information File.

After clicking on Open, the Choose Columns form (Figure 2-24) pops up,
which allows the user to enter essential information (e.g. name,
platform, manufacturer, layout of array elements, etc.) about the new
array type and map data columns in the ArrayType Information File to the
corresponding MicroarrayDB data fields by selecting a specific data
column header and clicking on the corresponding MicroarrayDB data field
to be mapped. Data columns already mapped will be shown in yellow and
their corresponding MicroarrayDB data fields will be marked with a
![](media/image27.png){width="0.15833333333333333in"
height="0.14166666666666666in"}. Clicking on
![](media/image52.png){width="0.11666666666666667in" height="0.125in"}
will undo a previous column assignment. Clicking on OK at the bottom of
Figure 2-24 commits data in the ArrayType Information File to the new
ArrayType, which becomes available for selection in the ArrayType field
of the Input Form (Figure 2-6).

Figure 2-24 gives an example of entering array information in the
MetaCol/MetaRow/Col/Row format. In the example there are four Meta
columns and three Meta rows, numbered left to right, and top to bottom,
respectively. Each Meta column has seven columns and each Meta row has
eight rows. Figure 2-24 illustrates assigning data in the ArrayType
Information File to MetaCol, MetaRow, Col and Row data fields in
ArrayTrack.

![](media/image53.png){width="5.152777777777778in"
height="5.002777777777778in"}

Figure 2‑24: Mapping columns in ArrayType Information File to
MicroarrayDB data fields and MetaCol/MetaRow/column/row example.

Figure 2-25 gives an example of entering array information in the
Block/Column/Row format. In the example, each block has 7 columns and 8
rows and the blocks are arranged in 4 columns by 3 rows and numbered
sequentially first left to right then top to bottom. Figure 2-25
illustrates assigning data in the ArrayType Information File to Block,
Column, and Row data fields in ArrayTrack.

![](media/image54.png){width="4.5993055555555555in" height="4.625in"}

Figure 2‑25: Example of Block/Col/Row

*Tip*: ArrayType information can be conveniently viewed from the Chip
Library (see discussion on Chip Library in Chapter 3).

## Data Sharing and Security Protection

The owner of the experiment can assign Read/Write privileges to
individuals by clicking on
![](media/image55.png){width="0.7361111111111112in"
height="0.18472222222222223in"} of Input Form (Figure 2-6) to share the
experiment data with others (Figure 2-26), e.g. to allow a
bioinformatician to access and analyze the data. Data from the
experiment is invisible in ArrayTrack to users not granted Read/Write
privileges. This security feature allows the owner of an experiment the
full control in data sharing while not compromising data security.
(Note: ArrayTrack queries the Windows operating system to confirm user
login identification)

![](media/image56.png){width="3.2909722222222224in"
height="1.2166666666666666in"}

Figure 2‑26: Assigning Read/Write privileges to individuals.

To assign privilege to users who are not in the list, the owner can
create new user first by typing the user's name (the name that the user
uses to login his computer) and then click "add user". The new user name
will show in the list, and the owner can assign Read and Write privilege
to the new user. See Figure 2-26.

## Exploring and Viewing Data in MicroarrayDB from Tree View

[]{#__RefHeading___Toc112231286 .anchor}**Structure of Database
Contents**: The ArrayTrack database is structured similar to Windows
explorer and thus is arranged hierarchically as shown in Figure 2-27.
When ArrayTrack is closed, the tree view is saved for the particular
user and will be recovered the next time that user restarts ArrayTrack.

The tree structure hierarchy is always arranged in the following nested
order: owner, experiment, hybridization, raw data and normalized data.
Thus the tree structure appears as follows, with the indicated icons
denoting to different types of data:

MicroarrayDB (![](media/image57.png){width="1.0972222222222223in"
height="0.18125in"})

Owner (![](media/image58.png){width="0.13541666666666666in"
height="0.1875in"})

> Experiment (![](media/image59.png){width="0.1875in"
> height="0.15625in"})
>
> Hybridization (![](media/image60.png){width="0.17708333333333334in"
> height="0.16666666666666666in"}: two-color array;
> ![](media/image61.png){width="0.19791666666666666in"
> height="0.19791666666666666in"}: filter array;
> ![](media/image62.png){width="0.19791666666666666in"
> height="0.17708333333333334in"}: GeneChip^®^ array)
>
> Significant Gene List
> ![](media/image63.png){width="0.13541666666666666in"
> height="0.13541666666666666in"}
>
> Raw gene expression array data
> (![](media/image64.png){width="0.14583333333333334in"
> height="0.125in"})
>
> Normalized gene expression array data
> (![](media/image65.png){width="0.16666666666666666in"
> height="0.17708333333333334in"}, if available)

![](media/image66.png){width="2.4923611111111112in"
height="2.627083333333333in"}

Figure 2‑27: Hierarchical Structure of the MicroarrayDB Contents.

[]{#__RefHeading___Toc112231287 .anchor}**Exploring Database Contents**:
The tree-like Database Contents structure allows the user to select
and/or open a particular user
(![](media/image58.png){width="0.13541666666666666in"
height="0.1875in"}), experiment (![](media/image59.png){width="0.1875in"
height="0.15625in"}), or hybridization
(![](media/image60.png){width="0.17708333333333334in"
height="0.16666666666666666in"},![](media/image61.png){width="0.19791666666666666in"
height="0.19791666666666666in"}, or
![](media/image62.png){width="0.19791666666666666in"
height="0.17708333333333334in"}) by toggling on the lock
(![](media/image67.png){width="0.15625in"
height="0.11458333333333333in"}) unlock
(![](media/image68.png){width="9.375e-2in" height="0.15625in"}) signs.
The content structure can also be manipulated by right-click on a
particular item (Figure 2-28). For example, by right-clicking on a
hybridization, the user can either Expand completely or Collapse
completely the content (sub) tree beneath it. A right mouse click makes
available various operations that depend on the level of the tree
structure, as illustrated in Figure 2-28. Right clicking any level
(i.e., experiment, hybridization or dataset) gives a list of options,
the last of which is Tree options that when clicked gives the user five
ways for displaying (or hiding) more detailed information:

1)  Show samples on hybridizations appends sample names to the
    hybridization name that are colored green or red corresponding to
    Cy3 and Cy5, respectively;

2)  Show hybname on dataset appends the hybridization name to the
    dataset names.

3)  Show samples on datasets appends sample names to the dataset names
    that are colored green or red corresponding to Cy3 and Cy5,
    respectively;

4)  Show original data filename on raw datasets appends the name of the
    original raw data file to the beginning of the raw data; and,

5)  Show label names appends sample label names (e.g., Cy3 and Cy5) to
    the all levels of the tree that are colored green or red
    corresponding to Cy3 and Cy5, respectively.

6)  For 1, 2 and 3 above, green and red applies only to two-channel
    systems using Cy3 and Cy5 labels. For single channel systems, the
    sample name will be colored blue.

![](media/image69.png){width="3.7756944444444445in"
height="1.3493055555555555in"}
![](media/image70.png){width="1.7909722222222222in"
height="1.4979166666666666in"}

![](media/image71.png){width="3.2527777777777778in"
height="2.2604166666666665in"}

Figure 2‑28: Database Contents tree can be expanded or collapsed
completely by right-clicking on an item. Depending on the nature of the
object that is right-clicked on, a set of applicable functions become
accessible. (A) Experiment; (B) Hybridization; and (C) Array Dataset.

[]{#__RefHeading___Toc112231288 .anchor}**Operating on Experiment**:
Double-clicking on an Experiment will bring up the Input Form (Figure
2-6). Upon right-click on an Experiment, in addition to Expand
completely and Collapse completely the tree, the user can choose several
other options (Figure 2-28A).

Select raw datasets highlights (i.e., selects) all the raw datasets
underneath this Experiment making them collectively available for a
subsequent operation (e.g. Normalization).

Select normalized datasets highlights (i.e., selects) all the normalized
datasets underneath this Experiment making them collectively available
for a subsequent operation (e.g. Data Export).

Show hybridizations spreadsheet collects the information about the all
hybridizations contained in the experiment together with the attached
toxicological information including all hybridization, sample, treatment
and dosing information.

Show possible flip-dye hybridization pairs automatically detects all
possible flip-dye hybridization pairs based on the user entered sample
and labeling information for all the hybridizations within the
Experiment. The term "possible" is used to denote that, for example, a
specific sample can be associated with multiple hybridizations that
could also be dye-flip paired depending on user input. The flip-dye
paring information is listed in a spreadsheet view (Figure 2-29 left).
Hybridizations without matched flip-dye pairing will be displayed in a
separate spreadsheet (Figure 2-29 right). This function is based on the
sample and hybridization information entered in the Input Form (Figure
2-6). Thus, it is important to make sure that information in Input Form
is entered correctly. Note that this function only works for an
experiment utilizing two dye labels (a so-called two color system).

![](media/image72.png){width="2.782638888888889in"
height="1.6131944444444444in"}
![](media/image73.png){width="2.4159722222222224in"
height="1.1805555555555556in"}

Figure 2‑29: Automatic detection and display of Flip-Dye Pairs
information for an Experiment. Left: List of matched Flip-Dye pairs;
Right: List of Unmatched hybridizations.

[]{#__RefHeading___Toc112231289 .anchor}**Operating on Hybridization**:
Double-click on a Hybridization will bring up the Input Form (Figure
2-6) with information about the Experiment and the Hybridization shown.

Right-click on a Hybridization allows the user to Rename hybridization
and Import data in addition to Expand completely and Collapse completely
(Figure 2-28B). As is mentioned in Data Import part of this Chapter,
this is another way of loading gene expression array data into
MicroarrayDB.

[]{#__RefHeading___Toc112231290 .anchor}**Operating on Array Data**:
Double-click on an Array Dataset will bring up the Spreadsheet view for
this array dataset. Right-click on selected Array Datasets pops up a
long list of functions applicable (Figure 2-28C). Instructions for use
of these functions are discussed in Chapters 4 through 8.

## Significant Gene List 

Significant Gene List allows the user to Import, Export, Delete and
Display a list of significant genes associated with this Experiment
(double-click any experiment will bring up the Significant Gene List
showing in a spreadsheet, see Figure 2-30)

![](media/image74.png){width="2.263888888888889in"
height="1.0326388888888889in"}

Figure 2‑30: Double-click the significant gene lists for display.

![](media/image75.png){width="3.357638888888889in"
height="3.0347222222222223in"}

Figure 2‑31: Display of Significant Gene List information associated
with an Experiment

![](media/image76.png){width="3.122916666666667in"
height="1.7236111111111112in"} ![](media/image77.png){width="2.125in"
height="1.2666666666666666in"}

![](media/image78.png){width="3.027083333333333in"
height="1.2055555555555555in"}

Figure 2‑32: select Significant Gene List to display

[]{#__RefHeading___Toc112231292 .anchor}**Display Significant Gene
List**

Double-clicking the name of a Significant Gene List
![](media/image79.png){width="0.14583333333333334in"
height="0.13541666666666666in"} will bring out the spreadsheet
displaying the significant gene list. Another way to bring out
significant gene list is right-clicking the experiment-\> choosing
Significant Gene List-\> choosing Display, then a pop-window will show
up and list all the significant gene lists under the experiment. The
user can choose the significant gene list to display, see Figure 2-32.
If the user selects all the significant gene lists to display, then the
multiple lists will be shown in one spreadsheet.

The user can open multiple significant gene lists individually and
combine the lists to get common gene lists. For example, if the user
wants to combine list A and list B. He can open the two lists first
separately and then highlight the record in list B, drag to list A, or
vice versa, choose the ID's from the two lists for mapping. If the two
gene lists are from the same array type, you can select SpotID for
matching; if the gene lists are from different array type, then the user
can select LocusID, or GeneName or other ID depending on the column
contents in the gene lists. See Figure 2-33 and Figure 2-34.

![](media/image80.png){width="5.124305555555556in"
height="2.323611111111111in"}

Figure 2‑33: combine two lists to get common gene list

![](media/image81.png){width="3.08125in" height="1.9819444444444445in"}

Figure 2‑34: choose the column SPOTID for mapping two lists

From the display table, the user can get additional information about
the list of genes by accessing other functions including Gene Library,
Protein Library, and Pathway Library that appeared on top of the table.
The user can also add annotations to the gene lists by clicking
![](media/image82.png){width="0.7083333333333334in"
height="0.15486111111111112in"} and then typing in the explanation. Bar
Chart (Chapter 6) can be accessed by right-licking on selected gene
records.

![](media/image83.png){width="5.626388888888889in" height="1.76875in"}

Figure 2‑35: The combined gene list result

Figure 2-35 shows the common genes from the two significant gene lists.
The number of the filter icons at the left bottom indicates the number
of the significant gene lists involved combining. From the combined
result the user can access other libraries by clicking the buttons at
the top row. Please be aware that if the user click Ingenuity button,
the fold change results in the significant gene list will be
automatically be converted to log~2~Fold or original Fold (when 0\< fold
\<1, it will be -- 1/Fold) in Ingenuity analysis results.

![](media/image84.png){width="2.7909722222222224in"
height="1.4270833333333333in"}

Figure 2‑36: Fold converting options

The user can continually combine the already combined lists. The lists
can be in the same experiment or across different experiments, as long
as the array types are the same. This function is very helpful for
finding the common genes across different experiment.

[]{#__RefHeading___Toc112231293 .anchor}**Import Significant Gene
List**:

The text file for Import can be loaded (Figure 2-37) and the data
columns in the text file can be mapped to ArrayTrack database fields.
The user may have already noticed that the process of importing
Significant Gene List is quite similar to that of importing new array
type (Figure 2-23).

![](media/image85.png){width="2.428472222222222in"
height="1.5902777777777777in"}

Figure 2‑37: Loading a text file with information about Significant Gene
List associated with an Experiment.

![](media/image86.png){width="3.0708333333333333in"
height="2.0083333333333333in"}

Figure 2‑38: Mapping Significant Gene List information to ArrayTrack
database fields.

[]{#__RefHeading___Toc112231294 .anchor}**Export Significant Gene
List**:

Click the significant gene list-\>export sig. gene list-\> save to local
drive.

[]{#__RefHeading___Toc112231295 .anchor}**Delete Significant Gene
List**:

Click the significant gene list-\>delete.

[]{#__RefHeading___Toc112231296 .anchor}**To create significant gene
list**:

From T-test/ANOVA result the user can set the filter criteria to get the
significant gene list. See Chapter 6.

## VennDiagram

The Venn diagram is used to describe the relationships of multiple data
set (C~1~, C~2~, C~3~......Cn) which may have some (but not all)
elements in common. ArrayTrack can draw the VennDiagram with n\<=3.
There are 3 ways to activate VennDigram: a) select 2 or 3 significant
gene lists and right-click, choose VennDigram then select the ID for the
mapping, b) from the Tool panel, click VennDigram icon, c) from the Tool
pull-down menu. See Figure 2-39.

![](media/image87.png){width="3.373611111111111in"
height="1.7055555555555555in"}
![](media/image88.png){width="1.6041666666666667in"
height="1.6145833333333333in"}

Figure 2‑39: get common genes from 2 significant gene lists

![](media/image89.png){width="4.088888888888889in"
height="4.124305555555556in"}

Figure 2‑40: The result of the Venn diagram for the common genes

#### a) If you activate VennDiagram in the first way you will see the Venn diagram (Figure 2-40, an example of the common genes from the 3 gene lists). The red text in the diagram represents the name of the gene list, and blue number represents the number of the unique IDs in the significant gene lists. The blue number in the overlapped part of the circles represents the number of the common genes. The user can label the circles with different colors by right-clicking anywhere in the window and select Color Chooser. Right-clicking -\> choosing Highlighted Data View -\> selecting Original data will bring up the data tables for all the colored parts and selecting ID only will bring up the table of common ID only. See Figure 2-41.

![](media/image90.png){width="4.252777777777778in"
height="2.379166666666667in"}
![](media/image91.png){width="1.0416666666666667in"
height="2.3743055555555554in"}

Figure 2‑41: The original data view of the common gene list and original
data with ID only

b\) If you activate VennDiagram
(![](media/image92.png){width="1.0520833333333333in"
height="0.19791666666666666in"} ) from the Tool panel, a pop-up window
will let you open your gene lists (maximum 3 gene lists) from the local
drive. See Figure 2-42. Click open button to load the gene lists and
select the common ID column for the three gene lists, then click Draw
Venn button. The result of the VennDiagram will show, see Figure 2-43.

![](media/image93.png){width="3.928472222222222in"
height="3.941666666666667in"}

Figure 2‑42: open gene lists from local drive

![](media/image94.png){width="3.122916666666667in"
height="3.4006944444444445in"}

Figure 2‑43: VennDiagram for three gene lists

In Figure 2-43, right-clicking any highlighted area in the VennDiagram
and choosing HighlightedData View-\>Original data will bring up the
tables (Figure 2-44) under "DataInput" tab showing the three original
lists with some highlighted rows corresponding to the highlighted area
in the VennDiagram. The user can export those highlighted rows by
right-clicking the rows -\>choosing Export-\> selected rows. The
unselected rows also can be exported in the same way. If the user wants
to clear all the highlights, s/he can choose "Clear Selection".

![](media/image95.png){width="4.442361111111111in"
height="4.817361111111111in"}

Figure 2‑44: Original data view with highlighted rows corresponding to
the highlighted area in VennDiagram

c\) Activate VennDiagram from the Tool menu.

![](media/image96.png){width="4.416666666666667in"
height="1.7305555555555556in"}

Figure 2‑45: Activate Venn Diagram from Tool pull-down menu

# Working with Libraries

The libraries within ArrayTrack provide a powerful capability to augment
the analysis of microarray data, and assist in the interpretation of
experimental results. Even if the user is not analyzing microarray data
*per se*, the libraries provide significant utility for investigating
biological data since the salient information and data from public
databases on sequence, genes and their function, proteins and their
function, conserved orthologous genes and pathways are aggregated and
interlinked.

The contents of the online version Libraries in ArrayTrack are updated
weekly. If you have a local installation of ArrayTrack in your own
server, the Libraries are NOT automatically updated. Instructions for
updating your locally installed Libraries are available from our website
(<http://edkb.fda.gov/webstart/arraytrack/>).

The Library panel (Figure 3-46) provides access to the Gene Library,
Pathway Library, Protein Library, IPI Library, Orthologene Library,
GOFFA Library, Chip Library, Toxicant Library, EDKB Library and ID
Converter tool. All libraries can be searched (or filtered) in a number
of ways to be explained below. Moreover, results of a query on a
particular library can generally be used as the basis for a subsequent
search against another library, and so on, allowing the user to
drill-down to more detailed and related biological information. For
example, the gene library can be searched for a list of genes, the
result of which can be used to search for associated protein, pathway
and cross-species orthologous gene information in one or more of the
other libraries.

The ID Converter tool is used for converting one type of ID to another,
e.g. converting Locus ID to Unigene ID or other ID. This tool will be
very helpful for library searching.

![](media/image97.png){width="1.4791666666666667in"
height="2.2083333333333335in"} ![](media/image98.png){width="2.4375in"
height="2.3020833333333335in"}

Figure 3‑46: Libraries can be accessed from the Library panel or the
Library pull-down menu.

ArrayTrack's libraries integrate gene, protein, pathway and other data
allowing data interrogation and mining of data across data types. The
Gene Library, Pathway Library, Protein Library, IPI Library, Orthologene
Library, GOFFA Library and Chip Library are interconnected on the basis
of gene, protein or species, whereas the Toxicant Library and EDKB
Library are interconnected with genes or pathways based on a chemical's
CAS number.

For ease of use, all libraries except the GOFFA present the same user
interface, which is split into two panels: 1) the left panel is the
search form; and 2) the right panel is for displaying search results
(Figure 3-47). Additionally, the user interfaces for these libraries
have been to the extent practicable, designed to present identical or
similar features and means for executing functions and operations, that
is, to have a similar "look and feel". Thus, as the libraries are
individually discussed below, redundancy will be minimized where
possible. Therefore, once the user understands the use of the Gene
Library, this understanding will mostly apply to the other libraries.

![](media/image99.png){width="5.99375in" height="4.343055555555556in"}

Figure 3‑47: Gene Search panel displays the number of records in Gene
Library.

## Gene Library

Gene Library has human, mouse, and rat data. The user can double-click
on Gene Library to bring up the panel. The detailed functionality of
Gene Library is summarized as follows:

### Working with the Table 

The main part of the Gene Library is an Excel-like spreadsheet table. As
shown in Figure 3-47, 109,290 records are present in the table (on
5-20-2005). All the gene records in ArrayTrack are searched and the
total number of records in Gene Library is displayed; however, only the
first few thousands (6,000) are retrieved and displayed in the result
table. To get a more complete list of the genes, the user needs to click
on the (![](media/image100.png){width="0.125in"
height="8.333333333333333e-2in"}) button shown at the left-bottom of the
result table (Figure 3-47).

[]{#__RefHeading___Toc112231301 .anchor}**Filter Genes:** A very useful
feature about the table is that the user can use a combination of
filters to display gene records of interests. The filters can be entered
on the line under the individual column names and at the right of the
![](media/image101.png){width="0.48333333333333334in"
height="0.10833333333333334in"} sign. After the filters have been
entered, clicking the search icon
![](media/image102.png){width="0.5083333333333333in" height="0.2in"} (or
pressing the Enter key) will display all the records meeting the filter
criteria. In the example shown in Figure 3-48, the user entered a
histone deacetylase filter under gene DESCRIPTION and a homo filter
under SPECIES. As a result, 12 records met these search criteria: 12
human (*Homo sapien*) genes are (related to) histone deacetylases
(HDACs). It is clear from the PATHWAY and ONTOLOGY columns that the
biological functions of HDACs are mainly cell cycle-related.

It is important to note that by default all the filters are operating in
a logic "AND" manner, *i.e*., only those records that meet \*all\* the
filter criteria will be displayed.

More sophisticated queries can be entered. For example, the user can add
additional Filters by clicking on
![](media/image103.png){width="0.19166666666666668in"
height="0.11666666666666667in"} and the entered search criteria can be
combined in complicated logic operations as illustrated by example in
Figure 3-48. Newly inserted filtering rows can be deleted by selecting
it, right-clicking on them and then choosing "remove this filter row".

![](media/image104.png){width="4.501388888888889in"
height="1.6958333333333333in"}
![](media/image105.png){width="1.2090277777777778in"
height="0.9944444444444445in"}

Figure 3‑48: Use of a combination of filters to find out interesting
genes.

[]{#__RefHeading___Toc112231302 .anchor}**Sort Table by Column:** The
table can be sorted
(![](media/image106.png){width="0.11666666666666667in"
height="6.666666666666667e-2in"} or
![](media/image107.png){width="0.11666666666666667in"
height="5.8333333333333334e-2in"}) by toggling on the column header and
then pressing ![](media/image102.png){width="0.5083333333333333in"
height="0.2in"}. Note that the sorting is performed in a way that is
consistent with the inherent definition of the data type of the column
field. For example, GENENAME is searched by ASCII order (Figure 3-49A),
whereas LOCUSID is searched by numerical order (Figure 3-49B). An
additional sorting column can be added to a previously sorted table.

![](media/image108.png){width="1.3631944444444444in"
height="1.8333333333333333in"}
![](media/image109.png){width="3.1631944444444446in"
height="1.8041666666666667in"}

Figure 3‑49: Genes can be sorted by columns. (A) Sort by GeneName; (B)
Sort by LocusID.

[]{#__RefHeading___Toc112231303 .anchor}**Rearrange Table Columns**: The
order in which the columns are arranged can be changed by dragging and
moving a particular column header to its desired position.

### Searching Functional Information for a List of Genes 

There are three simple steps to find functional information for a list
of genes using the functions in Box  as shown in Figure 3-47:

1.  Select a gene IDs (e.g. GenBank accession number, UniGene ID,
    LocusID, Swiss-Prot accession number, manufacturer's gene ID, and
    gene symbol) that matches the ID of the gene list.

2.  Cut/paste a list of gene IDs of the gene list from e.g. an Excel
    spreadsheet into the Enter Searching Data box. The gene IDs can be
    separated by space " ", \<tab\>, ":", ";", or \<Enter\>. Checkboxes
    for Hs, Mm, and Rn are provided for specifying a gene search query
    (when the ID type is Gene symbol). A
    ![](media/image110.png){width="0.2833333333333333in"
    height="0.11666666666666667in"} button is provided for clearing all
    the entries in the Enter Searching Data box.

3.  Click ![](media/image111.png){width="0.35833333333333334in"
    height="0.1in"} to find functional information for the gene list in
    the Enter Searching Data box, which are displayed in a
    spreadsheet-like table on the right side (Box ). Further search can
    be conducted within the domain of the results from a previous search
    by checking the within result option next to the
    ![](media/image111.png){width="0.35833333333333334in"
    height="0.1in"} button.

Figure 3-50 displays the results of searching against GeneSymbol for a
group of 12 histone deacetylase (HDAC) related genes. The first column
is the input gene name that the user types/pastes in the searching
section. The rest columns are the matching results for the genes.

![](media/image112.png){width="4.749305555555556in"
height="3.3097222222222222in"}

Figure 3‑50: Gene Search results by searching against GeneSymbol and
Homo sapiens.

[]{#__RefHeading___Toc112231305 .anchor}**More Information about a
Gene** : On the top of the result table there are several comboboxes:
"More info" allows the user to view detailed information for a selected
gene record (Figure 3-51), including Synonym, NCBI RefSeq, GenBank
Sequences, Gene Ontology, References, Chromosomal Map, Pathway, and
Summary. Select the item that you wish to view and click on OK.

![](media/image113.png){width="1.6034722222222222in"
height="1.5451388888888888in"}

Figure 3‑51: More information can be displayed for a selected gene
record.

[]{#__RefHeading___Toc112231306 .anchor}**Link to Other Public
Databases** : "Link to" allows the user to open the official web pages
(UniGene, EntrezGene, LocusLink, OMIM, GeneCard, Swiss-Prot, GDB, KEGG,
IPI, GeneBank, UniSTS and Homologene) for one selected record (Figure
3-52).

![](media/image114.png){width="5.190972222222222in"
height="2.078472222222222in"}

Figure 3‑52: Official web sites are linked for a selected gene record.

[]{#__RefHeading___Toc112231307 .anchor}**Sort Genes Based on Their
Common Pathway and Proteins** ( and ): The user can highlight any
number of genes in the table and click
![](media/image115.png){width="0.6729166666666667in"
height="0.21041666666666667in"} or
![](media/image116.png){width="0.7493055555555556in"
height="0.20902777777777778in"} to reorder the highlighted genes based
on their shared common pathways. Details are discussed under Pathway
Library.

![](media/image117.png){width="0.675in" height="0.15in"} allows the user
to explore the protein information for the selected genes. Details are
discussed under Protein Library.

[]{#__RefHeading___Toc112231308 .anchor}**Customize Table**: The table
that displays search results can be customized by clicking on Customize
Table button in Figure 3-47. The user can select particular fields to be
displayed in the table (see Figure 3-53).

![](media/image118.png){width="3.0527777777777776in" height="1.175in"}

Figure 3‑53: Table items that can be chosen for display in the Gene
Search results table.

[]{#__RefHeading___Toc112231309 .anchor}**Export Table Contents to a
Local File** : The contents of a search result table can be exported to
a text or Excel file on a local disk by clicking on the Export Table.
The dialog box shown in Figure 3-54 allows the user to customize the
contents and format of the data to be exported.

*Tip*: The user can use Copy/Paste to transfer table contents to other
applications.

![](media/image119.png){width="2.7333333333333334in"
height="1.3409722222222222in"}

Figure 3‑54: Export of search results into a local text or Excel file.

[]{#__RefHeading___Toc112231310 .anchor}**Copy/Paste selected
rows/columns:** The user can select some of the records, right-click the
highlighted rows, choose "Copy selected rows to clipboard" and then
pasted at the other place. If the user only want to copy a specific
column of the selected rows, he can highlight the rows first, move the
mouse to the column to be copied, then right-click and choose "Copey
selected columns on selected rows to clipboard". See Figure 3-55.

[]{#__RefHeading___Toc112231311 .anchor}**Stop a Search Session**: When
a query is being searched, the
![](media/image102.png){width="0.5083333333333333in" height="0.2in"}
button becomes grayed out and the arrows keep spinning
(![](media/image120.png){width="0.5333333333333333in"
height="0.18333333333333332in"}). The user can terminate the current
search session by clicking on
![](media/image121.png){width="0.4083333333333333in"
height="0.16666666666666666in"}.

[]{#__RefHeading___Toc112231312 .anchor}**Launch Bar Chart for Selected
Genes**: Bar Chart across multiple arrays (also see Chapter 6 for more
information on the use of the Bar Chart) can be launched by right-click
on (a maximum of five) selected genes (Figure 3-55).

![](media/image122.png){width="5.99375in" height="1.2993055555555555in"}

Figure 3‑55: Launch Bar Chart for selected gene records.

User can also click Help to get information about the libraries (Figure
3-56).

![](media/image123.png){width="2.982638888888889in"
height="2.176388888888889in"}

Figure 3‑56: Information on the libraries under Help menu

## 

## Pathway Library

The Pathway Search panel can be activated by clicking on
![](media/image124.png){width="1.1458333333333333in" height="0.1875in"}
in the Library panel or Library pull-down menu (Figure 3-46).

Figure 3-57 shows that pathway information is available in ArrayTrack
for 31,257genes. This library contains pathway information from KEGG and
ParthArt.

The user may have already noticed that the user interface for the
Pathway Search panel is the same as that of the Gene Search panel
(Figure 3-47). As with the Gene Library, clicking the **Customize
Table** button will display a panel for the user to customize the
information that will be displayed (Figure 3-58).

![](media/image125.png){width="5.9944444444444445in"
height="3.834722222222222in"}

Figure 3‑57: Pathway Library window

![](media/image126.png){width="5.998611111111111in"
height="2.7083333333333335in"}

Figure 3‑58: Select fields for display

Two types of search can be done in the Pathway Library: 1) search based
on gene information; and 2) search based on chemical compound
information. The user chooses the type of search by clicking either the
Gene Info tab, or the Compound Info tab that are located in the upper
left of the panel (Figure 3-57).

![](media/image127.png){width="5.124305555555556in"
height="3.691666666666667in"}

Figure 3‑59: Pathway Search panel for gene information.

When searching by Gene Info, the user specifies the type of ID, types or
pastes the ID in the window, and then clicks the search button. All the
pathways related to the genes are displayed. Also note that a button is
provided to clear the contents in the search data window. If searching
by GeneSymbol (i.e., gene name), then the user can check the boxes to
include Human (Hs), Mouse (Mm) and Rat (Rn) in the search.

When searching by Compound Info, the user can specify compound name ("="
option) or part of a name ("contains" option), and/or the number of
carbon, hydrogen or oxygen atoms in the compound's chemical formulae,
and/or the compounds CAS Number. CAS numbers can be typed or pasted in
the window provided, and can be cleared by clicking the clear button. An
example of searching by chemical formula is shown in Figure 3-60.

Note that, for searching for either Gene Info or Compound Info- based
search, a subsequent search can be performed that is limited to the
genes or compounds associated with the previous search by checking the
"within result box".

As for the Gene Library, the user can rearrange the table columns by
dragging and moving the column header.

![](media/image128.png){width="5.3694444444444445in" height="3.86875in"}

Figure 3‑60: Pathway searching panel for compound information

The user can select a group of interesting or relevant genes from the
display table and request pathway information from ArrayTrack. After the
selecting the genes, the user clicks on either the
![](media/image115.png){width="0.5555555555555556in"
height="0.17222222222222222in"} or
![](media/image116.png){width="0.6111111111111112in"
height="0.17083333333333334in"} button; since Kegg and ParthArt are
different libraries, probably different search results can be expected.
There is another button
![](media/image129.png){width="0.7354166666666667in"
height="0.18333333333333332in"} for pathway that will be addressed in
detail later. In Figure 3-61, highlight all the filtered genes after
typing "citrate cycle" in the first row, then click
![](media/image115.png){width="0.47152777777777777in"
height="0.14722222222222223in"}.

![](media/image130.png){width="5.376388888888889in"
height="2.040277777777778in"}

Figure 3‑61: type "citrate cycle" in the first row for filtering, then
highlight all the filtered results.

After clicking on the
![](media/image115.png){width="0.5555555555555556in"
height="0.17291666666666666in"} button, the user is prompted to select
which species is to be searched (Figure 3-62). Multiple species can be
specified for one search.

![](media/image131.png){width="2.2083333333333335in"
height="1.1611111111111112in"}

Figure 3‑62: Select species for Pathway Search.

All pathway information related to these genes is displayed in a
separated table, called Pathway table, as shown in Figure 3-63. At the
bottom of the pathway table, a summary on pathway information is
provided: *Total submitted genes: 33, 18 genes found, 15 not found,
Total 13 pathway maps*, indicating that the user submitted 33 genes to
ArrayTrack for pathway information and pathway information is available
for 18 genes, which are involved in 13 different pathways. The Pathway
table is organized by gene name, Locus ID, pathway map, and the category
of the pathway.

![](media/image132.png){width="5.293055555555555in"
height="3.251388888888889in"}
![](media/image133.png){width="5.624305555555556in"
height="1.9159722222222222in"}

Figure 3‑63: Search results from Kegg (A) and PathArt (B).

When you move the mouse cursor over the name of a pathway map or its
category, that pathway will be highlighted. In Figure 3-63A, The
"Citrate cycle (TCA cycle)(mmu00020)" pathway is highlighted. By
clicking on the pathway that is highlighted, you will be directed to the
KEGG/Pathway HTML page (Figure 3-64).

![](media/image134.png){width="5.440277777777778in"
height="4.622916666666667in"}

Figure 3‑64: A list of the pathways in which the eleven genes are
involved is displayed in the format of KEGG/Pathway database search
result.

In Figure 3-63B, the user can highlight and then double-click one
record, then the Jubilant Biosys's PathArt will show up, see Figure
3-65.

![](media/image135.png){width="3.5555555555555554in" height="2.29375in"}

Figure 3‑65: Jubilant Biosys's PathArt

Click the pathway name under the "Result" tab in the left panel, the
pathway will show up in the right panel. See Figure 3-66.

![](media/image136.png){width="5.998611111111111in"
height="2.4444444444444446in"}

Figure 3‑66: Pathway from PathArt

Chemical structures can also be displayed by double-clicking on the
compound name.

The Pathway table can also be saved in a local file by clicking on
![](media/image137.png){width="0.15in" height="0.15833333333333333in"},
see Figure 3-63.

![](media/image138.png){width="3.0416666666666665in"
height="4.389583333333333in"}

Figure 3‑67: KEGG pathway map (hsa00120) on Bile acid biosynthesis.
Query compounds are highlighted in red cycles.

## Ingenuity Library

*Ingenuity* delivers systems biology expertise to biologists and
bioinformaticians through pathways analysis software, genome-scale
computable network databases, and knowledge management services and
infrastructure, resulting in increased R&D productivity and faster drugs
to market. This manual will not describe how to use Ingenuity software.
For more information about Ingenuity, please visit website:
<http://www.ingenuity.com/>. This website provides tutorial and manual
for using *Ingenuity* software.

In Figure 3-62, if the user highlight some record and click
![](media/image129.png){width="0.8854166666666666in" height="0.21875in"}
button, then the user will go into Ingenuity system to get pathway
analysis. The list of genes will be passed to the Ingenuity and directly
launch the software. First the user needs to type the name and password
to login to the software, see Figure 3-68.

![](media/image139.png){width="4.570833333333334in"
height="2.0034722222222223in"}

Figure 3‑68: login to Ingenuity Pathway software

Once login, the following window will show up. The user can type in the
name for the dataset file, then click "Next" button. See Figure 3-69.

![](media/image140.png){width="4.378472222222222in"
height="2.6013888888888888in"}

Figure 3‑69: Upload dataset file

![](media/image141.png){width="5.709027777777778in"
height="3.317361111111111in"}

![](media/image142.png){width="2.501388888888889in"
height="2.282638888888889in"}

Figure 3‑70: State the detail of the dataset before create analysis and
give a name for the analysis

In Figure 3-70A, the user needs to specify the identifier type
(ArrayTrack uses LocusLink as the identify type) and expression value
type (ArrayTrack will automatically convert the fold change to logFold
or Fold depending on the selection, so the user should choose Fold or
logFold) then click "Create Analysis" button. A new window show up
letting the user create a name for the analysis (see Figure 3-70B).
Click "next" button and click "Run Analysis" button.

![](media/image143.png){width="4.501388888888889in" height="2.3125in"}

Figure 3‑71: Run Analysis

![](media/image144.png){width="5.498611111111111in"
height="2.1395833333333334in"}

Figure 3‑72: Choose the dataset to see the results

![](media/image145.png){width="4.292361111111111in"
height="1.9791666666666667in"}

Figure 3‑73: The results of the analysis

## Protein Library

Similar to accessing the Gene Search and Pathway Search panels, the user
can click on Protein Library to bring up the Protein Search panel, which
consists of two parts: the left part is the search form, and the right
part displays the search results. Figure 3-74 shows that protein
information is available for 24,024 records in ArrayTrack.

Similarly, the user can search the Protein Library by pasting a list of
gene ID's. The result is represented in a spreadsheet-like table on the
right side. The gene ID's that can be searched against include GenBank
accession number, UniGene ID, LocusID, Swiss-Prot accession number,
manufacturer's gene ID, and gene symbol. Further search can be conducted
within the domain of the results from a previous search by checking the
within result option next to the
![](media/image111.png){width="0.35833333333333334in" height="0.1in"}
button.

The contents of the table can be customized by selecting data items
shown in the Figure 3-75.

![](media/image146.png){width="4.7347222222222225in" height="3.43125in"}

Figure 3‑74: Protein Search panel.

![](media/image147.png){width="3.984027777777778in"
height="1.6215277777777777in"}

Figure 3‑75: Items that can be chosen for display in the Protein Search
result table.

**Link to Other Public Databases:** Just the same as for the Gene
Library and Pathway Library, "Link to" provides the user the gateways to
other official web pages (Swiss-Prot, PDB, SWISS-2DPAGE, Pfam, PROTSITE,
InterProt, SMART, ProDom, UniGene, EntrezGene, LocusLink, OMIM,
GeneCard, GDB, Kegg, IPI, UniSTS, Homologene) for obtaining information
about selected protein (see Figure 3-74).

## IPI Library

IPI Library contains protein information from the International Protein
Index (<http://www.ebi.ac.uk/IPI/IPIhelp.html>). The IPI search panel
(Figure 3-76) is similar to that of Gene Search and Protein Search.

![](media/image148.png){width="4.997916666666667in"
height="3.4118055555555555in"}

Figure 3‑76: IPI Library search panel

## Orthologene Library {#orthologene-library .unnumbered}

The Orthologene Library contains genes linked across species that are
determined to be orthologous based on analysis of homology using Blast
(<http://www.ncbi.nlm.nih.gov/Education/BLASTinfo/information3.html>).
The library currently contains orthologs for human, mouse and rat.

![](media/image149.png){width="4.404166666666667in"
height="3.176388888888889in"}

Figure 3‑77: Orthologene search panel.

Orthologene Library has the same interface structure as the other
libraries, and provides links to other resources as for the other
libraries. However, the links to external websites is different, and
consists of website databases related to cross-species, chromosome
location across species, gene homology and species-specific genome
information (see Figure 3-78).

> ![](media/image150.png){width="1.8645833333333333in" height="1.25in"}
> ![](media/image151.png){width="2.2625in"
> height="1.6701388888888888in"}

Figure 3‑78: More links to other resources

## GOFFA Library {#goffa-library .unnumbered}

The GOFFA Library provides gene ontology information, using the standard
vocabulary (terminology) of the Gene Ontology Consortium; the ontology
provides standard vocabularies for the description of the molecular
function, biological process and cellular component of gene products.
These terms are to be used as attributes of gene products by
collaborating databases, facilitating uniform queries across them. The
controlled vocabularies of terms are structured to allow both
attribution and querying to be at different levels of granularity.

The user can access GOFFA library in the same way as the other
libraries.

*Tips:* a convenient way to access GOFFA is through Gene Library. In
Gene Library panel, the user can highlight the gene records with
interest. Then click GOFFA button at the top, select organism (human,
mouse, rat, etc), click OK button. The highlighted gene records will be
automatically shown in GOFFA, see Figure 3-79.

![](media/image152.png){width="3.002083333333333in"
height="1.520138888888889in"}
![](media/image153.png){width="2.7534722222222223in"
height="1.6590277777777778in"}

Figure 3‑79: Accessing GOFFA via Gene Library

The GOFFA Library displays three vertically parallel panels: 1) the left
panel for pasting the name of genes to be searched; 2) after clicking
"Search Go", the middle panel will show the associated gene terms
displayed in different views. The right panel will show the gene
products associated with the terms in the middle panel.

In the middle panel, there are four tabs categorized as: 1) Tree. 2)
Term Clustering. 3) All Genes. 4) GO Path Plot.

[Under Tree tab]{.underline}: the search results will be shown here in
three root groups: a) molecular function; b) biological process; c)
cellular component. Each group has more branched sub-groups. A
red-colored number is suffixed to the group name of every level tree.
That number represents the total number of genes in the search that was
found in this ontological category/sub-category. The suffixed green
number is Fisher exact test P-value. Following P value is the E
(Enrichment Factor, see equation 1.1) value. When you click on a GO term
the right panel will show the gene list related to the GO term. See
Figure 3-80.

![](media/image154.png){width="5.247222222222222in"
height="3.6868055555555554in"}

Figure 3‑80: GOFFA Library search panel

Enrichment factor =(ni/N)/(gi /G)  (1.1)

Where ni is the number of hit genes in term i. N is the number of input
genes. gi is the number of gene or protein associated with term i, G is
the total number of gene or protein in the database.

At the bottom of the middle panel, the user can enter a term in the
"Find terms containing" text box, choose search by "GO term", "Gene name
or gene symbol" or "P value", then click search button, the term
contained at all levels of the tree will be highlighted in blue color.
See Figure 3-80.

[Under Term Clustering tab]{.underline}: a spreadsheet with 6 columns
titled in No., Term, GO ID (GO accession number), Average Level (the
average hierarchical level of the term showing in all the paths),
Average Fisher P Value and Gene Hits (the number of the gene products
associated with the term). This is an alternative view of the tree
structure with P value pre-sorted. Users can sort the table by clicking
on the column header. To do multiple column sorting you can click on
column header while press ctrl key. Single-click any row will bring up
the associated genes shown in the right panel; double-clicking any row
will switch back to the tree tab view with interested terms highlighted
in blue.

![](media/image155.png){width="4.315972222222222in"
height="2.1020833333333333in"}

Figure 3‑81: Under Term Clustering tab in GOFFA library

[Under All Genes tab]{.underline}: This spreadsheet lists all the genes
and their associated info such as gene symbol, Average P-value and
average hierarchical level. Similarly the users can sort the table by
clicking the column header. Double-click on any row will bring up tree
view with highlighted gene number in blue on any associated term.

![](media/image156.png){width="2.873611111111111in"
height="2.182638888888889in"}

Figure 3‑82: Under All genes tab in GOFFA library

[Under GO Path tab]{.underline}: under this tab, there are three
sub-tabs named biological, molecular function and cellular component. P
Path plots provide a numerical figure-of-merit for the statistical
significant of paths, for the purpose of comparing the potential
significance between paths.

For each of three sub categories, tree paths are ranked in statistical
significance based on equation 1.1. The top 10 tree paths with max value
are plotted for each category.

$- \sum_{}^{}{\text{log}P_{i}}$ (1.2)

Where Pi is Fisher exact test (right-tail) probability value for each
node.

The user can zoom in/out the plot by right-clicking anywhere in the plot
and choosing zoom in/out. The plot can also be saved. Clicking any spot
on a colored line or clicking on legends will cause a return to the Tree
tab, which displays with clicked path highlighted in blue, see Figure
3-83.

In Figure 3-83, the x-axis is the level of the tree (or called node),
y-axis is Log P-value. Moving around the mouse in the plot, the user can
see a blue line and P-value above the line. The user can zoom in/out the
plot by right-clicking anywhere in the plot and choosing zoom in/out.
The plot can also be saved. Clicking any spot on a colored line will
cause a return to the Tree tab display with the pathway nodes on that
line colored in blue, as shown in Figure 3-83C.

![](media/image157.png){width="3.4451388888888888in"
height="2.245833333333333in"}

![](media/image158.png){width="3.042361111111111in" height="2.2625in"}
![](media/image159.png){width="2.376388888888889in"
height="1.9277777777777778in"}

Figure 3‑83: Top ten paths with lowest average P value

**What information you can get from the GO Path Plot?**

GO Path Plot lists the top ten paths ranked in statistical significance.
The x-axis is the level of the path (from root to tip, 1\~10), while the
y-axis is the logP value. The plot supports quick identification of most
significant terms through visualization.

In GO, the terms (functions) from the root to the leaves decent from
general to specific. While comparing the significance of two terms that
has the same p-value, the following questions have to be addressed:

1.  If two terms have same P-value, and in the same level of the tree
    but in different path, which term is more significant? The Path Plot
    identifies the term that associates with the terms along the path
    with the smaller p-values significant (Figure 3-84).

Figure 3‑84: "Protein serine threonine kinase" is more significant than
"Transferase transferring

Phosphorus containing group**"**

2.  If two terms have same P-value, but in the different level of the
    tree and in different path, which term is more significant? The Path
    Plot identifies the term in the lower level significant (Figure
    3-85).

Figure 3‑85: "Receptor signaling Protein serine threonine kinase" is
more significant than "Transferase transferring phosphorus containing
group"

3.  From different path you can reach to the same term, which path is
    more significant? Usually the smaller the average P-value of the
    path is, the more significant the path is. If the path is longer,
    then the average P-value will be smaller which means this path is
    more significant.

Figure 3‑86: The function of the term "Protein kinase" is more close to
"Transferase transferring

Phosphorus containing group**"**

4.  According to the GO Path Plot trend, how to decide which level you
    should stop at and how to interpret the trend of GO Path Plot? If
    two terms have the close p-values, you need to toggle back to the
    tree view by double click the path and the term containing most
    genes should be the significant one, see Figure 3-87.

![](media/image160.png){width="6.0in" height="3.619291338582677in"}

Figure 3‑87: Determine which level you should stop at

## Chip Library

[]{#__RefHeading___Toc112231322 .anchor}**Display ArrayType
Information**: The Chip Library hosts information for spots from any
microarray array types available within MicroarrayDB. The Chip Search
panel (Figure 3-88) is similar to that of Gene Search and Protein
Search. By default, all array elements are searched and displayed at the
launch of Chip Library.

![](media/image161.png){width="3.9722222222222223in"
height="2.5006944444444446in"}

Figure 3‑88: Default display of Chip Search panel.

However, the user can also view the arraytype information for a
particular array type (e.g. different manufacture, species, channels
etc) by specifying it under ![](media/image162.png){width="1.56875in"
height="0.18819444444444444in"}. Figure 3-89 displays all the Affymetrix
array type. Double-click any one of the chip will bring out a
spreadsheet showing the detail of the individual chip.

![](media/image163.png){width="3.5868055555555554in"
height="2.7555555555555555in"}

Figure 3‑89: Display all the Affymetrix arraytype.

[]{#__RefHeading___Toc112231323 .anchor}**Compare Different
ArrayTypes**: By clicking on Comparison at the top left of Chip Library
(![](media/image164.png){width="1.81875in"
height="0.20347222222222222in"}), the user can query from a selected
list of array types for the overlapped (AND) or combined (OR) genes.
First choose the array types to be compared, click "search" button,
decide the common ID for the two array types. The overlapped gene lists
will show in the right panel.

![](media/image165.png){width="5.995138888888889in"
height="2.704861111111111in"}

Figure 3‑90: Overlapped gene list between Affy_HG_133A array and
Affy_HG_133A_2 array.

If the user want to see some specific genes in the viewer, she/he can
highlight those genes and right click (Figure 3-91), choose
![](media/image166.png){width="2.1527777777777777in"
height="0.22083333333333333in"}. The highlighted genes will be marked in
the pre-opened array viewer, see Figure 3-92.

![](media/image167.png){width="5.99375in" height="2.9145833333333333in"}

Figure 3‑91: Choose specific gene records to be marked in the viewer

> ![](media/image168.png){width="5.247222222222222in"
> height="3.8993055555555554in"}

Figure 3‑92: the marked genes chosen in the Chip Library

[]{#__RefHeading___Toc112231324 .anchor}**Summary**: By clicking on
Summary tab at the top left of Chip Library
(![](media/image169.png){width="1.570138888888889in"
height="0.17916666666666667in"}), the right panel will show the array
types grouped in different categories. For example, if the user wants to
see all the Agilent array types, s/he can select "Agilent" in the
Manufacture frame, and the right panel will list only Agilent array
types.

## Toxicant Library

Toxicant Library contains toxicological data and chemical structures
(Figure 3-93). The result table shows all compounds in the Toxicant
Library, one compound per row. The structure of a compound is displayed
in the left panel when its' name is clicked on. Search can be conducted
based on substructure, similarity, (partial) compound name, formula, and
compound ID. It is linked to several public databases (Toxnet, Cactus,
ChemIDplus, ChemACX, ChemFinder, NCI DTP) on small molecules (see Figure
3-93). Currently, binding affinity data obtained at the NCTR on estrogen
receptor and androgen receptor along with information from the CPDB
(Cancer Potency DataBase) for the NTP tested chemicals have been made
available in Toxicant Library.

By clicking the "Edit" button, the user can edit the chemical structure
(e.g. adding functional groups, changing atoms, etc). Structure editing
is done similar to with ISIS draw software, see Figure 3-96, using pull
down menus, icons and options available by left or right-clicking the
chemical structure.

![](media/image170.png){width="5.382638888888889in"
height="3.9493055555555556in"}

Figure 3‑93: Toxicant Library panel.

The user can also highlight a group of chemicals and click
![](media/image171.png){width="0.81875in"
height="0.21388888888888888in"} button and will see how many compounds
and pathway information have been found, see Figure 3-94.

![](media/image172.png){width="6.045833333333333in"
height="1.6611111111111112in"}

Figure 3‑94: Compound pathway

In Figure 3-94, click any record will bring up the pathway map. See
Figure 3-95.

![](media/image173.png){width="5.125in" height="4.186805555555556in"}

Figure 3‑95: Pathway map

![](media/image174.png){width="3.415277777777778in"
height="1.8763888888888889in"}

Figure 3‑96: Molecular structure editing

## EDKB Library

The EDKB Library contains chemical structures and endocrine activity
properties of compounds tested in several assays including more than
3200 records of endocrine activity related endpoints such as binding the
estrogen and androgen nuclear receptors, uterotropic weight gain,
E-screen and combined receptor-reporter gene data (Figure 3-97). All the
data are linked to their associated citations. Activities across
different assays are scaled relative to estradiol, such that they can be
viewed together in a Graphic Activity Profile. The user can link each
chemical to the databases such as Chemfind, Chemplus, etc. and search
data by assay type (in left panel, specify assay type combo) or directly
search any column by typing a key word in the first row of the
spreadsheet and hitting return. The user can also perform chemical
structure or chemical similarity search in the upper left panel. In
similarity search, the 50 most similar chemicals will be reported in a
spreadsheet, one compound per row, with multiple columns listing the
activity information. The structure of a compound is displayed when its'
name is clicked. Search options are similar to those in the Toxicant
Library.

The biological activity for compounds in the EDKB Library is displayed
in a graphic way (Figure 3-97). The X-axis is arranged first by assay
type then by compound. The Y-axis represents the relative potency (in
log10 scale). Each box represents a particular compound and the height
of the box reflects the (max -- min) of multiple biological data for the
same compound in the same assay.

If the user is interested in a particular compound, he can highlight the
compound and select Individual Compound from the pull down list of "More
Info...", and a new window will pop up showing the detail of the
compound information (Figure 3-98).

![](media/image175.png){width="2.94375in" height="2.240972222222222in"}

Figure 3‑97: EDKB Library panel.

The user can also highlight a group of chemicals and click
![](media/image171.png){width="0.81875in"
height="0.21388888888888888in"} button and will get the chemicals
pathway information.

![](media/image176.png){width="3.7493055555555554in"
height="2.9159722222222224in"}

Figure 3‑98: Individual compound information

For more information about EDKB (Endocrine Disruptor Knowledge Base),
please visit <http://edkb.fda.gov/>.

## ID Converter

> ID Converter is a very useful tool for converting one kind of ID to
> another kind

when searching. The user can activate ID Converter by clicking the icon
![](media/image177.png){width="0.16666666666666666in"
height="0.16666666666666666in"} in the Library panel, see Figure 3-99.

.![](media/image178.png){width="1.4131944444444444in"
height="2.151388888888889in"}

Figure 3‑99: ID Converter in Library panel

Once the ID Converter is activated, the user can choose the ID type to
be converted and select the species (Human, Mouse, Rat). There is a
radio button labeled "official name only" under the species option. If
this radio button is checked, the output will only display the official
name without any synonyms or other unofficial name. The user needs to
type/paste the ID in the left panel, then choose the output ID type,
click Convert button. The converted results will be shown in the right
panel. See Figure 3-100. The user can highlight the searched results and
click the library buttons (Gene Library, Chip Library, etc) at the top
to go to different library directly.

![](media/image179.png){width="5.196527777777778in"
height="3.2222222222222223in"}

Figure 3‑100: ID Converter tool

The ID types we accept are: Gene Bank Accession Number, Gene Name, Locus
ID, Swissprot Entry Name, Swissprot Accession Number, Unigene ID, Affy
ID, Image ID, IPI name.

The ID types we can give out are: Locus ID, Gene Name, Unigene ID,
Swissprot Entry Name, Swissprot Accession Number, IPI name, Enzyme
Number, Image ID, Reference sequence number, Protein reference sequence
number.

#  Working with Tools: Quality Control

## Overview of TOOL

The third component of ArrayTrack is TOOL that consists of various
functions for normalizing and visualizing microarray data. These
functions are accessible within ArrayTrack either from the TOOL panel or
Tool pull-down menu (Figure 4-101). The TOOL functionalities are
classified into three categories: Quality Tools, Normalization Methods,
and Visualization Tools. As has been pointed out earlier, these tools
can also be accessed by right-clicking on selected array data sets.

Quality Tools is discussed in this Chapter; Normalization and
Visualization are discussed in Chapter 5 and Chapter 6, respectively.

![](media/image180.png){width="1.8270833333333334in"
height="2.165277777777778in"}
![](media/image181.png){width="3.584722222222222in"
height="1.4354166666666666in"}

![](media/image182.png){width="4.4875in" height="3.6173611111111112in"}

Figure 4‑101: Quality Tools can be accessed from (A) TOOL panel; (B)
Tool pull-down menu; and (C) Right-click on selected array(s).

## Overview of Quality Control

Quality Control provides various visual plots and numerical parameters
for measuring the quality of a hybridization (array). Currently, Quality
Control is available only for arrays for which the original gene
expression file data were input from the GenePix GPR file format (see
Figure 2-12 of Chapter 2).

## Launch of Quality Control

[]{#__RefHeading___Toc112231332 .anchor}**From the TOOL Panel**: When
Quality Control is launched from the TOOL panel (Figure 4-101A), the
user is asked to select array(s)/hybridization(s) for Quality Control
view from a list of hybridizations (Figure 4-102), which by default are
sorted first by the Exp ID and then by Hybridization name. A Quality
Control panel for each of the selected arrays will be displayed after
the OK button is pushed.

![](media/image183.png){width="3.808333333333333in" height="2.8125in"}

Figure 4‑102: Select arrays for Quality Control view.

[]{#__RefHeading___Toc112231333 .anchor}**From the TOOL Pull-down
Menu**: When Quality Control is launched from the TOOL pull-down menu
(Figure 4-101B), a Quality Control view will be opened for each of the
currently selected arrays under the MicroarrayDB Contents tree structure
(Figure 2-28C). However, if no array is selected under the MicroarrayDB
Contents structure and the user tries to launch Quality Control from the
pull-down menu, a warning is displayed (Figure 4-103).

![](media/image184.png){width="3.2256944444444446in"
height="0.9736111111111111in"}

Figure 4‑103: The Quality Control panel can not be launched from the
pull-down menu until one or more arrays are selected from the
MicorarrayDB Contents tree.

[]{#__RefHeading___Toc112231334 .anchor}**From Right-click on Selected
Arrays in the MicorarrayDB Contents Tree**: This may be the most
commonly used way of launching the Quality Control view (Figure 4-101C).
A Quality Control panel will be launched for each of the selected
arrays.

## Contents of Quality Control View

Figure 4-104 and Figure 4-105 are two example views of Quality Control.
Each view is consisted of the following main sections:

[]{#__RefHeading___Toc112231336 .anchor}**Preview of Scatterplot**: The
Scatterplot displays the Cy5 (F635 Median) versus Cy3 (F532 Median)
intensities for spots on the array. The user can have the options of
background subtraction, showing flagged spots, and switching between
Scatterplot and MA Plot. Details about Scatterplot can be found in
Chapter 7.

[]{#__RefHeading___Toc112231337 .anchor}**Preview of Rank Intensity
Plot**: The Rank Intensity Plot is displayed for both Cy5 (red) and Cy3
(green) channels. The user can have the options of background
subtraction and adjusting the two channels to a common mean. Details
about Rank Intensity Plot can be found in Chapter 7.

[]{#__RefHeading___Toc112231338 .anchor}**Quality Control Parameters**:
Various quality control parameters are shown in the middle of the panel
(Figure 4-104 and Figure 4-105) for both the Cy5 (F635 Median) and Cy3
(F532 Median) channels. A **PASS** or **FAIL** mark is shown for each
QC/QA parameter based on the corresponding Threshold value preset.
Threshold values can be reset by clicking on Save. Other QC/QA notes on
RNA quality/integrity, hybridization, and labeling are also shown if
they were entered in the Input Form (Figure 2-6) when loading data to
MicroarrayDB.

[]{#__RefHeading___Toc112231339 .anchor}**Overall Judgment on Array
Quality**: A final judgment (Pass, Fail, Review, or None) can be
assigned to each array and save in MicroarrayDB.

The array shown in Figure 4-104 has a much higher signal-to-noise ratio
than the one shown in Figure 4-105.

![](media/image185.png){width="3.563888888888889in"
height="4.745833333333334in"}

Figure 4‑104: Quality Control view and parameters -- a successful
hybridization with much higher signal-to-noise ratio.

![](media/image186.png){width="4.052083333333333in"
height="5.372222222222222in"}

Figure 4‑105: Quality Control plots and parameters -- a failed
hybridization with very low signal-to-noise ratio.

[]{#__RefHeading___Toc112231340 .anchor}**Function Buttons for Quality
Control**: On the top of the Quality Control view, there are four
function buttons (Figure 4-106). Save as image allows the user to save
the whole Quality Control view into an image file in JPEG, TIFF, or PNG
format. The exact file format is specified by the file name extension of
.JPG, .TIF, or .PNG, respectively. The user can Launch array viewer,
Launch Scatter Plot, and Launch Rank Intensity Plot from the Quality
Control page. Details of these functions are discussed in Chapter 7 on
Visualization.

![](media/image187.png){width="5.157638888888889in"
height="0.5652777777777778in"}

Figure 4‑106: Functional options available within the Quality Control
view.

## Overview of Quality Filtering

Quality Filtering provides the view of filtered spots that meets certain
criteria. The users can immediately see how many gene spots are of good
quality.

[]{#__RefHeading___Toc112231342 .anchor}***Launch of Quality
Filtering***

Quality Filtering is launched in a similar way to launch Quality
Control.

## Contents of Quality Filtering

Figure 4-107 shows the Quality Filtering window that has three colored
sections of filtering criteria: 1) Not-Identified (gray). 2) Un-Detected
(blue). 3) Saturated (white). When the user enters a value of filtering
criteria in the text box in each section, the filtered spots will be
marked with the corresponding color in the viewer (Figure 4-107).

![](media/image188.png){width="5.959027777777778in"
height="5.9430555555555555in"}

Figure 4‑107: Functional options available within the Quality Filtering
view

In Figure 4-107, the user can set the criteria for filtering the spots
that s/he can define as "Not-Identified", "Un-Detected", and
"Saturated". After typing the numbers in the white boxes for filtering,
click "Apply", then the spots that meet the criteria will be marked in
the corresponding colors. For example, the spots marked in grey color
meet the criteria that the user set for "Not-Identified"; the spots
marked in blue meet the criteria for "Un-Detected" and the spots marked
in white are "Saturated" spots.

If the user moves the mouse over any spot, the intensity value for that
spot will be shown in the three colored sections.

[]{#__RefHeading___Toc112231344 .anchor}**Functions for Quality
Filtering**: For marked spots, the user can do further searches in the
other libraries by clicking Spots \> Marked spots \> Libraries, or the
user can paste them in spreadsheets and/or export, see Figure 4-107.

Under the View menu, the user can choose the style and color of the spot
marking, and toggle the tool bar about the spot information, see Figure
4-108.

![](media/image189.png){width="5.991666666666666in"
height="5.998611111111111in"}

Figure 4‑108: Toggle the tool bar about spot information.

The filtered data can be saved as new dataset by clicking the button
"Save as new dataset".

# Working with Tools: Normalization

## Overview

A microarray experiment is based on the analysis/comparison of multiple
arrays (hybridizations). Across-array (hybridization) reproducibility is
the most important criterion for judging the quality of a microarray
experiment. There are many experimental factors that may render the
microarray data inconsistent. Therefore, normalization methods are
needed to (partially) correct systematic variations in microarray data
introduced by experimental factors such as dye bias, efficiency
difference in the cDNA synthesis and labeling reactions, nonlinear
optical feature of the detector (scanner), etc.

Experiment normalization methods are used to standardize microarray data
so that real (biological) variations in gene expression levels can be
differentiated from variations due to the measurement process.
Normalization scales microarray data so that you can compare relative
gene expression levels. There are four normalization methods in
ArrayTrack that can be invoked from the TOOL panel, the Tool pull-down
menu, or right-click on selected array(s) (Figure 5-109).

Double-click on ![](media/image190.png){width="1.2416666666666667in"
height="0.125in"} at the TOOL panel hides or shows the contents
(normalization methods) underneath it.

![](media/image191.png){width="1.9604166666666667in"
height="1.6118055555555555in"}
![](media/image192.png){width="2.2993055555555557in"
height="0.8055555555555556in"}
![](media/image193.png){width="1.6243055555555554in"
height="1.395138888888889in"}

Figure 5‑109: Four normalization methods have been implemented in
ArrayTrack and can be accessed from (A) Right-click on selected
array(s); (B) Tool pull-down menu; and (C) TOOL panel.

By selecting one of the four normalization methods, the user will be
prompted with a table of arrays (hybridizations) which s/he has access
to. The user can select one or multiple arrays for the normalization
method to be applied to by clicking on the OK button (Figure 5-110).

![](media/image194.png){width="4.251388888888889in" height="1.81875in"}

Figure 5‑110: Normalization method applies to a set of selected arrays.

In our experience, however, the user is more likely to use the Database
Contents tree (see Figure 2-28. for details) to select a set of arrays
on which a particular normalization method is to be applied. For
example, by right-clicking on an experiment, the user can quickly select
all the raw datasets arrays contained within this experiment for
normalization (Figure 5-111). By right-clicking on any of the selected
raw arrays, a set of functions including Normalize... can be applied
(Figure 5-112) and the user can choose one of the four normalization
methods (Figure 5-109C).

If you don't have Write permission to the selected array data, you are
still allowed to use the Normalize... function and save your normalized
data into MicroarrayDB. However, you cannot change the original data or
anyone else's normalized data.

![](media/image195.png){width="2.946527777777778in"
height="1.9104166666666667in"}

Figure 5‑111: All raw data (arrays) under an experiment can be
conveniently selected by right-click on the name of the experiment.

![](media/image196.png){width="4.746527777777778in" height="3.09375in"}

Figure 5‑112: Normalization methods can be applied to a set of selected
arrays under the Database Contents tree.

Each normalization method adjusts the original intensity values in a way
as is defined in the individual normalization method and saves the
adjusted (normalized) intensity data in MicroarrayDB.

## Lowess

Lowess (Loess) refers to [Lo]{.underline}cally [We]{.underline}ighted
regression and [s]{.underline}moothing [s]{.underline}catterplots
proposed by W.S. Cleveland (Cleveland, W.S. (1979) "Robust Locally
Weighted Regression and Smoothing Scatterplots," *Journal of the
American Statistical Association*, Vol. 74, pp. 829-836). Lowess
combines the simplicity of linear least-squares regression with the
flexibility of nonlinear regression. It does this by fitting simple
models to localized subsets of the data to build up a function that
describes the deterministic part of the variation in the data, point by
point. In fact, one of the chief attractions of this method is that the
data analyst is not required to specify a global function of any form to
fit a model to the data, only to fit segments of the data.

Lowess has been proposed to normalize microarray data in the hope of
correcting intensity-biased ratio measurement as seen in the MA plot
(see discussion in Chapter 6). The fundamental assumption for Lowess is
that the expression level for most of the genes in the two samples is
unchanged. Lowess only applies to data from two-color platforms. Lowess
is the default method of normalization in ArrayTrack (Figure 5-113).

![](media/image197.png){width="4.746527777777778in" height="2.86875in"}

Figure 5‑113: Parameter settings for Lowess normalization.

The Lowess normalization method performs a robust locally weighted
regression on the log ratio (M) versus log geometric average (A) spot
data, using each spot\'s locally estimated M value for spot by spot
correction of log ratio values. Thus, the Lowess method differs from
many other normalization techniques because it is able to correct
intensity (A) dependent ratio biases in an intensity-specific way.
Visually, this amounts to "straightening out" a curved MA plot. When the
final local regression estimate Mfit of the log ratio has been
calculated for a spot having channel values c1 and c2, the corrected
ratio c1\'/c2\' is determined by

$$\text{log}(\frac{c1'}{c2'}) = \text{log}(\frac{c1}{c2}) - \text{Mfit}$$

*(fitted M value become new zero point of M value at this average
intensity)*

which can be rewritten as

$$\frac{c1'}{c2'} = (\frac{c1}{c2})\frac{1}{b^{\text{Mfit}}}$$

*(where b is the logarithm base for computed M-values)*

To present this correction in two-channel format, we spread the ratio
correction factor reciprocally to both channels, so that the same ratio
correction is achieved:

$$c1' = c1\sqrt{\frac{1}{b^{\text{Mfit}}}}$$

$$c2' = c2\sqrt{\frac{1}{b^{\text{Mfit}}}}$$

There are three parameters that need to be set for a Lowess (Figure
5-113).

[]{#__RefHeading___Toc112231348 .anchor}**Smoothing Factor**: The
smoothing factor parameter determines the number of data points having
nearby (or equal) A values around the A value for a spot that are
included in the spot\'s local regression estimate, expressed as a
fraction 0 \< f \<= 1 of the total number of spots in the dataset. Thus,
for a smoothing factor of 0.2, roughly 20% of the spots with the closest
(or equal) A values to a given spot will be included for the regression
estimate at that spot (however, see the proviso below about equal runs
of A values). Within this window, the spots with the closest A values to
the spot to be estimated are given the most weight, with the weight
falling to zero near the edge of the smoothing window. However, one
proviso applies here: the regression window\'s edges will never lie in
the middle of a run of equal A values. A range of equal A values at the
edge of the local regression window may cause the number of points to
include in the regression to differ somewhat from that determined by the
smoothing factor alone. If the right window edge would end within a
range of equal A values based on the smoothing factor, then the window
is extended to the right to include the full run of equal A values. On
the other hand, the left window edge never lies within a range of equal
A values because of the way the algorithm moves the window through
ascending A values when choosing new points to estimate: When the
algorithm estimates an M value at one point, that fitted M value is
automatically set for all points with equal A values, and the next
window will begin past all of these equal A values. This behavior
delivers an important property of Lowess normalization which is that any
two spots with the same A value will have the same fitted M value.

[]{#__RefHeading___Toc112231349 .anchor}**Robustness Iterations**: The
Lowess regression algorithm is "robust", meaning that it resists giving
undue influence to outlying data points. Once a fitting of M values for
the A values has been obtained, the regressions can be repeated but this
time penalizing points with outlying M values via robustness weights
based on residuals relative to the latest estimated fit. Spots with
large residuals relative to the fitted M values will be given relatively
less weight in the subsequent regressions. Note that these robustness
weights are separate from the weights applied based on the A values'
distance to the center of the regression window, which are always in
effect even in the initial regression. The robustness iterations
parameter determines the number of times after the first that the
regressions will be done at each spot, for the purpose of reweighting
the outlying data points into relative insignificance in the
regressions. Thus, to give outlying data points the same weights as any
other points (i.e. weights based on their A values alone), 0 can be
chosen for robustness iterations.

[]{#__RefHeading___Toc112231350 .anchor}**Delta**: The delta parameter
is provided to speed up Lowess calculations on large data sets. Setting
delta \> 0 will let the algorithm skip over A values that are closer
than delta to an A value that already has an estimated M value, using
linear interpolation between the estimates. Leaving delta unset (leaving
the field empty) will result in delta defaulting to 0.01\*the range of A
values, which is usually a good compromise between speed and accuracy.
Setting delta to 0 is the most accurate but slowest setting; it means
that the regression is done for every A value in the data.

The default setting for smoothing factor, robustness iteration, and
delta is 0.2, 3, and empty (0.01\*the range of A values) as shown in
Figure 5-113.

Figure 5-114 shows the effect of Lowess on a dataset that showed
intensity-based ratio bias. Lowess effectively removed intensity-based
bias in the ratio values.

![](media/image198.png){width="2.486111111111111in"
height="2.1020833333333333in"}
![](media/image199.png){width="2.4034722222222222in"
height="2.0493055555555557in"}

Figure 5‑114: Systematic, intensity-based ratio bias (Left) is corrected
by Lowess (Right). The yellow line shows the Lowess fitted values, Mfit.

## Total Intensity Normalization

Total Intensity Norm. only applies to two-color platforms and tries to
"balance" the total intensity of the two channels (samples) in three
steps:

1)  Compute the sum of each channel's intensities, optionally
    subtracting backgrounds;

2)  Let *r* be the ratio of these sums, i.e., *r* = (sum ch1 vals)/(sum
    ch2 vals);

3)  Scale factor for first channel is $\frac{1}{\sqrt{r}}$ and
    $\sqrt{r}$ for channel 2.

Normalized data are permanently saved in MicroarrayDB and have the
following properties:

1)  Ratio of intensity sums for the two channels computed for the
    normalized data should be 1.0;

2)  Each spot is adjusted so that the ratio of channel values is 1/*r*
    times it\'s un-normalized value.

> The effect of Total Intensity Norm is the same as mean intensity
> normalization.

## Channel Scaling

Channel Scaling applies to both one-channel and two-channel platforms
and adjusts each channel's intensity values according each Median, Mean,
and user-specified Target Value (Figure 5-115). It is accomplished by
multiplying each intensity value by *T/m*, where *m* is the mean or
median of the channel data and *T* is the target mean/median value
option (default is 1,000). Channels are scaled separately in the case of
two-channel data. Normalized channel data will have a mean/median
matching the target value option.

> The effect of Channel Scaling is the same as median intensity
> normalization.

![](media/image200.png){width="4.196527777777778in"
height="2.5319444444444446in"}

Figure 5‑115: Channel Scaling normalization.

## GenePix Mean Log Ratio Normalization

GenePix Mean Log Ratio Norm. only applies to two-channel platforms
(Figure 5-116). The following steps are involved:

1)  Compute channel ratios after respective background subtraction if
    specified (it doesn't matter which channel is the numerator and
    which is the denominator);

2)  If the exclude ratio limit parameter *M* has been specified
    non-zero, then spots are ignored whose ratios don\'t lie between
    1/*M* and *M*; the default value for *M* is 10;

3)  Take log of remaining ratios (base doesn\'t matter, will cancel
    out);

4)  Apply anti-log to the average of these log ratios to get *r*;

5)  Scale factor for numerator channel is $\frac{1}{\sqrt{r}}$and
    $\sqrt{r}$ for denominator channel (applied after background
    subtraction, if specified).

After normalization, the average of the log of channel ratios is 0,
corresponding to r = 1.0. Each spot value is adjusted such that the
ratio of channels is 1/*r* times it\'s un-normalized value.

![](media/image201.png){width="4.0784722222222225in"
height="2.4652777777777777in"}

Figure 5‑116: GenePix Mean Log Ratio Normalization

## Linear and Lowess

> This normalization method is the combination of Linear and Lowess.
> First, values for each channel are multiplied by T/m where m is the
> mean or median of the channel data and T is the target mean/median
> value option (default is 1000). Then a Lowess normalization is
> performed on the resulting scaled channel data. See Figure 5-117. At
> the right side of the window, there are three pull-down lists letting
> you have the choice for background subtracting, scaling and flagging
> the spot. Be aware that the default for Scaling is Geometric Mean. The
> user can select Median or Mean for different approach.

![](media/image202.png){width="4.753472222222222in"
height="2.8430555555555554in"} ![](media/image203.png){width="1.125in"
height="0.7541666666666667in"}

Figure 5‑117: Linear and Lowess Normalization

[]{#__RefHeading___Toc112231355 .anchor}***Warning***

Although different normalization methods can be applied to the same raw
data, the user should apply the same normalization method to all the
arrays within the same experiment for a meaningful microarray data
analysis and comparison. The choice of a particular normalization method
is solely the responsibility of the user. It is also advised that data
normalized within ArrayTrack may need to be further "pre-processed"
(e.g. centering around mean zero and variance one) before being
systematically compared and analyzed by other data analysis software,
depending on the particular analysis methods.

# Working with Tools: Analysis Tools

[]{#__RefHeading___Toc112231357 .anchor}***Overview***\
The analysis tools can be accessed within the "Tool" window (Figure
6-118A), or from tool menu (Figure 6-118B), or from database with data
selected and right-clicking (Figure 6-118C). Analysis tools are provided
to perform four mathematical/statistical operations: 1) T-test, 2)ANOVA,
3) P-value, 4) Clustering, 5) Principal Components Analysis (PCA).

![](media/image204.png){width="1.75in" height="1.1527777777777777in"}
![](media/image205.png){width="3.3743055555555554in"
height="1.3958333333333333in"}

![](media/image206.png){width="4.814583333333333in"
height="2.4923611111111112in"}

## Figure 6‑118: Access analysis tools

###### Before proceeding to explain the use of each analysis tool, please note that the various analysis tools are interlinked. For example, after applying the T-test to two sets of data, ArrayTrack will enable additional analysis tools to be applied to the selected data (more about this will be explained further below).

## ***T-test and ANOVA***

The T-test is used to compare two groups of data. It tells us if the
variation between two groups is \"significant\". ANOVA (Analysis of
Variance) is used to compare multiple groups. The users might ask if
they can just do T*-*tests for all the pairs of groups. Multiple T-tests
are not the answer because when the number of groups grows, the number
of needed pair comparisons grows quickly. For example, if there are 7
groups of data, there will be 6+5+4+3+2+1 = 21 pairs. So the comparison
will be too complicated. ANOVA puts all the groups of data into one test
and gives us one P for the null hypothesis. There are three ways to
activate T-test analysis and ANOVA:

1\) Clicking the T-test icon under "Analysis Tool" in the Tools panel
will pop up a window (Figure 6-119A) that is used for choosing the
dataset to do the T-test analysis. The user can click "Browse" button to
choose the data file which must be combined in advance into one data
file containing multiple data set. If the user click "Gene ID's" button,
a window will pop up and allow the user choose different ID types that
will be shown in the T-test result, see Figure 6-119B.

![](media/image207.png){width="2.290277777777778in"
height="1.9430555555555555in"}
![](media/image208.png){width="1.7354166666666666in"
height="1.8604166666666666in"}

Figure 6‑119: Choosing the data after activating T-test through the
Tools panel

2\) First choose the dataset from database panel as shown in (Figure
6-118C), and then right click and choose Analysis -\> "T-test ...".
Accessing in this way, the user doesn't need to combine the data files.

3\) Choose datasets from database panel and then click Tool pull-down
menu -\>choose "Analysis" -\> "T-test ...".

As shown in Figure 6-118B &C, there are three options for choosing
T-test/ANOVA: 1) "T-test/ANOVA" let the selected data be exported
directly to the T-test or ANOVA; and 2) "T-test with custom data
options" provides the user the options to select other part of the data
to be analyzed by T-test; 3) "ANOVA with custom data options" provides
the ability to select additional data options as illustrated in Figure
6-120 and further explained below.

![](media/image209.png){width="3.0694444444444446in"
height="3.0805555555555557in"}

Figure 6‑120: Assign dataset to groups

In Figure 6-120, the user can assign the dataset to different groups by
highlighting the datasets first and then clicking "Assign to new group"
button. The assigned datasets will be marked with a yellow-colored
number. The dataset can also be assigned to an existing group by
clicking the button ![](media/image210.png){width="0.4444444444444444in"
height="0.27569444444444446in"}and typing the group number. The user can
unassign the group by clicking the "Unassign" button. If there are two
assigned groups then a T-test will be run, if there are three or more
groups then an ANOVA test will be run. Before running the T-test, the
user can choose to run T-test on all genes or on selected gene list by
clicking the button ![](media/image211.png){width="0.6041666666666666in"
height="0.1840277777777778in"}. A window will pop up letting the user
choose the gene list for the T-test.

![](media/image212.png){width="3.001388888888889in" height="1.41875in"}
![](media/image213.png){width="2.9583333333333335in"
height="1.413888888888889in"}

Figure 6‑121: Select gene list for T-test analysis

For the T-test analysis, there are two options -- 1) P values from
theoretical t-distribution, 2) P values from permutations of group
assignments. Under option 1 the user can choose "Welch t-test" and
"Simple t-test". Under option 2 the user can set the criteria for the
permutation T-test.

The following is the formula of permutation T-test:

> $$P_{\text{permuatation}} = \frac{\text{Numberof}(P_{k} < P_{0})}{\text{NumberofPermutation}}$$
>
> where ![](media/image214.png){width="0.9986111111111111in"
> height="0.38333333333333336in"}

The user can choose to calculate P~permutation~ using any number that
less than or equal to *Numberof Permutation.* For example, if n=12, k=6,
then P~k~ = 924. So the user can decide to calculate P~permutation~
using NumberofPermutation = 924 or limit the number of permutation to
any number that is less than 924. See Figure 6-122.

![](media/image215.png){width="4.372222222222222in"
height="4.372222222222222in"}

Figure 6‑122: Set the criteria of the T-test

Click "Do tests" button, the results of T-test/ANOVA will be shown, see
Figure 6-124.~~\
~~

The "Export Options" window is used to select the forms in which data
will be exported prior to the T-test. For example, in Figure 6-120, only
log intensity data will be exported in log base 2, and flag values will
be included as integers. By clicking the "Dataset Naming" tab, the user
can choose what parts of the data element names are to be included in
the export data table, including the hybridization name, raw dataset
description and normalization description. After option selection,
clicking OK results in the "T Test" window appearing; in this window the
user clicks the boxes to assign each data element to either the first or
the second group, as shown in Figure 6-123A. The "Gene Id's" button
opens another window (see Figure 6-123B) that allows choosing the ID
types that will be shown in the data export results.

Note that multiple data can be selected for export, for example, by
selecting both "include log intensities" and "include intensities", both
types of the data will be included in the data to be exported. The data
options available to be selected in the "Export Options" table depend
upon the form of the data that was selected prior to initiating the
T-test from the Analysis Tools icon. For example, if the initial data
was log data, then intensity data cannot be selected.

Clicking the "Gene Id's..." button will pop up the window that allows
choosing different types of id's to be shown in the analysis results, as
shown in Figure 6-123B.

When the "Do Tests" button is clicked, the T-test is performed and the
results are displayed in a new window titled "T-test Results", and shown
in Figure 6-124. The bottom of the results window contains additional
functions that can be applied to the results, such as filtering out
results above a specified p-value, etc. Buttons are also provided at the
bottom of the "T-test Results" window that allows additional operations
on the filtered results, such as volcano plot, HCA, PCA, etc. The T-test
results window also allows for launching searches of the gene library,
pathway library and protein library, as shown in Figure 6-124 and
further explained below.

![](media/image216.png){width="2.877083333333333in"
height="3.377083333333333in"}
![](media/image217.png){width="2.379166666666667in"
height="2.7493055555555554in"}

Figure 6‑123: assign data in two groups (A) and choose Gene ID types (B)

![](media/image218.png){width="4.872916666666667in"
height="4.113888888888889in"}

Figure 6‑124: T-test results window

In Figure 6-124, the users can get the value of the T-test result like
mean of group 1, mean group 2 and mean difference (group 2 - group 1),
etc. Once the T-test results are obtained, ArrayTrack provides several
types of filters and interactive graphics tools to aid the user in
evaluating the data, generally, and in choosing a significant set of
genes, particularly. As shown in Figure 6-124, the user can perform
significance filtering. A P-value cutoff can be placed in the text box,
with an option for Bonferroni correction in the adjacent drop-down menu.
Alternative, a text box is available for specifying a certain number of
genes with the lowest P-values. Text boxes are also provides for
removing spots below the specified mean channel intensity and below a
specified minimum fold-change. The filters can be applied in parallel.

Highlight one gene and choose "Selected Spot" pull-down menu -\> Create
Bar Chart... will launch the Gene Expression Bar Chart.

Clicking the ![](media/image219.png){width="0.8333333333333334in"
height="0.20833333333333334in"} button causes a volcano plot of the
selected export data to be produced and displayed in a new window.
Notice that in the example volcano plot of Figure 6-125, the plot is
partitioned into six areas by two vertical (x-axis representing the
fold-change scale) and one horizontal (y-axis representing the p-value
scale) dashed lines. The volcano plot is intended as a graphical tool to
select a list of significant genes based on some combination of
p-value/fold-change criteria, or to examine the effects of p-value
and/or fold change cutoff values on the significant gene list. Usually,
the genes appearing in the upper left and upper right areas, areas A and
C in Figure 6-125 will comprise the significant gene list, that is, the
spots denoted by red in Figure 6-125.

The volcano plot produced by ArrayTrack has a number of features and
interactive capabilities providing particular utility for selection of a
significant gene list, as summarized below:

1\) The mouse cursor can be used on one of the vertical dashed lines to
drag the lines either further apart or closer together, increasing or
decreasing, respectively, the fold-change encompassed between the
vertical lines. The corresponding absolute fold change is displayed to
the right of the right side vertical dashed line, and can be seen to
change as the vertical position along the x-axis is changed.
Alternatively, a fold change difference can be typed into the text box
above the plot, causing the vertical lines to be adjusted to that value.

2\) Similarly, the mouse cursor can be used to move the horizontal
dashed line up or down to change a hypothetical P-value cutoff, with
spots above the horizontal line being below the p-value of the
intersection of the y-axis. Alternatively, the p-value cutoff can also
be typed into the text box above the plot, which will cause the
horizontal line to be adjusted to that value.

3\) Floating the cursor over a spot will cause information about the
spot to be displayed above the plot. The upper line gives numerical
values for fold-change, p-value and average channel intensity (average
intensity of the spot across all channels and all microarrays). The
second line gives the identification information for the spot that was
selected prior to the T-test, as shown in Figure 6-123. The third line
displays the number of significant genes corresponding to: 1) both the
fold-change and P-value cutoff (areas A and C of Figure 6-125); 2) the
P-value cutoff alone (Areas A, B and C of Figure 6-125); and, 3) the
fold-change cutoff alone (areas A, C, D and F in Figure 6-7); the number
of non-significant genes is also displayed (area E of Figure 6-125).

4\) The adjustable P-value and fold change lines divide the plot into
several color-coded regions

(see the keys at the right of the plot) that correspond to regions of
significance or non-significance that depend on cutoff values. There are
three display options chosen by drop-down menu: 1) color by region; 2)
color by mean channel intensity (Red to blue); and color by mean channel
intensity (gray scale), as shown in Figure 6-125.

> ![](media/image220.png){width="3.035416666666667in"
> height="3.234027777777778in"}
>
> Figure 6‑125: Volcano Plot

6)  The pull-down menu labeled "Selected Spots" provides the user the
    ability to: a) create a significant gene lists that corresponds to
    the desired P-value and fold-change combination; b) perform cluster
    analysis on the selected significant genes; c) perform search of the
    ArrayTrack gene, protein and pathways libraries for the selected
    significant genes; d) mark the selected significant genes in other
    ArrayTrack viewer windows that might be open (e.g., image viewer).

![](media/image221.png){width="3.248611111111111in"
height="1.1069444444444445in"}

Figure 6‑126: Selected spots pull-down menu

## ***P-value Plot***

A p-value is associated with a test statistic. It is \"the probability,
if the test statistic really were distributed as it would be under the
null hypothesis, of observing a test statistic \[as extreme as, or more
extreme than\] the one actually observed\". The smaller the P value, the
more strongly the test confirms the null hypothesis (Econterms).

The user can get P-value plot from T-test result window by clicking the
![](media/image222.png){width="0.8465277777777778in"
height="0.21736111111111112in"} button (see Figure 6-124).

Also P-value Plot tool can be activated by clicking
![](media/image223.png){width="0.9375in" height="0.17708333333333334in"}
under the Tool/Analysis section. See Figure 6-118A. A pop up window with
title "Choose Data Source" allows the user to choose the data file
(Figure 6-127). The user needs to assign the columns to the
corresponding buttons (e.g. clicking column "P" and then click "P-value"
button), then click OK to get P-value plot.

![](media/image224.png){width="5.370833333333334in"
height="3.216666666666667in"}

Figure 6‑127: Choose data file do get p-value plot

![](media/image225.png){width="4.433333333333334in"
height="4.502083333333333in"}

Figure 6‑128: P-value plot

In P-value plot window, the y-axis is rank and x-axis is p or q (q= 1-p,
user can choose rank(q) vs q or p vs rank(p)). By dragging the gray
dashed line, the user can change the p(or q) value and consequently the
selected spots(#sel) and unselected spots(#unsel).

The blue dashed line can indicate the number of truly affected and truly
unaffected genes.

In P-value plot window, the user can also access volcano plot,
clustering analysis and other libraries. See Figure 6-129.

![](media/image226.png){width="3.1506944444444445in"
height="1.6118055555555555in"}

Figure 6‑129: Access other functions from p-value plot

## ***Clustering*** 

Hierarchical Clustering Analysis (HCA) can be activated by 1) clicking
the HCA icon under the Analysis tool, or 2) choosing the dataset in
database panel, right clicking and then choosing "Analysis-\>
Hierarchical Component Analysis", see Figure 6-118.

1\) If HCA is activated in the first way, a pop up window will ask the
user to select a file to do analysis. The data file must be text file
format (.txt) (see Figure 6-130A). The text file will be shown after the
user click "Open" button (see Figure 6-130B). Then the user can do data
analysis by clicking "Analysis" in the menu bar or clicking HCA icon
![](media/image227.png){width="0.16666666666666666in"
height="0.16666666666666666in"}. The result is shown in Figure 6-132.

![](media/image228.png){width="2.6243055555555554in"
height="2.265972222222222in"}
![](media/image229.png){width="2.515277777777778in"
height="2.248611111111111in"}

Figure 6‑130: Select data file to do data analysis

2\) If HCA is activated in the second way, the "Export options" window
pops up (Figure 6-131). Clicking OK will bring out the HCA plot, see
Figure 6-132.

> ![](media/image230.png){width="4.125in" height="3.4875in"}

Figure 6‑131: Export Option window

![](media/image231.png){width="3.4472222222222224in"
height="0.875in"}![](media/image232.png){width="2.604861111111111in"
height="3.25in"} ![](media/image233.png){width="2.5944444444444446in"
height="3.372916666666667in"}

> Figure 6‑132: HCA analysis result

The user can zoom in/out the plot by clicking
![](media/image234.png){width="0.1798611111111111in"
height="0.17916666666666667in"} or
![](media/image235.png){width="0.18055555555555555in"
height="0.18055555555555555in"} icon at the top of the window. The user
can also change the font and the color of the label for each branch of
the tree cluster by right-clicking the branch and choosing the right
options. From the HCA plot there is a link to Gene Library according to
the available IDs. The user can save the HCA image to the local drive.
See Figure 6-132.

##  PCA

Principal Component Analysis is a classical statistical method and is a
way of identifying the data patterns and highlighting the data's
similarity and differences. The main use of PCA is to reduce the
dimentionality of a data set while retaining as much information as
possible. In PCA plot, the first principal component is the combination
of variables that describe the greatest amount of variation. The second
principal component defines the next largest amount of variation and is
independent to the first principal component, and so on.

Similar to the other analysis tools, PCA can be activated by 1) clicking
the icon PCA under the Tool/Analysis section or 2) choosing the
normalized dataset in database panel, right clicking and then choosing
"Analysis-\> Principal Component Analysis.

1\) If the user activates PCA in the first way, the following window
pops up. The user can open a file from the local drive or import data
from database to do PCA analysis. See Figure 6-133.

![](media/image236.png){width="2.178472222222222in"
height="1.5180555555555555in"}

Figure 6‑133: Open a file or import data from database for PCA

![](media/image237.png){width="3.053472222222222in"
height="3.0069444444444446in"}.

Figure 6‑134: Choose data from the database to do PCA

In Figure 6-134, multiple hybridization data can be selected. Clicking
OK will bring up the "Export option" window (Figure 6-131). Click OK
button, then the PCA plot will show up (see Figure 6-135A).

![](media/image238.png){width="2.7493055555555554in"
height="1.9270833333333333in"}
![](media/image239.png){width="2.6659722222222224in"
height="1.8895833333333334in"}

Figure 6‑135: PCA plot

2\) If the user activates PCA in the second way, the "Export option"
window pops up, click OK then PCA results will show up.

In Figure 6-135A, if the user circle any spots in the plot, the
"Relative Variance" view will be switched to the "Score" tab view, with
the corresponding records highlighted (Figure 6-135B). The user can also
click EigenValue and Loadings tab to see the Eigen value and loadings
value for each principal component.

The user can also view the PCA plot in three dimensions by clicking the
3D view button, see Figure 6-136. The spot color can be changed by
selecting it and then right-clicking-\> choose new color. The spot shape
can be changed to cube or sphere.

![](media/image240.png){width="4.180555555555555in"
height="3.029166666666667in"}

Figure 6‑136: PCA three dimension plot

For the three-dimension PCA plot, the user can do the following
movements:

1)  Zoom in/out: hold shift key while drag the plot to bigger/smaller
    view.

2)  Move the plot without rotation: hold ctrl key while drag the plot to
    the desired position.

3)  Rotate: just drag the plot.

4)  Reset: click the "Reset Viewpoint" button will bring the plot to the
    original position.

# Working with Tools: Visualization

## Overview

Visualization is an important step in analyzing microarray data and can
be used to identify abnormalities within the data. For example, when two
(replicate) arrays are compared to each other, the user can gain an
understanding on reproducibility of the experiment. It is highly
recommended that the user maximize the use of the visualization tools
made available within ArrayTrack before doing massive (and
time-consuming) data mining and statistical analysis. Any suspicious
arrays should be dealt with care when biological and statistical
conclusions are withdrawn. Don't forget: "*Garbage-in-garbage-out*."

The following visualization tools have been implemented within
ArrayTrack: Scatter Plot, MA Plot, Mixed Scatter Plot, Virtual Array
Viewer, Rank Intensity Plot, P-Value Plot and Cross-Dataset Gene Bar
chart.

Most of these functions can be accessed in three different ways (Figure
7-137): (A) from the TOOL panel; (B) from the Tool pull-down menu; and
(C) by right-clicking on one of the selected arrays.

Double-click on ![](media/image241.png){width="1.3020833333333333in"
height="0.16666666666666666in"} at the TOOL panel hides or shows the
contents (visualization tools) underneath it.

Many of the visualization tools are interconnected to each other, thus
allowing the user to gain a more in-depth view of the data from
different perspectives, as will be seen from the detailed discussions in
the following sections.

![](media/image242.png){width="3.3916666666666666in"
height="1.4305555555555556in"}
![](media/image243.png){width="1.3270833333333334in"
height="1.5402777777777779in"}

![](media/image244.png){width="4.227777777777778in"
height="2.6333333333333333in"}

Figure 7‑137: Three ways of accessing many Visualization Tools
implemented in ArrayTrack: (A) TOOL panel; (B) Tool pull-down menu; and
(C) Right-clicking on one of the selected arrays.

## Scatter Plot

[]{#__RefHeading___Toc112231367 .anchor}**Default Plot**: By default,
Scatter plot for data plots the fluorescence intensity data of the Cy3
channel versus those of the Cy5 channel for the same array. An error
message will be displayed if it is trying to be applied to one-channel
data. Figure 7-138 shows the scatter plot for hybridization
NCTR_Mouse_20K. The user can toggle data points that are flagged out
e.g. by the Axon GenePix Pro software (Flagged data points are generally
those spot features that do not show reliable fluorescence intensity
signal; shown as grey cross symbols in Figure 7-138). The user can also
choose to plot the background-subtracted intensity data. At the bottom
of the plot, statistics for the two channels are displayed. When the
mouse moves over the spots, the identity and intensity values of that
spot are displayed. "Interesting" spots can be selected by
click-and-circle using the mouse, and the selected spots are colored in
red.

![](media/image245.png){width="5.071527777777778in" height="4.325in"}

Figure 7‑138: Scatter Plot showing the fluorescence intensities for the
two hybridization channels.

Right-click on the plot area will pop up a list of actions that can be
taken on the scatter plot (Figure 7-138). All the actions are
self-explaining and are discussed as follows.

[]{#__RefHeading___Toc112231368 .anchor}**Selected Spots**: For selected
spots, five actions can be applied:

1)  Launch library search against Gene Library, Pathway Library, Protein
    Library or Chip Library using the GenBank accession numbers of
    selected spots as queries. See Chapter 3 for details on library
    search.

2)  View gene expression data for the selected spots in a spreadsheet
    form. For details, see Chapter 9: Data Export.

3)  Launch cross-dataset gene Bar Chart for selected spots (for a
    maximum of five spots). For details, see the section on Bar Chart in
    this Chapter.

4)  Launch a new Virtual Array Viewer with the selected spots marked.
    For details, see the section on Virtual Array Viewer in this
    Chapter.

5)  Select (mark) the same set of selected spots in all other viewers of
    the same array type.

[]{#__RefHeading___Toc112231369 .anchor}**Search for Genes in This
Viewer**: Allows the user to enter/paste the GenBank accession numbers
of a list of interested genes to find their location in the scatter plot
(Figure 7-139A). Spots already selected can be kept by choosing "Retain
current selections". Optionally, the search can be conducted on all
other viewers of the same array type. After clicking on Search, genes
found on this scatter plot are marked in red (Figure 7-139C); a
![](media/image246.png){width="0.1in" height="0.10833333333333334in"} or
![](media/image247.png){width="0.1in" height="0.11666666666666667in"}
mark is shown before each GenBank accession number to indicate the
presence or absence, respectively, of that individual gene on the
scatter plot (Figure 7-139B).

![](media/image248.png){width="1.7090277777777778in"
height="1.679861111111111in"}
![](media/image249.png){width="1.7326388888888888in"
height="1.6972222222222222in"} ![](media/image250.png){width="1.90625in"
height="1.7166666666666666in"}

Figure 7‑139: Locate a list of interested genes (by GenBank accession
numbers) on the scatter plot.

[]{#__RefHeading___Toc112231370 .anchor}**Axes Options**

1)  Set ranges... sets the range of the plot in X and Y axis.

2)  Reverse axes options exchanges the assignment of the X and Y
    variables (Figure 7-140).

3)  Force origin (0, 0) to be visible allows the origin (0, 0) of the
    Scatter Plot to be visible. Otherwise, ArrayTrack automatically sets
    the limits of the X and Y axes based on the range of the X and Y
    values.

4)  Constrain MA plot's M axis to \[-3.0, 3.0\] (MA plot only): If this
    option is toggled on, the limits of the Y axis (i.e. M or log fold
    change) is set to be \[-3, 3\]. Otherwise, ArrayTrack automatically
    sets the limits based on the range of the M values.

5)  Log base: allows the axes to be switched to log base 2, log base 10
    or log base e.

![](media/image251.png){width="4.813888888888889in"
height="1.6590277777777778in"}

Figure 7‑140: Axes Options for scatter plot.

[]{#__RefHeading___Toc112231371 .anchor}**Show Lowess curve** (MA plot
only): The Lowess normalization curve is displayed in yellow (Figure
7-141). For details about Lowess, see Chapter 5 on Normalization
Methods.

[]{#__RefHeading___Toc112231372 .anchor}**Adjust Lowess parameters** (MA
plot only): The Lowess Parameters panel (Figure 7-142) allows the user
to adjust the three parameters. As expected, when the Smoothing Factor
was adjusted from its default value of 0.2 (e.g. 20% of all data points)
down to 0.01 (1%), the Lowess curve showed more variation (Figure
7-141B), resulting form the fitting of a much smaller portion of the
neighboring data points. The user has the option of setting these values
as the default values for the remaining session.

[]{#__RefHeading___Toc112231373 .anchor}**Save as image**: The graphics
can be saved as an image file in JPEG, TIFF, or PNG format. The exact
file format is specified by the file name extension of .JPG, .TIF, or
.PNG, respectively.

![](media/image252.png){width="2.557638888888889in"
height="1.7319444444444445in"}
![](media/image253.png){width="2.515972222222222in"
height="1.7020833333333334in"}

Figure 7‑141: Lowess curve is displayed in yellow. A: Default Lowess
parameter settings (smoothing factor = 0.2); B: Smoothing facto = 0.01.

![](media/image254.png){width="2.2423611111111112in"
height="1.2243055555555555in"}

Figure 7‑142: Adjusting Lowess parameter settings for the current MA
plot.

## MA Plot

MA Plot only applies to two-channel data (Figure 7-143). It is a special
form of Scatter Plot in which the X axis is the log geometric average
(**a**ddition) of the intensity values of the two channels and the Y
axis is the log fold change (**m**inus of log intensities):

$$X = \text{log}\sqrt{\text{Cy}5 \ast \text{Cy}3} = \frac{1}{2}(\text{log}\text{Cy}5 + \text{log}\text{Cy}3)$$

$$Y = \text{log}(\frac{\text{Cy}5}{\text{Cy}3}) = \text{log}\text{Cy}5 - \text{log}\text{Cy}3$$

The MA Plot is also called *RI* plot, where *R* refers to log
***R***atio and *I* refers to log average ***I***ntensity. All the
functions described in Scatter Plot apply to MA Plot. In fact, Scatter
Plot and MA Plot are interchangeable (Figure 7-143) and applicable only
to two-channel microarray data.

![](media/image255.png){width="2.3506944444444446in"
height="2.3506944444444446in"}

Figure 7‑143: MA Plot and Scatter Plot are interchangeable.

## Mixed Scatter Plot

Mixed Scatter Plot applies to both two-channel and one-channel data. It
allows the user to compare two arrays in one plot in addition to the
options of plotting different information items for the same array (if
only one array is selected before launching this function). If more than
two arrays are selected, Mixed Scatter Plot will not be accessible by
right-click on the selected arrays.

After selecting two arrays and launching Mixed Scatter Plot, a plot
displaying the Cy3 channel intensities of the two arrays is shown
(Figure 7-144). Options such as background subtraction, flagged spots,
statistics panel, and log transformation can be selected. There are
several data items that can be used as variables for the X and Y axes
(Figure 7-144 Right). Combination of such choices makes it possible for
displaying various kinds of scatter plots including MA Plot and the
regular Scatter Plot. Similar to Scatter Plot and MA Plot, right-click
on the plot area will allow access to many functions applicable to the
Mixed Scatter Plot.

![](media/image256.png){width="1.4333333333333333in"
height="1.425in"}![](media/image257.png){width="2.1638888888888888in"
height="3.2416666666666667in"}
![](media/image258.png){width="1.4416666666666667in"
height="1.2784722222222222in"}

Figure 7‑144: Mixed Scatter Plot (Left) and options available for
plotting (Right).

## Rank Intensity Plot

Rank Intensity Plot applies to both two-channel and one-channel data. It
provides a convenient way of visualizing the distribution of intensity
data across all the spots on the array: a closer Rank Intensity Plot
indicates a closer distribution of the data items (e.g. Cy3 vs. Cy5
intensities from the same array). In the Rank Intensity Plots shown in
Figure 7-145, the X axis represents the intensity sorted rank (from low
to high) and the Y axis is the corresponding log10 intensity.

Rank Intensity Plot is shown in the Quality Control panel (Chapter 4).

![](media/image259.png){width="1.6916666666666667in"
height="1.3652777777777778in"}
![](media/image260.png){width="1.6659722222222222in"
height="1.3631944444444444in"}
![](media/image261.png){width="1.5819444444444444in"
height="1.3319444444444444in"}

Figure 7‑145: Rank Intensity Plot. In the middle plot, mean intensities
for Cy3 and Cy5 channels are adjusted to equal mean of zero. In the
right plot, Lowess normalized data are plotted.

## Choosing Data Source for Plotting

[]{#__RefHeading___Toc112231378 .anchor}**From MicroarrayDB Arrays**:
When Scatter Plot, Mixed Scatter Plot, MA Plot, and Rank Intensity Plot
are activated from the TOOL panel or the pull-down menu, a list of
arrays stored in the MicroarrayDB is displayed in a spreadsheet from
(Figure 7-146). The default for data source type is MicroarrayDB
database and the data type to be plotted is raw data. Multiple arrays
can be selected for plotting.

[]{#__RefHeading___Toc112231379 .anchor}**From Local Data Files**: When
the user choose file as the data source type (Figure 7-146), a local
disk file can be input for plotting (Scatter Plot, Mixed Scatter Plot,
MA Plot, and Rank Intensity Plot), as shown in Figure 7-147. The table
columns in the local file can be mapped to the required data fields
(e.g. the X and Y coordinates) for plotting.

![](media/image262.png){width="3.388888888888889in"
height="2.9618055555555554in"}

Figure 7‑146: List of arrays in MicroarrayDB. Selected arrays will be
plotted after click on OK.

![](media/image263.png){width="5.435416666666667in"
height="3.9409722222222223in"}

Figure 7‑147: Inputting local data file for plotting.

## Virtual Array Viewer

[]{#__RefHeading___Toc112231381 .anchor}**Overview**: The Virtual Array
Viewer (Figure 7-148) displays gene expression data derived from the
original array image in a pseudo image format. It applies to both
two-channel and one-channel microarray data. In Virtual Array Viewer,
the arrangement of spots is exactly the same as in the original image
(constructed from the Block/Row/Column description about array elements
in the ArrayType Information File). The brightness represents the
(average) intensity of the spot, and the color indicates the ratio (for
two-channel system). The difference between Virtual Array Viewer and
real array image is that some information (e.g. background and spot
morphology) is lost in Virtual Array Viewer, but Virtual Array Viewer
provides a faster and more convenient way of inspecting the quality and
browsing the contents of an array, and looking for significant spots and
information about their genes.

Some rudimentary information about the data set and the spots and their
genes is displayed just above the array image and changes as the mouse
moves over the spots (a white, squared box surrounds the current spot).
This includes the data set name and sample descriptions for the two
channels, the position of the spot under the cursor both in the
manufacturer\'s coordinate system and in a regular row/column coordinate
system, the Cy3 and Cy5 intensities of the spot and their ratio, the
GenBank accession number for the gene on the spot, and the
manufacturer\'s description of the gene on the spot. For more
information about genes on a group of spots, the array image allows the
user to mark spots and then display more in-depth gene information about
the marked spots.

[]{#__RefHeading___Toc112231382 .anchor}**Adjust Brightness of Virtual
Array Image**: The brightness of the image can be changed by adjusting
the Brightness bar ![](media/image264.png){width="2.125in"
height="0.13333333333333333in"} located at the top-left of Figure 7-148.

[]{#__RefHeading___Toc112231383 .anchor}**Zoom In and Out of Virtual
Array Image**: This is done by clicking on the + or -- sign
![](media/image265.png){width="1.0083333333333333in"
height="0.21666666666666667in"} (Figure 7-148).

![](media/image266.png){width="3.3069444444444445in"
height="3.6215277777777777in"}

Figure 7‑148: Virtual Array Viewer showing a pseudo microarray image
reconstructed from fluorescence intensities of a two-channel array. The
location of the current gene (*actb*) on the microarray slide is shown
on top of the figure.

[]{#__RefHeading___Toc112231384 .anchor}**Filter Spots**: There are two
slider controls for filtering out unwanted spots().

Fold Change slider eliminates spots whose symmetric fold change is less
than the chosen number (the precise value is displayed to the left of
the slider). Here the symmetric fold change means the maximum of i1/i2
and i2/i1, where i1 and i2 are the intensities for the two channels. For
example, positioning the slider at the value 1.3 means only spots will
be shown such that one intensity is at least 1.3 times of the other,
without regard to which channel is greater. Note: For one channel data,
the fold change slider is not adjustable as no ratio data is available
for one such array.

Intensity threshold filter can be used to eliminate spots whose
intensities do not fulfill the criteria. There are three options for the
intensity threshold filter: Either channel only displays those spots for
which at least one of the two channel intensities is greater than the
threshold value; Channel 1 and Channel 2 filter out spots for which the
intensity for the Channel 1 and Channel 2, respectively, is below the
threshold.

This is useful since spots with both intensities small might have
intensity ratios that are too influenced by noise in the data to be
useful. Note that both intensities must fall below the threshold for a
spot to be eliminated. This choice was made since a spot with one
intensity being small and the other large will have a symmetric ratio
that may not be very accurate but is in any case certain to be large and
may therefore still be worthy of consideration.

![](media/image267.png){width="3.2930555555555556in"
height="0.6888888888888889in"}

Figure 7‑149: Fold Change and Intensity slide controls for filtering out
spots for display.

[]{#__RefHeading___Toc112231385 .anchor}**Pull-Down Functions on
Spots**: There are five pull-down functions under Spots (Figure 7-150):

Mark Remaining Spots marks all spots that passed all filtering criteria
and are displayed.

Mark Flagged Spots marks all the spots that are flagged.

Clear All Marks makes all spots as unmarked.

Search for genes opens a panel for the user to paste a list of GenBank
accession numbers and mark the corresponding spots. The process is
similar to that shown in Figure 7-139 for the Scatter Plot.

Marked Spots opens another list of functions applicable to the currently
marked spots (see below on Functions Applicable to Marked Spots).

![](media/image268.png){width="3.5006944444444446in"
height="1.836111111111111in"}

Figure 7‑150: Pull-down functions for Spots and Marked Spots.

[]{#__RefHeading___Toc112231386 .anchor}**Pull-down Options**: Several
other options including flag and background handling are available
(Figure 7-151A). The color of the virtual array image can also be set in
four different ways (Figure 7-151C). By default, the Red/Green (Cy5/Cy3)
ratio is used; however, the user can choose to display either channel
intensity in a grey-scale, or to swap the default Red/Green color
assignment. The user can also choose the style of marking -- circle,
crosshair or thick circle (Figure 7-151B) and the color of marking.

![](media/image269.png){width="2.2083333333333335in"
height="0.9090277777777778in"}
![](media/image270.png){width="3.3534722222222224in"
height="1.0284722222222222in"}

> ![](media/image271.png){width="3.8722222222222222in"
> height="1.28125in"}

Figure 7‑151: Options allow the setting of spot color by channel
intensities.

[]{#__RefHeading___Toc112231387 .anchor}**Select and Deselect Current
Spot**: As the mouse cursor moves over the Virtual Array Viewer, the
current spot is covered with a white, squared box (Figure 7-148) and can
be selected/deselected by clicking on it.

*Tip*: If you want to perform any actions on the current spot, you must
select (mark) it first.

[]{#__RefHeading___Toc112231388 .anchor}**Mark Spots**: There are three
ways the user can mark spots, which can be used in combination with each
other.

First, the user can always mark a spot directly by clicking on it.

More usually, the user will mark multiple spots simultaneously by
filtering out unwanted spots first using the slider controls, and then
by either right-clicking on the Virtual Array Viewer or from the
pull-down Spots menu to Mark Remaining Spots (Figure 7-150 and Figure
7-152).

The user can also use Search for genes to input the GenBank accession
numbers and mark a set of interested genes. The interface is exactly the
same as shown in Figure 7-139 for selecting a set of genes in the
Scatter Plot. Gene marks can be cleared out by Clear Marks. And the
Virtual Array Viewer can be saved as an image file by choosing Save as
Image, just like in the Scatter Plot.

[]{#__RefHeading___Toc112231389 .anchor}**Right-click Accessible
Functions**: Right-click on the Virtual Array Viewer pops up a list of
functions (Figure 7-152) that are the same as those accessible from the
Spots pull-down menu (Figure 7-150).

[]{#__RefHeading___Toc112231390 .anchor}**Functions Applicable to Marked
Spots**: They are accessible from either right-click on the Virtual
Array Viewer or from the Spots pull-down menu.

1)  Spreadsheet Data: View gene expression data for the selected spots
    in a spreadsheet form. For details, see Chapter 8: Data Export.

2)  Bar Chart: Launch cross-dataset gene Bar Chart for selected spots
    (for a maximum of five spots). For details, see the section on Bar
    Chart in this Chapter.

3)  Mark in New Scatter Plot: Launch a new Scatter Plot and mark the
    selected spots. For details, see the section on Scatter Plot in this
    Chapter.

4)  Mark in other Viewers: Select (mark) the same set of selected spots
    in all other viewers of the same array type.

5)  Mark Duplicate Genes: If the current gene is spotted at multiple
    locations on the array, they will all be marked)

6)  Libraries: Launch library search against Gene Library, Pathway
    Library, Protein Library or Chip Library using the GenBank accession
    numbers of selected spots as queries. See Chapter 3 for details on
    library search.

![](media/image272.png){width="4.439583333333333in"
height="2.142361111111111in"}

Figure 7‑152: Right-click functions and those applicable to Marked
Spots.

## Actual Array Viewer

Actual Array Viewer (Figure 7-153) displays the original image from the
scanner and loaded into ArrayTrack from the Data Import part (Figure
2-6) of Input Form.

Note: Actual Array Viewer has not been made available to the general
user of ArrayTrack 3.1.

![](media/image273.png){width="1.1159722222222221in"
height="0.6597222222222222in"}

Figure 7‑153: Actual Array Viewer displays the original microarray
image.

Only a small part of the whole microarray image is shown in this figure.
A list of operations for color adjustment pops up after right-click on
the Actual Array Viewer.

## Bar Chart

[]{#__RefHeading___Toc112231393 .anchor}**Overview**: Bar Chart (Figure
7-154) displays expression data for a single gene across multiple arrays
within the same experiment or across different experiments. It gives the
user an overview of the differential expression levels of this gene
across different samples. Bar Chart can be invoked from the
TOOL/Visualization panel or pull-down menu, as well as from the Scatter
Plot or Virtual Array Viewer on selected/current spots (see detailed
discussions on these topics in this Chapter). When Bar Chart is invoked
from the TOOL/Visualization panel or pull-down menu, the Gene Expression
Bar Chart across Different Arrays panel pops up (Figure 7-154).

As mentioned earlier, the Bar Chart function can also be accessed from
Gene Library, Protein Library, Pathway Library, Chip Library, IPI
Library, Orthologene Library, T-test/ANOVA result table, Volcano plot,
Cluster tree, database tree and the Significant Gene List table.

[]{#__RefHeading___Toc112231394 .anchor}**Query Specification**: First,
specify the type of gene ID (gene symbol, GenBank accession number,
UniGene ID, Locus ID, and Manufacturer's gene ID). Second, enter a gene
ID to be searched. Third, select one or more experiments for which
expression data for the query gene is to be displayed. Fourth, select
the type of data to be displayed (Raw Data vs. Normalized Data). If
Normalized Data is to be displayed, further select the normalization
method. Finally, click on ![](media/image277.png){width="0.725in"
height="0.13333333333333333in"} to show the Bar Chart.

[]{#__RefHeading___Toc112231395 .anchor}**Bar Chart Display**: The X
axis displays all the arrays within the selected experiment and the Y
axis represents either ratio (for two-color platform) or intensity (for
one-channel platform).

There are two tabs above the bar chart, titled: 1) gene name@ experiment
name, 2) Standard Deviation (if the bars that representing the
hybridizations have been assigned to several groups).

![](media/image278.png){width="5.579861111111111in"
height="3.4993055555555554in"}

Figure 7‑154: Arrangement of Bar Chart panel.

[]{#__RefHeading___Toc112231397 .anchor}**Toolbars**: Under the first
tab, user can see seven tool buttons (Figure 7-155):

![](media/image279.png){width="5.995833333333334in"
height="0.8736111111111111in"}

Figure 7‑155: Toolbars for Bar Chart

Clear Color clears the current color assignment of the bars and
re-displays them in the default color of light gray.

Color Chooser pops up a color chooser dialog box (Figure 7-157) from
which the user can select a color to be applied to the following
selections of bars (see Table Display and Bar Chart Display) until it is
re-assigned. It is very useful for the user to group arrays within an
experiment by using different colors.

Save Color saves the color combinations the user chose for different
groups. Once the color info is saved, the Standard Deviation bar chart
will be drawn and each bar will be marked in the corresponding color
that is assigned for the group (see Figure 7-158). Also the records will
be marked in the corresponding colors (see Figure 7-156). The way how
the arrays are grouped also applies to all the other genes for that
specific experiment. Every time the user re-groups the bars that
representing multiple hybridizations, the Standard Deviation chart will
be re-drawn. The grouping information is visible only for the user who
did the grouping.

Scatter Plot launches Scatter Plot viewer for all the arrays currently
being selected and color-coded the current gene in red for which the Bar
Chart is displayed.

Virtual Array Viewer can be launched in which the gene is marked.

Chip Library can be launched and information about the current gene will
be displayed.

Online help is available by clicking on
![](media/image280.png){width="0.375in" height="0.14166666666666666in"}.

![](media/image281.png){width="4.500694444444444in" height="3.53125in"}

Figure 7‑156: After saving the color group each record is marked in the
corresponding color

![](media/image282.png){width="2.9715277777777778in"
height="2.595138888888889in"}

Figure 7‑157: Color Chooser for assigning the chosen color as the
current color.

If the user click the second tab titled "Standard Deviation", he will
see the standard deviation bar chart in the corresponding colors that
were chosen for different groups. Each colored bar represents the
average intensity value for the corresponding group, while the height of
the T-shaped line above the bar is the standard deviation value for the
group. The y axis is the intensity. If the user put the mouse over each
colored bar, the SD value will show up. If the user clicks a bar, the
corresponding records will be highlighted in the spreadsheet below the
bar chart.

![](media/image283.png){width="5.127777777777778in" height="1.30625in"}

Figure 7‑158: Standard Deviation chart

The standard deviation is calculated according to the following formula:

$S_{N - 1} = \sqrt{\frac{1}{N - 1}\sum_{i = 1}^{N}{(x_{i} - \overline{x})^{2}}}$

[]{#__RefHeading___Toc112231400 .anchor}**Table Display**: The same
results are displayed in a Table format below the Bar Chart. Like the
Gene Library table, this Table can be queried. If the user assigned a
color before querying, the bars corresponding to the arrays that pass
the filtering query will be highlighted. The user can repeat this
process to assign a different color to another group of arrays (e.g.
samples from the treatment group). It is helpful to group arrays using
different colors.

Alternatively, the user can first sort the table by the value of a
particular column (e.g. the SAMPLE1 column in Figure 7-159 to separate
control samples from treatment samples). Click-on and drag-down on table
rows will change the color of the corresponding bars to the currently
assigned color. In the example shown in Figure 7-159, control animals
are colored in green; whereas valproic acid treated animal samples are
colored in red. It is obvious that there is an increase of expression
for this gene (NM_007812, Cyp2a5, "cytochrome P450, family 2, subfamily
a, polypeptide 5") after valproic acid treatment.

[]{#__RefHeading___Toc112231401 .anchor}**Links between Bar Chart and
Table**: The Bar Chart and Table are linked together; clicking on a bar
will highlight the corresponding row in the Table (and switch the color
of the bar between the default color and currently assigned color). By
clicking, holding down, and moving the mouse across the Bar Chart,
multiple rows in the Table will be highlighted and the color of the
selected bars will be assigned to the current color.

Similarly, clicking on a record (row) in the Table will blink the
associated bar in the Bar Chart. Multiple rows can be selected by
clicking and dragging-down the rows, and the color of their
corresponding bars will be assigned as the currently assigned color.

![](media/image284.png){width="2.501388888888889in"
height="2.1444444444444444in"}

Figure 7‑159: Bar Chart displays gene expression data for a gene across
multiple arrays. Control samples are colored in green and chemical
treated samples are colored in red.

## Summary

As we have demonstrated in this Chapter, many visualization tools have
been made available for examining microarray data. These functions are
highly interconnected. Figure 7-160 is a screenshot of ArrayTrack in
which many connected plots and tables are displayed.

![](media/image285.png){width="3.75in" height="2.595138888888889in"}

Figure 7‑160: A screenshot showing multiple visualization
functionalities within ArrayTrack.

#  Working with Tools: DMVS

DMVS (Data mining and Visualization System) is a comprehensive software
package for the analysis of high-dimensional data including DNA
microarray gene expression data. DMVS was originally developed by
Zhenqiang Su (<zhqsu@chipscreen.com>) of Chipscreen Biosciences, Ltd.
([http://www.chipscreen.com](http://www.chipscreen.com/)). Arrangements
have been made between NCTR and Chipscreen that allow the convenient
data transfer between ArrayTrack and DMVS to achieve integration (Figure
8-161).

Under the NCTR-Chipscreen agreement, NCTR is authorized to distribute
DMVS along with ArrayTrack to not-for-profit organizations. For users
from the private sectors, please contact us for details on gaining
access to DMVS.

![](media/image286.png){width="2.5256944444444445in"
height="1.1736111111111112in"}
![](media/image287.png){width="2.9895833333333335in"
height="1.1909722222222223in"}

Figure 8‑161: Data transfer between ArrayTrack and DMVS.

DMVS Version 2.0 includes the following functions that complement with
ArrayTrack: principal component analysis (PCA), hierarchical clustering
analysis (HCA), K-means clustering, self-organizing maps (SOM), support
vector machine (SVM), partial least-squares regression (PLS), other
functions for data analysis, visualization, and chemical
structure/similarity handling (Figure 8-162).

![](media/image288.png){width="1.5in" height="1.3125in"}

Figure 8‑162: Additional TOOL functions available in ArrayTrack/DMVS.

###  

#  Data Export

## How to Access Data Export Functions

Experimental data stored in MicroarrayDB can be conveniently exported to
local data files or ArrayTrack spreadsheets for further analysis
purposes. Data export can be accessed from the Export pull-down menu
(Figure 9-163), or from right-clicking on selected arrays of the same
arrays type (Figure 9-164), where data export options are shown on the
top of the list of functions applicable to the selected arrays.

![](media/image290.png){width="5.499305555555556in"
height="1.5652777777777778in"}

Figure 9‑163: Accessing Data Export function from pull-down menu.

![](media/image291.png){width="5.999305555555556in"
height="2.9034722222222222in"}

Figure 9‑164: Accessing Data Export from right-clicking on selected
arrays.

## Options for Data Export

When exporting any data for spreadsheet view, the user will see the
Export Options dialog box (Figure 9-165) which allows the user to define
exactly what data items and formats to be exported from a variety of
combinations by checking appropriate boxes.

These options are grouped into two categories: Intensity Data Fields and
Spot Fields (i.e. annotation information about spots that is to be
exported along with intensity data columns). These options are
self-explaining and will not be discussed here. Options set here will
remain effective for Data Export until re-set.

![](media/image292.png){width="4.496527777777778in"
height="2.8430555555555554in"}

![](media/image293.png){width="4.496527777777778in" height="2.8375in"}

![](media/image294.png){width="4.176388888888889in"
height="1.7395833333333333in"}

Figure 9‑165: Setting Export Options before Data Export. A: Intensity
Data Fields options; B: Spot Fields options; C: Dataset Naming options

## Export selected datasets as spreadsheet

Data for selected arrays are to be saved as a local text file in which
each row represents a spot (gene) and each column is a particular data
item from an array. If multiple arrays are selected, additional columns
are added in the spreadsheet. (This is the so-called "wide" format, in
contrast to the "narrow" format -- see below). The user will need to
specify the folder and name where this file is to be saved. However, if
the user has not selected any arrays before trying to use this option, a
warning message will be displayed (Figure 9-166).

![](media/image295.png){width="2.732638888888889in"
height="0.8868055555555555in"}

Figure 9‑166: One or more arrays need to be selected before Export
selected datasets as spreadsheet.

## Preview selected datasets as spreadsheet

Instead of saving data as a local text file, Preview displays the data
for selected arrays in a spreadsheet within ArrayTrack. If the user has
not selected any arrays before trying to use this option, a warning
message is displayed (Figure 9-166). Figure 9-167 is a sample
spreadsheet view. Note that the column names encode all the information
needed to unambiguously identify the identity of that column.

![](media/image296.png){width="5.995138888888889in" height="1.96875in"}

Figure 9‑167: Preview of selected datasets as spreadsheet view.

Many functions are available from the Edit, View, Input/Output and Spots
pull-down menus for the Spreadsheet view table (Figure 9-168). Under the
Edit pull-down menu, the user can (1) Copy selected rows (along with
column headers); (2) Copy selected column on selected rows (along with
column headers), resulting in the copying of values from multiple table
cells from the same selected column; (3) Add new filter row allows
additional filter rows to be added on top of the table so that more
flexible filtering/querying can be conducted (for more details, see
discussion on Gene Library in Chapter 3). Alternatively, new filter rows
can be added by clicking
on![](media/image297.png){width="0.19166666666666668in" height="0.1in"}.
Furthermore, filter rows can be added or removed by clicking on the
existing filter rows and typing or deleting the filter parameter.

Under the View menu, the user can Hide selected column; Unhide column
(if any column is hidden); use Dittos Mode (") to display data items
having the same value with its previous row; Limit number of records
returned initially (current default is 10,000); Count total results
(slower queries) fetch all record at once to get the record number; and
Resize cols to fit data reset the width of individual columns so that
the columns can be displayed in a clean format, e.g. all column headers
are fully displayed.

The Hide, Unhide, and Sort order functions are also accessible by
right-click on a column header.

Under the Input/Out menu, the user can Print the current Spreadsheet,
Export to file. Under the Spots menu, the user can mark selected spots
in a pre-opened viewer.

Any ongoing query session can be stopped by clicking on
![](media/image298.png){width="0.25277777777777777in"
height="0.2638888888888889in"}.

![](media/image299.png){width="2.9680555555555554in"
height="1.4166666666666667in"} ![](media/image300.png){width="2.5in"
height="1.5208333333333333in"}\
\
![](media/image301.png){width="2.0833333333333335in"
height="1.0833333333333333in"}
![](media/image302.png){width="3.8743055555555554in"
height="0.8173611111111111in"}

Figure 9‑168: Edit, View, Input/Output and Spots pull-down menus for the
Spreadsheet view table.

## Export original files for selected datasets

Original data files (e.g. GPR files from Axon's GenePix software), CELL
file, image file(s), and setting files loaded in MicroarrayDB can be
exported in their original formats (Figure 9-163).

## Export data in a narrow format

In the narrow format, each row represents a spot (gene) with columns
corresponding to the selected fields to be exported. If multiple arrays
are selected, additional rows are added in the spreadsheet. This narrow
format has been designed for statisticians using SAS.

## Export in TIGR MeV format

Exporting in TIGR MeV format
(<http://www.tigr.org/software/tm4/mev.html>)

is also supported. MeV is TIGR (The Institute for Genomic Research)
Multi-experiment Viewer software.

## Export Gene ID matches

If the user choose multiple data set (same or different array type), he
can export the data with common genes by right-clicking the data set and
choose Export -\>Export gene id matches. See Figure 9-169.

![](media/image303.png){width="5.997222222222222in"
height="1.7180555555555554in"}

Figure 9‑169: Export data with gene ID matches

#  What to Expect from ArrayTrack 4.0

ArrayTrack is under continuous development at the Center of
Toxicoinformatics of the NCTR/FDA. ArrayTrack contains three integrated
components, [database]{.underline}, [libraries]{.underline} and
[tools]{.underline}. Retrospectively, the development of ArrayTrack is
divided into three stages:

1.       The version 1 series (ArrayTrack v1.\*) is focused on the
database component

2.       The version 2 series (ArrayTrack v2.\*) is focused on the
library component.

3.       The version 3 series (ArrayTrack v3.\*) is focused on the tool
component.

 

Although there will be many small changes along the way as ArrayTrack
development proceeds, the following majors changes are anticipated in
the forthcoming releases:

 

•     We will have several minor releases of version 3 in 2005 that
include additional analysis tools, such as SAM, F-test and so forth.

 

•    The next major release of ArrayTrack version 4 is scheduled for
next year (around April 2006); it will provide all essential
functionalities for data analysis and interpretation. In addition, a
more flexible data exchange mechanism will be available, by which users
can easily exchange data between ArrayTrack and other public microarray
data repositories (e.g., GEO, ArrayExpress, CEBS).

 

•    Currently, we only allow the FDA data to be stored in ArrayTrack
for an obvious reason, lack of resources. However, we will start to
permit some non-FDA users to deposit their data into ArrayTrack in
accordance with available resources.

 

•    For the local installation version, ArrayTrack has to be run in the
ORACLE environment. Towards the end of 2006 (version 5), we plan the
release of an ArrayTrack version that will utilize freeware databases
(e.g. Postgres or MySQL) as a backend instead of ORACLE.    

### References

1.  Tong W, Cao X, Harris S, Sun H, Fang H, Fuscoe J, Harris A, Hong H,
    Xie Q, Perkins R, Shi L and Casciano D (2003)
    ArrayTrack\--supporting toxicogenomic research at the U.S. Food and
    Drug Administration National Center for Toxicological Research.
    *Environ Health Perspect* **111**:1819-26.

2.  Tong W, Harris S, Cao X, Fang H, Shi L, Sun H, Fuscoe J, Harris A,
    Hong H, Xie Q, Perkins R, Casciano D (2004) Development of Public
    Toxicoinformatics Software for Microarray Data Management and
    Analysis. *Mutation Research* (in press)

3.  Tong W, Hong H, Fang H, Xie Q and Perkins R (2003) Decision forest:
    combining the predictions of multiple independent decision tree
    models. *J Chem Inf Comput Sci* **43**:525-31.

### ArrayTrack Team

The following people (in alphabetical order) have made direct
contributions to the development of ArrayTrack:

> Cao, Xiaoxi (Megan)
>
> Fang, Hong
>
> Harris, Stephen C.
>
> Hong, Huixiao
>
> Perkins, Roger
>
> Qian, Feng
>
> Shi, Leming
>
> Sun, Hongmei
>
> Tong, Weida
>
> Wu, Jie
>
> Xie, Qian

### Acknowledgments

Many colleagues at the FDA's National Center for Toxicological Research
have been supportive during the development of ArrayTrack. The Center
for Toxicoinformatics is particularly appreciative of the full support
of Dr. Dan Casciano, Director of NCTR/FDA and Dr. Ralph Kodell, Director
of the Division of Biometry and Risk Assessment, NCTR/FDA.

Many early users of ArrayTrack around the world have provided values
suggestions on the update of ArrayTrack. We appreciate their continuing
interests in and support of ArrayTrack.

The ArrayTrack team is looking forward to your input for the next
release. Thank you!

**Contact Us**

For further information on ArrayTrack and/or the Center of
Toxicoinformatics of the NCTR/FDA, please contact:

Weida Tong, Ph.D.

Director, Center for Toxicoinformatics

National Center for Toxicological Research

Food and Drug Administration

3900 NCTR Road

Jefferson, Arkansas 72079

U.S.A.

Tel: +1-870-543-7142, +1-870-543-7387

Fax: +1-870-543-7662

> E-mail: <NCTRArrayTrackSupport@NCTR.FDA.GOV>
>
> <Weida.Tong@fda.hhs.gov>, <Leming.Shi@fda.hhs.gov>
>
> <http://edkb.fda.gov/webstart/arraytrack/>