[]{#__RefHeading___Toc175387814 .anchor}Use Cases

[]{#__RefHeading___Toc175387815 .anchor}Adoption of Integrative Cancer
Research (ICR) Tools Project

The Jackson Laboratory

DOCUMENT REVISION HISTORY

+------------+--------------+------------------------------------------+
| Version    | Date         | Description                              |
| Number     |              |                                          |
+------------+--------------+------------------------------------------+
| 1.0        | August 20,   | -   Final version of Use Case document   |
|            | 2007         |                                          |
+------------+--------------+------------------------------------------+
| 2.0        | December 17, | -   Revised version of Use Case document |
|            | 2007         |     to clarify questions raised by Booz  |
|            |              |     Allen Hamilton                       |
+------------+--------------+------------------------------------------+
| 3.0        | January 4,   | -   Revised version of Use Case document |
|            | 2007         |     to clarify use of GenePattern,       |
|            |              |     geWorkbench and Cytoscape requested  |
|            |              |     by Booz Allen Hamilton               |
+------------+--------------+------------------------------------------+

[]{#__RefHeading___Toc175387816 .anchor}Background/Summary

The adoption of integrative cancer research tools project at The Jackson
Laboratory will provide researchers a seamless pipeline for the analysis
of microarray gene expression and array comparative genomic
hybridization data generated within their own laboratory or from other
institutions via caGrid. It will also make cancer-relevant microarray
studies available to the cancer research community on the caGrid.

Table of Content

[Use Cases 1](#__RefHeading___Toc175387814)

[Adoption of Integrative Cancer Research (ICR) Tools Project
1](#__RefHeading___Toc175387815)

[Background/Summary 1](#__RefHeading___Toc175387816)

[1 Introduction 5](#introduction)

[1.1 Overview 5](#overview)

[1.2 Related Documents 5](#related-documents)

[2 Actors and Goals 5](#actors-and-goals)

[2.1 Principle Investigator 5](#principle-investigator)

[2.2 Post-doc/Research Assistant 6](#post-docresearch-assistant)

[2.3 Gene Expression Service 6](#gene-expression-service)

[2.4 Computational Sciences (CS) Analyst
6](#computational-sciences-cs-analyst)

[2.5 External Investigator 6](#external-investigator)

[2.6 caGrid 6](#cagrid)

[2.7 Public caGrid Data 6](#public-cagrid-data)

[2.8 caArray 6](#caarray)

[2.9 Analysis Results 7](#analysis-results)

[3 Use Cases 7](#use-cases)

[3.1 Request That a Microarray Experiment Be Conducted
7](#request-that-a-microarray-experiment-be-conducted)

[3.1.1 Use Case Model 7](#use-case-model)

[3.1.2 Brief Description 7](#brief-description)

[3.1.3 Primary Actor 7](#primary-actor)

[3.1.4 Secondary Actors 8](#secondary-actors)

[3.1.5 Preconditions 8](#preconditions)

[3.1.6 Basic Flow of Events 8](#basic-flow-of-events)

[3.1.7 Postconditions 9](#postconditions)

[3.2 Submit Microarray Data To caArray
9](#submit-microarray-data-to-caarray)

[3.2.1 Use Case Model 9](#use-case-model-1)

[3.2.2 Brief Description 9](#__RefHeading___Toc175387842)

[3.2.3 Primary Actor 9](#primary-actor-1)

[3.2.4 Secondary Actor 9](#secondary-actor)

[3.2.5 Preconditions 10](#preconditions-1)

[3.2.6 Basic Flow of Events 10](#basic-flow-of-events-1)

[3.2.7 Postconditions 11](#postconditions-1)

[3.2.8 Special Requirements 12](#special-requirements)

[3.3 Publish Microarray Data to caGrid
12](#publish-microarray-data-to-cagrid)

[3.3.1 Use Case Model 12](#use-case-model-2)

[3.3.2 Brief Description 12](#brief-description-2)

[3.3.3 Primary Actor 12](#primary-actor-2)

[3.3.4 Secondary Actor 12](#secondary-actor-1)

[3.3.5 Preconditions 13](#preconditions-2)

[3.3.6 Basic Flow of Events 13](#basic-flow-of-events-2)

[3.3.7 Postconditions 13](#postconditions-2)

[3.4 Access Microarray Data From caGrid
13](#access-microarray-data-from-cagrid)

[3.4.1 Use Case Model 13](#use-case-model-3)

[3.4.2 Brief Description 14](#brief-description-3)

[3.4.3 Primary Actor 14](#primary-actor-3)

[3.4.4 Secondary Actor 14](#secondary-actor-2)

[3.4.5 Preconditions 14](#preconditions-3)

[3.4.6 Basic Flow of Events 14](#basic-flow-of-events-3)

[3.4.7 Postconditions 14](#postconditions-3)

[3.5 Analyze Microarray Gene Expression Data
15](#analyze-microarray-gene-expression-data)

[3.5.1 Use Case Model 15](#use-case-model-4)

[3.5.2 Brief Description 15](#brief-description-4)

[3.5.3 Primary Actor 15](#primary-actor-4)

[3.5.4 Secondary Actors 15](#secondary-actors-1)

[3.5.5 Preconditions 16](#preconditions-4)

[3.5.6 Basic Flow of Events 16](#basic-flow-of-events-4)

[3.5.7 Postconditions 19](#postconditions-4)

[3.6 Analyze aCGH Data 19](#analyze-acgh-data)

[3.6.1 Use Case Model 19](#use-case-model-5)

[3.6.2 Brief Description 20](#brief-description-5)

[3.6.3 Primary Actors 20](#primary-actors)

[3.6.4 Secondary Actors 20](#secondary-actors-2)

[3.6.5 Preconditions 20](#preconditions-5)

[3.6.6 Basic Flow of Events 20](#basic-flow-of-events-5)

[3.6.7 Postconditions 23](#postconditions-5)

[3.7 Correlate Genomic Aberrations and Differentially Expressed Genes
23](#correlate-genomic-aberrations-and-differentially-expressed-genes)

[3.7.1 Use Case Model 23](#use-case-model-6)

[3.7.2 Brief Description 24](#brief-description-6)

[3.7.3 Primary Actors 24](#primary-actors-1)

[3.7.4 Secondary Actors 24](#secondary-actors-3)

[3.7.5 Preconditions 24](#preconditions-6)

[3.7.6 Basic Flow of Events 24](#basic-flow-of-events-6)

[3.7.7 Postconditions 26](#postconditions-6)

[3.8 Explore Pathways Containing Genes of Interest
26](#explore-pathways-containing-genes-of-interest)

[3.8.1 Use Case Model 26](#use-case-model-7)

[3.8.2 Brief Description 26](#brief-description-7)

[3.8.3 Primary Actors 27](#primary-actors-2)

[3.8.4 Secondary Actors 27](#secondary-actors-4)

[3.8.5 Preconditions 27](#preconditions-7)

[3.8.6 Basic Flow of Events 27](#basic-flow-of-events-7)

[3.8.7 Postconditions 28](#postconditions-7)

[4 Overall Requirements 28](#overall-requirements)

[4.1 Requirements Prioritization 28](#requirements-prioritization)

[5 Data Definitions 29](#data-definitions)

[6 Sign Off 30](#sign-off)

[6.1 Approval 30](#approval)

# Introduction

## Overview

The adoption of integrative cancer research tools project at The Jackson
Laboratory (TJL) will provide researchers a seamless pipeline for the
analysis of microarray gene expression and array comparative genomic
hybridization data generated within their own laboratory or from other
institutions via caGrid. The two specific aims of this project (taken
from ICR Proposal Document) are as follows.

1.  Adopt the grid-enabled caArray microarray data repository and
    management tool along with several analysis and visualization tools
    to provide TJL researchers with a comprehensive microarray analysis
    pipeline. An internal end-user training program will also be
    established.

2.  Make cancer-relevant microarray studies available to the cancer
    research community on the caGrid by integrating TJL's internal
    Microarray Database system with caArray.

The microarray data analysis pipeline is a set of analysis tools that
address specific work flows and feature a number of caBIG tools
including Bioconductor and GenePattern.

## Related Documents

ICR Proposal Document - caBIG-ICR_Proposal_sansbudget.pdf (in proposal
subdirectory in Computational Sciences Work Management System project
folder #1306)

# Actors and Goals

## Principle Investigator

A Principle Investigator is a user who directs and conducts research
that utilizes microarray technology. The goal of the Principle
Investigator is to analyze microarray data to obtain a better
understanding of the biological mechanisms relevant to cancer.

## Post-doc/Research Assistant

A Post-doctoral Fellow or Research Assistant is directed by a Principle
Investigator to conduct research that utilizes microarray technology.
The goal of the Post-doc/Research Assistant is to assist in the overall
research program.

## Gene Expression Service

The Gene Expression Service is directed by a Principle Investigator to
conduct a microarray experiment. The goal of the Gene Expression Service
is to generate and store raw microarray data that can be analyzed.

## Computational Sciences (CS) Analyst

The Computational Sciences (CS) Analyst is directed by a Principle
Investigator to conduct analyses of microarray data. The goal of the CS
Analyst is to provide rigorous analytical services to the Principle
Investigator.

## External Investigator

An External Investigator is a user who conducts research and wants to
use microarray data generated by another investigator. The goal of the
External Investigator is to analyze microarray data to better understand
the biological mechanisms relevant to cancer.

## caGrid

The caGrid is a network of data repositories that NCI Cancer Centers
manage. The goal of the caGrid is to enable sharing of cancer relevant
data across research centers.

## Public caGrid Data

Public caGrid data are microarray data made available to any researcher
that uses the caGrid. These data completely represent the experimental
design and all raw data from the experiment so that it can be analyzed
elsewhere.

## caArray

The caArray system is an internal repository of microarray data. The
goal of the caArray is to encapsulate all microarray data for an
experiment including sample and experimental design information.

## Analysis Results

Analysis Results are products of various microarray data analyses. These
results summarize biological findings from the microarray experiment.
For example, the results could be a list of probes that represent genes
that are significantly differentially expressed in a microarray
experiment.

# Use Cases

## Request That a Microarray Experiment Be Conducted

### Use Case Model

![](media/image2.png){width="5.364583333333333in" height="2.5in"}

**Figure 1**: Use case for requesting that a microarray experiment be
conducted.

### Brief Description

A user (e.g., principle investigator) completes a web-based form to
request a microarray gene expression or CGH experiment. The system
notifies the Gene Expression Service of the request.

### Primary Actor

A principle investigator, or another researcher (post-doc/research
assistant) who acts on behalf of a principle investigator, who wishes to
initiate a microarray experiment is the primary actor.

### Secondary Actors 

The manager of the Gene Expression Service acts as a secondary actor as
he/she receive the request generated by the investigator.

### Preconditions

#### Minimum Information Regarding Experiment 

The Principle Investigator or Post-doc/Research Assistant must know the
following about the experiment they wish to conduct. Additional
information regarding the experimental design are done in a meeting
between the investigator, Gene Expression Service and a biostatistician
from Computational Sciences.

1.  Contact information (name, email, phone)

2.  Lab affiliation (name of Principle Investigator)

3.  Grant or account number to charge for materials and labor

4.  Grant agency and type

5.  Type of microarray project (one of the following):

    a.  Affymetrix gene expression

    b.  Illumina gene expression

    c.  CGH

6.  Research area (one or more terms from a controlled vocabulary)

7.  Disease area (one or more terms from a controlled vocabulary)

### Basic Flow of Events

The Principle Investigator or Post-doc/Research Assistant will enter
information required to initiate a request as shown in Figure 2. That
request is sent to the Gene Expression Service where they coordinate and
eventually conduct the experiment.

### ![](media/image3.png){width="6.072916666666667in" height="0.3645833333333333in"} {#section .unnumbered}

**Figure 2**: Flow diagram for Requesting That A Microarray Experiment
Be Conducted

### Postconditions

#### Experiment Request Submitted

As long as the experiment request form has been filled out properly, the
Gene Expression Service is notified and will arrange a meeting with the
investigator to design the experiment along with a biostatistician from
Computational Sciences.

## Submit Microarray Data To caArray

### Use Case Model

![](media/image4.png){width="3.5729166666666665in"
height="1.0729166666666667in"}

**Figure 3**: Use case for submitting microarray data to caArray.

### Brief Description

The Gene Expression Service will enter information about each microarray
experiment and load the raw data into caArray. Once a meeting has been
held with the investigator, Gene Expression Service and a
biostatistician from Computational Sciences, details about the samples
and experimental design are entered into the system. Following the
completion of the experiment, the raw data is submitted into the system.

### Primary Actor

The Gene Expression Service is the Primary Actor as the group manages
and conducts the experiment and has expert knowledge in how to code
information about the experiment into the system.

### Secondary Actor

The caArray system is the Secondary Actor as it is the repository for
microarray data.

### Preconditions

#### Experimental Design Defined

A meeting will have been held by the Gene Expression Service,
investigator and a biostatistician from Computational Sciences to define
details of the project, design of the experiment and information about
the samples.

### Basic Flow of Events

Once a meeting has been held with the investigator, Gene Expression
Service and a biostatistician from Computational Sciences, details about
the samples and experimental design are entered into the system.
Following the completion of the experiment, the raw data is submitted
into the system. These steps are shown in top portion of Figure 4. Once
the experimental portion of the project is complete (e.g, microarrays
scanned and basic data quality checked by the Gene Expression Service),
the system exports data into MAGE-TAB format. The MAGE-TAB formatted
data is automatically converted to MAGE-ML format by a script that
periodically checks for the presence of MAGE-TAB files in a directory.
The caAMEL API is used to validate the format of the MAGE-ML file, load
the data to caArray, and determine whether the load was successful or
not. The user is notified if the MAGE-ML file is not properly formatted
or that the data has been successfully or unsuccessfully loaded into
caArray.

### ![](media/image5.png){width="6.495833333333334in" height="6.7444444444444445in"} {#section-1 .unnumbered}

**Figure 4**: Flow diagram for submitting microarray data to caArray.

### Postconditions

#### Experimental Data Loaded Into caArray

All data for the experiment, including sample information, experimental
design and raw data, are stored in caArray.

### Special Requirements

#### Automatic Loading of MAGE-TAB Files to caArray

Once any MAGE-TAB formatted files generated by the system, they are
automatically loaded into caArray. The permissions for these projects
are initially set to limit access to internal investigators only.

## Publish Microarray Data to caGrid

### Use Case Model

![](media/image6.png){width="3.4270833333333335in"
height="1.0729166666666667in"}

**Figure 5**: Use case for publishing microarray data to caGrid.

### Brief Description

The Principle Investigator can make their microarray data publicly
available on the caGrid. Once the data is made available, external
investigators can access it (see Section 3.4).

### Primary Actor

The Principle Investigator is the Primary Actor as they initiated the
experiment and have sole control on who can access it.

### Secondary Actor

The caGrid is the Secondary Actor as the data is made available to any
investigator who uses caGrid.

### Preconditions

#### Experiment Must Be Complete

An experiment can only be published to caGrid after it is complete and
contains data.

### Basic Flow of Events

The Principle Investigator for the experiment selects it using the
system and then changes access permissions to public as shown in Figure
6.

### ![](media/image7.png){width="4.947916666666667in" height="0.3645833333333333in"} {#section-2 .unnumbered}

**Figure 6**: Flow diagram for publishing microarray data to caGrid.

### Postconditions

#### Selected Experiment is Accessible to Any User on caGrid

After the access permissions are changed to public for the selected
experiment, any user on caGrid can access it.

## Access Microarray Data From caGrid

### Use Case Model

![](media/image8.png){width="5.0in" height="2.5in"}

**Figure 7**: Use case for accessing microarray data from caGrid.

### Brief Description

Any investigator, either within The Jackson Laboratory or at another
institution, can obtain microarray data using caGrid and apply it to
their own research.

### Primary Actor

A Principle Investigator or an external investigator is the Primary
Actor.

### Secondary Actor

The public caGrid data that will be downloaded by the user is the
Secondary Actor.

### Preconditions

#### Publicly Available Microarray Experiments

At least one microarray experiment on caGrid needs to be publicly
available.

### Basic Flow of Events

A Principle Investigator or an external investigator can query caGrid to
find microarray experiments of interest and then download those data as
shown in Figure 8.

### ![](media/image9.png){width="6.5in" height="1.15in"} {#section-3 .unnumbered}

**Figure 8**: Flow diagram for accessing and downloading data from
caGrid.

### Postconditions

#### Microarray Data Downloaded

Once the experiment is selected the relevant data files are downloaded
to the user's workstation.

## Analyze Microarray Gene Expression Data 

### Use Case Model

![](media/image10.png){width="4.947916666666667in" height="2.5in"}

**Figure 9**: Use case for analyzing microarray gene expression data.

### Brief Description

A Principle Investigator or an analyst from Computational Sciences can
analyze microarray gene expression data using a flexible analysis
pipeline. The output is a set of output files that can be read in by
other analytical tools for additional analysis.

### Primary Actor

A Principle Investigator, or Computational Sciences Analyst who acts on
behalf of a principle investigator, who wishes to analyze a microarray
gene expression experiment is the primary actor.

### Secondary Actors 

The analysis results generated by the gene expression analysis pipeline
is the Secondary Actor.

### Preconditions

#### Experiment To Analyze 

Knowledge of which experiment to analyze must be known. The experiment
can be one that was conducted within The Jackson Laboratory or an
experiment made publicly available on caGrid.

### Basic Flow of Events

The analysis of microarray gene expression data is a complex process and
varies depending on the design of the experiment conducted. As shown in
Figure 10, there are two main branches in the analysis pipeline: one for
designs with replication and another for no replication. The data from
arrays without replication are limited to fold change analysis as
additional statistics cannot be computed. Examples of non-replicated
experiments include Dr. Mills' mouse tumor samples that are compared to
a control. For experiments with replication, the analysis can be complex
as multiple ANOVA models can be applied depending on the experimental
design. For each model, each factor can be tested and various linear
contrasts for each factor can be compared in a contrast.

Regardless of design, the process begins by selecting the experiment to
analyze from within caArray. Then, the raw data are transformed (using
log base 2) and then normalized in a platform specific fashion. For
Affymetrix GeneChip data, BioConductor's RMA procedure (from R/affy
package) is used to derive a quantiles normalized measure for each probe
set. For Illumina BeadChip data, the transformed bead summary level data
for each probe are quantiles normalized using the R/limma package. Next,
several QC plots to scan for aberrant arrays that may require further
investigation by the Gene Expression Service and may be excluded in the
worst case. The last common step is to create a data file that
completely specifies the experimental design that is required by
BioConductor's R/maanova package.

For replicated designs, R/maanova is used to read in the transformed and
normalized data along with the experimental design file. The user then
selects the ANOVA model to use by specifying the factors, interactions
and main effects. That model is then fit using R/maanova. Next, a single
factor or interaction in the ANOVA model is selected by the user and
then tested using a permutation F test. This is a computationally
expensive step as 1,000 permutations are typically run for this test.
Following the test, a pairwise contrast is done on a test and reference
level for the factor that the user defines. Fold change is computed for
each probe (Illumina) or probe set (Affymetrix) for the contrast.
Several output files are generated next. One contains the fold change, F
statistic, p-value derived from a permutation test, and q-value (from
R/qvalue package) for each probe or probe set. Another are the "top
hits" from the experiment that include only those probes or probe sets
with a q-value less that 0.05. Both files contain annotation about each
probe or probe set. A volcano plot, as shown in Figure 11, is also
generated that summarizes the relationship between fold change and
q-value for all probes or probe sets. Once the single contrast for a
single factor in a single model has been completed, the user may
optionally:

1.  Conduct another contrast for a different pair of levels for the same
    factor in the same model

2.  Test a different factor and perform a contrast for a pair of levels

3.  Change the ANOVA model, test a factor in the new model and perform a
    contrast on a pair or levels.

For non-replicated designs, R/maanova is used to read in the transformed
and normalized data along with the experimental design file. The pair of
samples are selected for the fold change calculation (one is the test
sample and the other the reference). The fold change is calculated,
results annotated and written out to an output file. The output file
contains a fold change for each probe or probe set on the array. A "top
hits" file is also generated that contains a summary for probes or probe
sets with a fold change greater than +2 or less than -2. Once these
files are generated for a specific pair of samples, the user may
optionally select another pair of samples to compare.

The GenePattern package will be used to implement the gene expression
analysis pipeline by executing a series of R scripts that, in turn,
utilize R/maanova. GenePattern has a web interface where the user can
specify input parameters and run their analyses on a remote server. When
the analysis is complete, output files can be downloaded onto the user's
desktop.

![](media/image11.png){width="6.492361111111111in"
height="7.895138888888889in"}

**Figure 10**: Flow diagram for analyzing microarray gene expression
data.

![](media/image12.jpeg){width="5.0in" height="5.0in"}

**Figure 11**: Example volcano plot.

### Postconditions

#### Microarray Gene Expression Analysis Output Files

A set of analysis output files are generated by the system that
summarizes the results for each probe or probe set with respect to
specific analysis (see section 3.5.6).

## Analyze aCGH Data 

### Use Case Model

![](media/image13.png){width="4.947916666666667in" height="2.5in"}

**Figure 12**: Use case for analyzing array CGH data.

### Brief Description

A Principle Investigator or an analyst from Computational Sciences can
analyze an array CGH (Comparative Genomic Hybridization) data using an
analysis pipeline. The output is a set of output files that can be read
in by other analytical tools for additional analysis.

### Primary Actors 

A Principle Investigator, or Computational Sciences Analyst who acts on
behalf of a principle investigator, who wishes to analyze an array CGH
experiment is the primary actor.

### Secondary Actors 

The analysis results generated by the aCGH analysis pipeline is the
Secondary Actor.

### Preconditions

#### Experiment To Analyze 

Knowledge of which experiment to analyze must be known. The experiment
can be one that was conducted within The Jackson Laboratory or an
experiment made publicly available on caGrid.

### Basic Flow of Events

The analysis of aCGH gene expression data is another complex process and
the analysis pipeline shown in Figure 13 supports the experimental
designs used by Drs. Mills and Yun to date. The two-color spotted array
data used by Drs. Mills and Yun feature dye-swap technical replication.
The analysis results in a profile of log ratios for a tumor sample
versus a control sample for probes distributed across the genome. Large
positive or negative log ratios indicate the location of potential
chromosomal aberrations, either amplifications or deletions,
respectively.

Bioconductor's R/maanova is used to read in the raw data from caArray
along with the experimental design file. The data is then transformed
using log base 2 and further transformed using rlowess. A linear model
(ANOVA) is used to simultaneously normalize and compute the log base 2
ratio between a test and reference sample for each probe. To date, Drs.
Mills and Yun's experiments have had a single factor that is the tumor
type with levels that identify each tumor or indicate that the sample is
a reference. Since these as dye-swapped experiments, the linear model
can also be used to test for the significant of the log base 2 ratio.
For array platforms that have probes that map to the same genomic
location (as in the MEEBO arrays from Microarrays, Inc. that Dr. Mills
users), values from these probes are averaged and a single value is used
for that genomic location. Next, a quantile smoothing procedure is
applied on the log ratios. Results are then annotated and results
summarized in output files. One output file reports the log ratio for
each probe (or collapsed probe) with the various annotation fields.
Another output file is a plot of the smoothed log ratio values across
the entire genome as shown in Figure 14. Once the pair of samples has
been analyzed in the above fashion, the user may select another pair of
samples to analyze.

The GenePattern package will be used to implement the aCGH analysis
pipeline by executing a series of R scripts that, in turn, utilize
R/maanova. GenePattern has a web interface where the user can specify
input parameters and run their analyses on a remote server. When the
analysis is complete, output files can be downloaded onto the user's
desktop.

![](media/image14.png){width="4.301388888888889in"
height="8.45486111111111in"}

**Figure 13**: Flow diagram for analyzing aCGH data.

![](media/image15.wmf){width="6.499305555555556in"
height="1.6770833333333333in"}

**Figure 14**: Example whole genome plot of log ratios for a tumor
sample versus a reference from aCGH analysis.

### Postconditions

#### Microarray aCGH Analysis Output Files

A set of analysis output files are generated by the system that
summarizes the results for each probe (or collapsed probe) with respect
to specific analysis (see section 3.6.6).

## Correlate Genomic Aberrations and Differentially Expressed Genes 

### Use Case Model

![](media/image16.png){width="4.947916666666667in" height="2.5in"}

**Figure 15**: Use case for correlating genomic aberrations and
differentially expressed genes.

### Brief Description

A Principle Investigator or an analyst from Computational Sciences can
analyze the results of analyses from an array CGH (Comparative Genomic
Hybridization) experiment and gene expression experiment to obtain a
list of genes that have both a genomic aberration and are differentially
expressed. The output is a set of output files that can be read in by
other analytical tools for additional anlaysis.

### Primary Actors 

A Principle Investigator, or Computational Sciences Analyst who acts on
behalf of a principle investigator, who wishes to correlate results from
aCGH and gene expression analyses is the primary actor.

### Secondary Actors 

The analysis results generated by the correlation analysis is the
Secondary Actor.

### Preconditions

#### Experiment To Analyze

Knowledge of which experiments to analyze must be known. The experiments
can be ones that were conducted within The Jackson Laboratory, an
experiment made publicly available on caGrid, or some combination
thereof.

#### aCGH and Gene Expression Results Annotated By Genomic Position

The aCGH and gene expression results need to be annotated by genomic
position in order to conduct the correlation analysis.

### Basic Flow of Events

The analysis of aCGH gene expression data provides a list of genomic
aberrations in a tumor sample versus a reference and these can be
correlated with the position of genes that are significantly
differentially expressed from a microarray gene expression experiment.
As shown in Figure 16, the process begins by reading in analysis results
from the aCGH and gene expression experiments. Then, the genomic
location (chromosome and base pair position) of the probes (or probe
sets) from each analysis are compared as follows. If the location of a
probe that defines an aberration (from aCGH) is within a user-defined
distance of a probe that is significantly differentially expressed, then
the pair of results is reported. By default, the distance is 50,000 bp.
Once all pairs are identified within the data, a plot of the log ratio
from aCGH and fold change from the gene expression experiment are
plotted across the entire genome as separate plots as shown in Figure
17.

### ![](media/image17.png){width="4.083333333333333in" height="3.7708333333333335in"} {#section-4 .unnumbered}

**Figure 16**: Flow diagram for correlating analysis results from aCGH
and gene expression data.

![](media/image18.wmf){width="6.492361111111111in"
height="3.8069444444444445in"}

**Figure 17**: Example whole genome plot from correlating analysis
results from aCGH and gene expression data.

### Postconditions

#### Correlation Analysis Output Files

A set of analysis output files are generated by the system that
summarizes the results for each probe with respect to specific analysis
(see section 3.7.6).

## Explore Pathways Containing Genes of Interest

### Use Case Model

![](media/image19.png){width="4.947916666666667in" height="2.5in"}

**Figure 18**: Use case for analyzing pathways for genes of interest.

### Brief Description

A Principle Investigator or an analyst from Computational Sciences can
analyze pathways represented by genes of interest identified by an array
CGH (Comparative Genomic Hybridization) experiment, gene expression
experiment, correlation analysis of results from a combination of
experiments, or other means. The output is a set of output files that
can be read in by other analytical tools for additional analysis.

### Primary Actors 

A Principle Investigator, or Computational Sciences Analyst who acts on
behalf of a principle investigator, who wishes to analyze pathways for
genes of interest is the primary actor.

### Secondary Actors 

The analysis results generated by the pathways analysis is the Secondary
Actor.

### Preconditions

#### List of Genes To Analyze

A list of genes to analyze must be known. These genes must have a widely
utilized identifier, such as a Entrez Gene Identifier (from the NCBI
Entrez Gene Database). Analysis output from gene expression, aCGH, or
correlation analysis defined in the previous sections contain such
identifiers.

#### Pathway Resources of Interest

The pathway resources (e.g., KEGG) of interest need to be available in
the cPath resource.

### Basic Flow of Events

The analysis of pathways begins by selecting the list of genes to
analyze. Most often, this will be an output file generated by a gene
expression or aCGH analysis as shown in Figure 19. Alternatively, it
could be a list of genes gathered by another resource, such as BioMart
at Ensembl. Each gene needs to have a widely utilized identifier, such
as a Entrez Gene identifier. Once the list is imported, the system will
map those genes onto pathway resources (e.g., KEGG) and allow the user
to view pathway maps containing at least of the genes of interest. These
pathway maps can be exported as image files.

The geWorkbench package will be used to upload a set of microarray data
and analyze the pathways represented by a subset of probes that are of
particular interest. Cytoscape will be used to view pathways that have
one or more genes targeted by the probes of interest. Cytoscape can be
run from within geWorkbench or independently. In the case of a list of
genes of interest (from a resource such as BioMart at Ensembl), the
stand-alone version of Cytoscape will be used. From within Cytoscape,
pathways from resources such as KEGG can be visualized. A pathway that
has several genes of interest may help the investigator generate new
hypotheses to test. Due to ongoing development with geWorkbench and
Cytoscape, the specific pathway and/or protein interaction resources
available may vary.

### ![](media/image20.png){width="4.65625in" height="5.166666666666667in"} {#section-5 .unnumbered}

**Figure 19**: Flow diagram for analyzing a list of genes from either a
microarray experiment or any list of genes of interest.

### Postconditions

#### Pathway Analysis Output Files

A set of analysis output files are generated by the system that
represents the pathways of interest (see section 3.8.6).

# Overall Requirements

## Requirements Prioritization

  ------------ ----------------------------------------------------------
  Priority     Use Case Name

  1            Request That A Microarray Experiment Be Conducted

  1            Submit Microarray Data to caArray

  1            Publish Microarray Data to caGrid

  2            Access Microarray Data from caGrid

  1            Analyze Microarray Gene Expression Data

  1            Analyze aCGH Data

  1            Correlate Genomic Aberrations and Differentially Expressed
               Genes

  2            Explore Pathways Containing Genes of Interest
  ------------ ----------------------------------------------------------

The first priority use cases are those that are most important to
investigators. However, all use cases are important to them.

# Data Definitions

Microarray Data -- All experiment information, experimental design,
sample information, and raw data. It is all information required to
analyze the data.

Microarray Analysis Results -- A set of output files that summaries
results on a probe or probe set basis. The results for gene expression
analyses include fold change and various statistics. The results for
aCGH analyses include log ratios. For each probe, various annotation
fields are provided such as the location the probe aligns to in the
relevant genome assembly.

# Sign Off

## Approval

*Workspace General Contractor Rep Print Name Date*

*Architecture Workspace Rep Print Name Date*

*VCDE Workspace Rep Print Name Date*

*Workspace Working Group Rep Print Name Date*

*NCICB Rep Print Name Date*