# Presentation: 553794

## geWorkbenchHands-On Training

- Session Date:
- Session Length:
- Target Audience:
- Trainer:
- Developer Subject Matter Expert	:

**Notes:**
Hands –on Training is designed to show users how to use the system to accomplish specified tasks
There should be emphasis on step by step instructions and hands on experience by the participants
It should also include examples, case scenarios and demonstrations followed by knowledge checks, exercises, etc. 

Session Length: How Long will the session last
Target Audience:  Who should be attend training? 
Trainer:  Who is delivering the training and an appropriate email address
Subject Matter Expert: Optional – but good for participants to know at the very least the developer of the application for reference and an appropriate email address

## geWorkbench

- geWorkbench is being developed at the Joint Centers for Systems Biology, Columbia University
- This work is supported by the NCI caBIG and the NIH NCBC programs.

**Notes:**
Hands –on Training is designed to show users how to use the system to accomplish specified tasks
There should be emphasis on step by step instructions and hands on experience by the participants
It should also include examples, case scenarios and demonstrations followed by knowledge checks, exercises, etc. 

Session Length: How Long will the session last
Target Audience:  Who should be attend training? 
Trainer:  Who is delivering the training and an appropriate email address
Subject Matter Expert: Optional – but good for participants to know at the very least the developer of the application for reference and an appropriate email address

## This training is designed for a user who is new to  
    geWorkbench.

Target Audience: Researchers and students interested in microarray gene expression experiment analysis.

The attendee is expected to have basic computer and biological knowledge.

Note – this is not a complete introduction to all geWorkbench components.  Its primary goal is to describe those features specified in the geWorkbench Year 1 SOW, and the context in which they can be used.

-     geWorkbench.
- Target Audience: Researchers and students interested in microarray gene expression experiment analysis.
- The attendee is expected to have basic computer and biological knowledge.
- Note – this is not a complete introduction to all geWorkbench components.  Its primary goal is to describe those features specified in the geWorkbench Year 1 SOW, and the context in which they can be used.

- Session Details:

## Session Details:Overview of the Training Environment

-  Classroom Training
-  Centra – Online Classroom
-  Web-based Delivery

- Session Details:**Overview of the Training Environment**

## Session Details:Hardware and Software

-  geWorkbench requires the Sun Java JRE 1.5 environment to be installed on your local machine.
-  geWorkbench requires significant memory.  At least 1 GB is recommended, especially if larger datasets are being read in or hierarchical clustering will be done.
-  Windows, Linux and Mac/PowerPC version of geWorkbench are available.
- See _[www.geworkbench.org](http://www.geworkbench.org/)_ for full details.

- Session Details:**Hardware and Software**

## Session Details:Session Goals

- Have a basic understanding of the purpose and aims of geWorkbench.
- Be able to set program preferences and load microarray data from local and remote sources.
- Understand how data files are organized into Projects, and how subsets of data can be formed and used.
- Use filtering and normalization components to prepare data.
-  Analyze and view data using a number of new components.

- Session Details:**Session Goals**

## Session Details:Outline of lessons

- Tutorial Data
- Part 1 – Data management
- Lesson 1: Basics of the graphical interface
- Lesson 2: Setting Preferences
- Lesson 3: Projects and Data Files
- Lesson 4: Working with Data Subsets
- Lesson 5: Working with Remote Sources
- Part 2 – Data manipulation
- Lesson 6: Normalization
- Lesson 7: Filtering
- Lesson 8: Experiment Annotations
- Part 3 – Analysis and display
- Lesson 9: The Scatter Plot component
- Lesson 10: Expression Value Distribution
- Lesson 11: Reverse Engineering
- Lesson 12: Gene Annotation and Pathway Viewing

- Session Details:**Outline of lessons**

## Introduction

- Introduction

## Introduction:Overview

- This section will describe in general the capabilities of geWorkbench in the following areas:
- Microarray analysis.
- Sequence analysis.
- Access to remote data and services
- A complete description of geWorkbench and online tutorials are available at www.geworkbench.org.

- Introduction:**Overview**

## geWorkbench – a platform for tool and data integration
geWorkbench is an open-source bioinformatics platform that provides an extensive collection of tools for the management, analysis, visualization and annotation of biomedical data. 
geWorkbench has been designed with a plug-in framework.  As new techniques are developed and implemented, they can be added to geWorkbench.

geWorkbench aims to allow different tools to easily work together, such as using microarray analysis to obtain a list of interesting genes, and then retrieving their coding or upstream sequences and using these in BLAST, pattern discovery, or transcription factor binding motif searches.

- geWorkbench is an open-source bioinformatics platform that provides an extensive collection of tools for the management, analysis, visualization and annotation of biomedical data.
- geWorkbench has been designed with a plug-in framework.  As new techniques are developed and implemented, they can be added to geWorkbench.
- geWorkbench aims to allow different tools to easily work together, such as using microarray analysis to obtain a list of interesting genes, and then retrieving their coding or upstream sequences and using these in BLAST, pattern discovery, or transcription factor binding motif searches.

- Introduction:**Overview**

## 
Obtaining data from local or remote data sources 
Filtering and normalization
Basic statistical analysis
Clustering (Hierarchical, SOM)
Gene Ontology analysis
Reverse Engineering
Visualization using many common tools
Scatter Plot
Volcano Plot
Expression Profiles
Expression Value Distribution
Color Mosaic
Dendrogram

- Obtaining data from local or remote data sources
- Filtering and normalization
- Basic statistical analysis
- Clustering (Hierarchical, SOM)
- Gene Ontology analysis
- Reverse Engineering
- Visualization using many common tools
- Scatter Plot
- Volcano Plot
- Expression Profiles
- Expression Value Distribution
- Color Mosaic
- Dendrogram

**geWorkbench supports many kinds of operations on microarray data:**

- Introduction:**Microarray data**

##  BLAST

 Pattern Discovery

 Transcription Factor Mapping

 Syntenic Region Analysis

-  Pattern Discovery
-  Transcription Factor Mapping
-  Syntenic Region Analysis

**geWorkbench also provides capabilities for  working with sequence data**:

- Introduction:**Sequence data**

## Introduction:External data services

-  External sources provide expression data, sequences and annotation:
- Microarray gene expression repositories (caArray)
- Gene annotation web pages (viaCGAP)
- DNA Sequence retrieval (UC Santa Cruz)
- Pathway diagrams (BioCarta via caBIO database at NCI)

- Introduction:**External data services**

## Introduction:External computational services

- BLAST – search for sequences similar to a query sequence.
- Access is provided both to a Columbia server and the NCBI BLAST service.
- Pattern Discovery – find repeated patterns in a group of sequences.
- Synteny – compare regions of one chromosome against another.
- Through the caGRID project, additional remote services are being added:
- Hierarchical clustering – tree-like grouping by expression similarity.
- SOM (Self-Organizing Maps) – divide expression profiles into a limited number of bins.
- ARACNE – regulatory network reverse engineering.

- Introduction:**External computational services**

## Tutorial Data

- Tutorial Data

## Tutorial Data:Overview

- The data can be downloaded from http://wiki.c2b2.columbia.edu/workbench/index.php/Download
- There are several file types
- Microarray
- Affymetrix MAS5/GCOS format files – a single file per array, as produced by Affymetrix software.
- The geWorkbench data matrix format, which merges all expression data from a set of experiments into a single file.  By default it uses the ending “.exp”.
- Genepix two-color array experiments (in base download).
- Sequence
- DNA and protein sequence files in FASTA format.

- Tutorial Data:**Overview**

## Tutorial Data:Data files

**(http://wiki.c2b2.columbia.edu/workbench/index.php/Download****)****.**
- The file "tutorial_data.zip" contains the following files:
**cardiogenomics.med.harvard.edu/**  Contains 10 individual MAS5/GCOS format
- data files.
**webmatrix_quantile_log2_dev1.2_mv0.exp**  A geWorkbench "exp" format matrix file containing filtered, normalized data. This data originally derives from the file "webmatrix2.exp".
**NM_024426-Wilms.fasta**   A Genbank nucleotide seqeuence file.
**NP_077744-Wilms.fasta**   A Genbank protein seqeuence file.
**H1H5_HistoneDB_NHGRI.fasta****  **Contains H1 and H5 histone sequences from the NHGRI.
**cluster_tree_total_pearsons_84_markers.csv**  Contains a list of genes derived from hierarchical clustering.
**64of84ClusterPearsonsSeqs.fasta**  Contains upstream DNA sequences derived from a subset of the above genes.

- Tutorial Data:**Data files**

## Tutorial Data:About the Cardiogenomics Microarray Dataset

- Harvard University:
-  _[http://cardiogenomics.med.harvard.edu/project-detail?project_id=229](http://cardiogenomics.med.harvard.edu/project-detail?project_id=229)__ _
- A number of MAS5 format data files are available there. The specific project is the
- "Belgium Dataset of Aortic Stenosis, Congestive Cardiomyopathy and Normal LV
- Function", and the data is downloadable from:
- _[http://cardiogenomics.med.harvard.edu/groups/proj1/pages/download_Hs-belgium.html](http://cardiogenomics.med.harvard.edu/groups/proj1/pages/download_Hs-belgium.html)__ _
- An abstract describing the study is also available, at:
- _[http://cardiogenomics.med.harvard.edu/groups/proj2/pages/Hs-belgium_home.html](http://cardiogenomics.med.harvard.edu/groups/proj2/pages/Hs-belgium_home.html)__ _

- Tutorial Data:**About the Cardiogenomics Microarray Dataset**

## Tutorial Data:Generation of example microarray dataset

- The file "webmatrix2.exp", available in the Download area, contains results
- from 100 Affymetrix HG-U95Av2 chips containing B-cell samples from
- numerous different disease states. 12,600 probes are
- represented.
- For use in these tutorials we normalized and filtered the data.
- The steps on the next page are just an example of how filtering and normalization can be used, and each dataset should be handled according to the type of analysis being undertaken and its goals.

- Tutorial Data:**Generation of example microarray dataset**

## Tutorial Data:Generation of example microarray dataset

- Normalization: Quantile normalization.
- Normalization: Log2 transformation.
- Filtering: Deviation filter with Deviation bound of 1.2.
- Filtering: Missing values filter with maximum number of missing arrays of 0.
- 	The result of performing these steps is available as the file "webmatrix_quantile_log2_dev1.2_mv0.exp", found in the tutorial data file "tutorial_data.zip”.

- Tutorial Data:**Generation of example microarray dataset**

## Part 1: Data Management

- Part 1: Data Management

- Data Management

## Part 1: Data ManagementObjectives

- Part 1: Data ManagementObjectives

- The objective of Part 1 is to learn the basic operation of geWorkbench.  This includes understanding the layout of the graphical interface in four main functional regions, and setting user preferences.  The loading of local and remote data files will be demonstrated. Perhaps of most importance is understanding how geWorkbench allows data to be divided into subsets, both for setting up analyses and utilizing their results.
- After completing Part 1, you should be able to:
- Load microarray data into geWorkbench from local and remote sources, and set display preferences.
- Understand how the data can be organized into projects and manipulated using sets.

## Part 1: Data ManagementLesson outline

- Lesson 1: Basics of the graphical interface
- Lesson 2: Setting Preferences
- Lesson 3: Projects and Data Files
- Lesson 4: Working with Data Subsets
- Lesson 5: Working with Remote Sources

- Part 1: Data ManagementLesson outline

## Lesson 1: Basics of the graphical interface:

- Basics of the graphical interface.

- Lesson 1: Basics of the graphical interface:

## Lesson 1: Basics of the graphical interfaceThe four areas of the GUI

- Data management  Workspace and Projects (upper left).
- Marker and Array/Phenotype set selection and management (lower left).
- 3.    Visualization tools (upper right).
- 4.    Analytical tools (lower right).

- Areas 2, 3 and 4 are defined for convenience. The actual placement of a
- given component into any of these three areas is controlled by a configuration
- file and can be customized as desired.** **

- Lesson 1: Basics of the graphical interface**The four areas of the GUI**

## Lesson 1: Basics of the graphical interfaceMenu bar and data management area

- The GUI provides  a menu bar at top with a standard choice of commands.
- Many commands that are available in the menu bar are also available by right-clicking on data objects.

**Data management area (area 1)**

- Working with geWorkbench involves creating a project within the top-level **Workspace**.
- Opened data files and the results of analysis are stored within a **Project**.
- Multiple projects can be used within a workspace to organize data.
- A workspace and all the projects and data within it can be saved and later reloaded.

- Lesson 1: Basics of the graphical interface**Menu bar and data management area**

## Lesson 1: Basics of the graphical interfaceSet selection area

- geWorkbench allows sets of markers (gene probes) and of arrays/phenotypes to be defined and used.  This allows the application to:
- analyze only a desired subset of the data
- Return lists of genes from one module which can then be used in another module, e.g. a list of genes returned by a t-test of differential expression can then be further investigated through sequence retrieval and analysis.

- Lesson 1: Basics of the graphical interface**Set selection area**

## Lesson 1: Basics of the graphical interfaceVisualization and analysis areas

- To simply the display area, only the visualization and analysis components relevant to the type of dataset currently selected in the Project Folders area (area 1) are displayed.
- Thus choosing a microarray dataset will result in a different set of tabs being displayed as compared with those seen when a nucleotide sequence file is selected.
- When a new data file is loaded, or an analysis produces a new data set, not only is it added to the Project area (area 1), but an appropriate viewer in the Visualization area (area 3) is automatically selected.
- A selection of visualization and analysis tools will be demonstrated in the following sections.

- Lesson 1: Basics of the graphical interface**Visualization and analysis areas**

## Lesson 2: Setting Preferences

**Setting Preferences**

- Lesson 2: Setting Preferences

## Lesson 2: Setting PreferencesModifying settings

- The Preferences selection in the Tools menu allows users to specify how certain aspects of the system will behave.
- Once the preferences are set, they are persistent between application sessions.

- From the main menu, click on **Tools >Preferences.**

**Modifying Settings**** **

- Lesson 2: Setting PreferencesModifying settings

## Lesson 2: Setting PreferencesModifying settings

**Text Editor**: The editor selected will be used to open and inspect data sets loaded in a project. Notepad is the default setting.
-  **Visualization**: The color scheme to be applied to color mosaic images.
- Absolute: (default) Values are scaled against the largest absolute value found in the dataset, with positive values red and negative green.
-  Relative: Each marker is mean-variance normalized across all arrays.   A red-blue color scheme is used, with red showing positive and blue negative values.
**Genepix Value Computation**: Specifies how to compute the value displayed for a Genepix array. The default setting is Option 1 (Mean F635 - Mean B635) / (Mean F532 - Mean B532).

- Lesson 2: Setting PreferencesModifying settings

## Lesson 2: Setting PreferencesNotes

- The relative display performs its own transformation on the data just for purposes of visualization.  The underlying data is not changed.
- The **relative** selection for the Microarray Viewer preference will give odd-looking results if only a small number of arrays are loaded (e.g. 2).  This is because with only two values, each point will be at a color extreme – either blue or red.
- Changing the Microarray Viewer relative/absolute preference  will not take effect until the next time a data set is loaded.

- Lesson 2: Setting PreferencesNotes

## Lesson 3: Projects and Data Files

- Lesson 3: Projects and Data Files

## Lesson 3: Projects and Data FilesFile types

**For Microarrays:**
- Affymetrix **MAS5/GCOS**  text files.
**Affymetrix File Matrix** - this is the native file type created by geWorkbench, and contains a data matrix from any number of experiments merged together.
**RMA Express File** - RMA Express is a sophisticated tool for combining data
-       from multiple Affymetrix chips.  It is not a part of geWorkbench.
**Genepix** Files – created by a popular analysis program for two color arrays.
**For Sequence:**
**FASTA** Files. DNA or protein sequence files in FASTA format.
**Pattern Files** – created by the Pattern Discovery component.

- Lesson 3: Projects and Data FilesFile types

## Lesson 3: Projects and Data FilesOpening a file

- In this example, we will load 10 individual Affymetrix MAS5 format files, merging them into a single dataset.
- Create a **Project**.  All data must belong to a project. Right-click on the **Workspace** entry in the **Project Folders **window at upper left to create a new project.

**2.****   **Next, right-click on the new** Project** entry and
-       select **Open Files**.

- Lesson 3: Projects and Data FilesOpening a file

## Lesson 3: Projects and Data FilesLoading  and merging data

**3.**   Select file type **Affymetrix MAS5/GCOS **as shown.
**4.   **Make sure to check the **Merge files** checkbox.
**5.****   **Select 10 MAS5 format text files from the tutorial data directory.
**6.**   Click **Open**.

**3**

**6**

**5**

**4**

**The chip type HG_U95Av2 is** **recognized...**

- Lesson 3: Projects and Data FilesLoading  and merging data

## Lesson 3: Projects and Data FilesViewing data

- The merged dataset is listed in the Project folder. The data is displayed, in single array format,
-  in the **Microarray Viewer**. Note we have increased the intensity slider to maximum here.

- Lesson 3: Projects and Data FilesViewing data

## Lesson 3: Projects and Data FilesRenaming and saving a merged dataset

- Right click on the merged dataset and select **Rename**.
- Enter a new dataset name, e.g. merged_cardio.
- The dataset can also be saved to disk for later reuse.
- Right-click on the merged dataset and select **Save.**
- Enter a filename.

- Lesson 3: Projects and Data FilesRenaming and saving a merged dataset

## Lesson 4: Working with Subsets of Data

**Working with subsets of data**

- Lesson 4: Working with Subsets of Data

## Lesson 4: Working with Subsets of DataBackground

- Sets can be defined by the user, or may be created as a result of an analysis.
**Sets of arrays** can be used to distinguish between different experimental states, for example as part of a statistical analysis.
- The t-test requires two states be defined for comparison.
**Sets of markers** are returned from various analysis routines. For example the t-test returns a list of markers showing signficant differential expression, and after hierarchical clustering, the markers in a subtrees of the resulting dendrogram can be saved.
- geWorkbench supports groupings of sets. Each such group can contain different sets of markers or arrays.

- Lesson 4: Working with Subsets of DataBackground

## Lesson 4: Working with Subsets of DataOverview

-  How to create a set of arrays.
-  How to mark a set of arrays as "Active“.
-  How to classify a set of arrays, e.g. as "case" vs. "control".
-  How arrays can be grouped in different ways with descriptive tags.

**In this tutorial you will learn**

- Lesson 4: Working with Subsets of DataOverview

## Lesson 4: Working with Subsets of DataPreparation

- The second example will use the tutorial file webmatrix_quantile_log2_dev1.2_mv0.exp

- Lesson 4: Working with Subsets of DataPreparation

## Lesson 4: Working with Subsets of DataAssigning arrays to sets

- contain samples from the congestive
- cardiomyopathy disease state:

- We will leave the arrays in the default group,
- however you can create a new group by pushing
- the **New** button on **Array/Phenotype Sets** at
- lower left.

- In the Arrays/Phenotypes component, select
- the six arrays beginning with **JB-ccmp**, which
- represent the samples from the congestive
- cardiomyopathy disease state.
- 2. Right click, select **Add to Set.**

- 1

- 2

- Lesson 4: Working with Subsets of DataAssigning arrays to sets

## Lesson 4: Working with Subsets of DataAssigning arrays to sets

- 3.   Enter "CCMP" in the input box and click **OK**.

- 3

- 4.   Next, similarly label the arrays beginning with JB-n as "Normal“.
- 	The **Array/Phenotype Sets** component will now show the two sets added:

- Lesson 4: Working with Subsets of DataAssigning arrays to sets

## Lesson 4: Working with Subsets of DataActivating sets

- of arrays is "Active". Various analysis and visualization components can be set to
- only use/display activated arrays or markers.

- Lesson 4: Working with Subsets of DataActivating sets

- Note – if no Marker sets are explicitly activated, then all Markers are implicitly active.  The same applies to Arrays.

## Lesson 4: Working with Subsets of DataClassifying a set

- 1. Left-click on the thumb-tack icon in front of the phenotype name.
- 2. Select **Case** to specify the disease arrays as the "Case". The remaining  "Normal" arrays are by default considered Control.

- 1

- 2

- Lesson 4: Working with Subsets of DataClassifying a set

## Lesson 4: Working with Subsets of DataClassifying a set

- 3. A red thumbtack indicates an array set has been marked as "Case".

- 3

- Lesson 4: Working with Subsets of DataClassifying a set

## Lesson 4: Working with Subsets of DataUsing multiple array groups

- Here we show how several different groupings are defined in the example data file "webmatrix_quantile_log2_dev1_mv0.exp“.
- After  loading this file into geWorkbench as type "Affymetrix File Matrix", four groups can be seen in the Arrays/Phenotypes group pulldown menu at right.

- Lesson 4: Working with Subsets of DataUsing multiple array groups

## Lesson 4: Working with Subsets of DataUsing multiple array groups

- sets of arrays at right are displayed:

- Lesson 4: Working with Subsets of DataUsing multiple array groups

- If instead we choose the group "Cell Line",
- a different grouping of the same arrays is
- seen:

- Lesson 4: Working with Subsets of DataUsing multiple array groups

## Lesson 5: Working with Remote Data Sources

- Lesson 5: Working with Remote Data Sources

## Lesson 5: Working with Remote Data SourcesThe remote Open File dialog

- The **Open File** dialog allows remote sources to be added to the list of those available either manually or through discovery using grid services.

- Right-clicking on **Project** will bring up the **Open File** dialog.
-  Click the **Remote** radio button. The **Open File** dialog window will be expanded to include remote sources.
- Entries (locations, parameters) for non-grid services can be edited.

- Lesson 5: Working with Remote Data SourcesThe remote Open File dialog

## Lesson 5: Working with Remote Data SourcesThe remote Open File dialog

**Remote source selector – **chose from available** **Remote Resources.
**Go** button - Accesses the Remote Source that you selected.
**Add A New Resource** button - Opens the Data Source Definition Page used to add Remote Data.
**4.**     **Edit** button - Edits Remote Source Parameters.

**1**

**2**

**3**

**4**

- Lesson 5: Working with Remote Data SourcesThe remote Open File dialog

## Lesson 5: Working with Remote Data SourcesLoading data from a remote instance of caArray

- Click on the **Go** button next to the caArray data source at the bottom of the dialog. All available caArray experiments will be displayed.

- Lesson 5: Working with Remote Data SourcesLoading data from a remote instance of caArray

## Lesson 5: Working with Remote Data SourcesSelecting an experiment

** **Here we depict the experiment ending in *99049. The number of derived bioassays, 12, is displayed, along with the experiment information. (A new dataset, "Public Rembrandt" has 53 bioassays available).
**2.**    To start retrieving the bioassays themselves, right-click on the experiment and press **Get bioassays**. This will download the list of available bioassays into geWorkbench

**Select an experiment that has bioassays**

**1**

**2**

- Lesson 5: Working with Remote Data SourcesSelecting an experiment

## Lesson 5: Working with Remote Data SourcesRetrieving bioassay data

**To Retrieve Bioassay Data **

- Select the desired arrays and push the **Open** button.
- (You might want to first select just one, as each can take several minutes to download).

- Lesson 5: Working with Remote Data SourcesRetrieving bioassay data

## Lesson 5: Working with Remote Data SourcesAdding or modifying a remote source

**To add a remote source**

**1.**** **Click on the **Add A New Resource** button.

**2.** Fill in the Data Source definition page. **URL** and **Short Name** are required fields.
**3**. Click on the **OK** button.  The configuration is set up to automatically reflect your additional Data Source.

**2**

**2**

**3**

**To modify a remote source**

**1.****    **Click on the **Edit** button.

**2. ** Make the changes that you need.
**3.  **Click on the **OK** button

**2**

**3**

**1**

**1**

- Lesson 5: Working with Remote Data SourcesAdding or modifying a remote source

## Part 1: Data ManagementReview

- Part 1: Data ManagementReview

- In Part 1, we covered
- The basic layout of geWorkbench
- Loading microarray data from local and remote sources, and creating a merged dataset.
- Setting display preferences.
- How geWorkbench uses sets of arrays and markers to organize data for analysis and to convey results from one tool to another.

## Part 2: Data Manipulation

- Data Manipulation

## Part 2: Data ManipulationObjectives

- Part 2: Data ManipulationObjectives

- The objective of Part 2 is to learn the a few of the basic techniques available in geWorkbench for microarray data normalization and filtering.  This section will also cover the manual and automatic annotation of datasets.
- After completing Part 2, you should be able to:
- Normalize a microarray dataset.
- Filter out unwanted data points, such as low quality or missing data.
- Use and create new dataset annotations.

## Part 2: Data Manipulation:Lesson outline

- Lesson 6: Normalization
- Lesson 7: Filtering
- Lesson 8: Experiment Annotations

- Part 2: Data Manipulation:Lesson outline

## Lesson 6: Normalization

- Lesson 6: Normalization

## Lesson 6: NormalizationOverview

- geWorkbench supports a number of basic types of normalization.  In this section, two will be described:  Housekeeping Gene normalization, and Quantile normalization.

- Lesson 6: NormalizationOverview

## Lesson 6: NormalizationHousekeeping Gene normalization

- They can be used to provide a reference point against which to normalize.
- Using multiple housekeeping genes can lower the effect if one or more of them is actually varying with the experimental conditions.  geWorkbench uses the average expression of all selected housekeeping genes as the normalization factor.
- To perform a housekeeping gene normalization:
- Load or select a dataset, such as the merged_cardio set created earlier.
- For this example, first perform a log2 normalization on the dataset.  This will reduce the dominance of the more highly expressed genes.
- In the **Housekeeping Gene** normalization component, the **Load** button allows a predefined list of genes to be loaded.  The supplied file “housekeeping_marker_list.csv” is a list of 26 such genes applicable to the Affymetrix HG_U95Av2 chip type.

- Lesson 6: NormalizationHousekeeping Gene normalization

## Lesson 6: NormalizationHousekeeping Gene normalization

- Loaded genes can be moved to and from the active list using the arrow buttons.
- Press the **Normalize** button.  The current dataset will be normalized.

- Lesson 6: NormalizationHousekeeping Gene normalization

## Lesson 6: NormalizationQuantile normalization

- The assumption is that the real expression profile on each array is quite similar.
**Quantile** normalization at the Affymetrix probe level is a feature of the advanced analysis technique called RMA.   Quantile normalization in geWorkbench is applied at the gene (probeset) level.
- To perform a **Quantile** normalization:
- Load or select a dataset, such as the merged_cardio set created earlier.
- Go to the **Normalization** component in the Analysis area.

- Lesson 6: NormalizationQuantile normalization

## Lesson 6: NormalizationQuantile normalization

- Choose an **Averaging** method for handling missing values.
- Mean profile marker – average for marker across all arrays.
- Mean microarray value – average for array across all markers.
- Push the **Normalize** button.  The current dataset will be normalized.

- Lesson 6: NormalizationQuantile normalization

## Lesson 7: Filtering

- Lesson 7: Filtering

## Lesson 7: FilteringOverview

- Lesson 7: FilteringOverview

## Lesson 7: FilteringGenePix Flags filtering

- The file can include flags on individual data points, indicating e.g. bad or missing data.
- geWorkbench can filter out these flagged data points.
- To perform GenePix flags filtering:
- Load a GenePix format file, such as 21161 neu10.gpr.  This is included in the geWorkbench data directory.
- In the Analysis area, go to the **Filtering** component and select **GenePix Flags Filter**.
- The list of available flags is presented.  Choose a flag such as “bad” to filter on by checking its box.
- Push **Filter**.

- Lesson 7: FilteringGenePix Flags filtering

## Lesson 7: FilteringGenePix Flags filtering

- Such values can be removed entirely from the dataset through use of the **Missing Values Filter** (not shown).

- Lesson 7: FilteringGenePix Flags filtering

## Lesson 8: Experiment Annotations

- Lesson 8: Experiment Annotations

## Lesson 8: Experiment AnnotationsThree annotation components

**Dataset Annotation** – allows the user to type in comments on a dataset.
**Dataset History** - automatically records data transformation steps.
**Experiment Info** – information about the makeup of the dataset, e.g. the files that were merged to create it.
- Shown on the next slide are annotations for the dataset used in the Housekeeping Gene normalization example.
- A text file can also be read in to the **Dataset Annotation** component using the **Load Custom Data Annotations** button.

- Lesson 8: Experiment AnnotationsThree annotation components

## Lesson 8: Experiment AnnotationsThree annotation components

**Dataset Annotation (text entered by hand)**
**Dataset History**
**Experiment Info**

- Lesson 8: Experiment AnnotationsThree annotation components

## Part 2: Data ManipulationReview

- Part 2: Data ManipulationReview

- In Part 2 we covered microarray data normalization and filtering.  We also saw how geWorkbench keeps a record of each data transformation, and how annotations can be added to an experimental dataset by hand or from a file.
- After completing Part 2, you should be able to:
- Normalize a microarray dataset using tools such as Housekeeping Genes Normalization and Quantile normalization.
- Filter unwanted data points out, for example flagged points from a GenePix datafile.
- View dataset annotations created automatically by geWorkbench when a dataset is transformed, and
- create new dataset annotations by hand.

## Part 3: Analysis and DisplayObjectives

- Part 3: Analysis and DisplayObjectives

- The objective of Part 3 is to introduce some of the major tools for microarray data analysis and display found in geWorkbench.  The Scatter Plot and Expression Value Distribution (EVD) components are used to inspect microarray data, for example to evaluate data quality and the effectiveness of normalization and filtering.  The Reverse Engineering component can be used to examine relationships between the expression pattern of a chosen gene and others in the dataset.  Lists of genes which result from analysis steps can be evaluated through annotations and Pathway diagrams retrieved using the Marker Annotations component.
- After completing Part 3, you should be able to:
- Use the Scatter Plot and Expression Value Distribution components to examine microarray datasets.
- Run Reverse Engineering on a microarray dataset to find interactions with a chosen hub gene, and
- Retrieve gene annotations and pathway diagrams using the Marker Annotations component and view them.

## Part 3: Analysis and Display

- Part 3: Analysis and Display

- Analysis and Display

## Part 3: Analysis and DisplayLesson outline

- Lesson 9: The Scatter Plot component
- Lesson 10: Expression Value Distribution
- Lesson 11: Reverse Engineering
- Lesson 12: Gene Annotation and Pathway Viewing

- Part 3: Analysis and DisplayLesson outline

## Lesson 9: The Scatter Plot component

- Lesson 9: The Scatter Plot component

## Lesson 9: The Scatter Plot componentOverview

- Two marker plots are shown here. The marker AFFX-BioC-5_at is on the x-axis while the markers AFFX-BioB-5_at and AFFX-BioC-3_at are on the y-axes.** **

- Lesson 9: The Scatter Plot componentOverview

## Lesson 9: The Scatter Plot componentUsing the Scatter Plot

- In the scatter plot component, select the Marker or Array tab to choose the type of comparison.  The above picture used Marker.
- Highlight a reference marker or array.  The second and any following items selected will result in a graph being drawn, up to a limit of six.

- Lesson 9: The Scatter Plot componentUsing the Scatter Plot

## Lesson 9: The Scatter Plot componentBasic usage

- Markers or arrays available for selection.
- The first marker or array selected is placed on the  x-axis and his highlighted in black. A different marker or array can be placed on the x-axis by right-clicking the marker/array name and choosing *Put on X-Axis*.

**Basic Usage:  The steps of basic usage are indicated with numbers in the screenshot**

- Subsequent selections of markers or arrays after  a marker/array is on the x-axis results in the creation of a chart. Plotted markers/arrays are highlighted in  grey. Clicking again on one of these markers/arrays  results in the plot being removed.

- Lesson 9: The Scatter Plot componentBasic usage

## Lesson 9: The Scatter Plot componentBasic usage

- Clicking the *Rank Statistics Plot* checkbox transforms the data for analysis. The *x* and *y* values are sorted and plotted according to their rank.
- By default, a black reference line with slope 1 is  displayed in each chart. This may be turned off with the  *Reference Line* checkbox. Also, the slope of the line may be adjusted in the *Slope* textbox.

**Basic Usage:  continued**

- The *Clear Charts* button removes all charts and removes the x-axis selection. The *Print* button prints  the charts after allowing the user to adjust the page setup and choose a printer. The *Image Snapshot* button  captures the charts as an image and places it in to the project underneath the current data set.

- Lesson 9: The Scatter Plot componentBasic usage

## Each chart can be manipulated by 
right-clicking anywhere in the plot 
area. This brings up a menu that 
allows the chart to be individually 
saved as an image or printed, 
zoomed and visual properties 
adjusted. 

- right-clicking anywhere in the plot
- area. This brings up a menu that
- allows the chart to be individually
- saved as an image or printed,
- zoomed and visual properties
- adjusted.** **

- Set Selections** **

- Markers or microarrays that are members of active sets will be plotted with unique visual properties. These selections are managed for arrays and markers in the Phenotype and the Marker components, respectively. Consider an example where the two sets  are activated in the Phenotype component:** **

- Lesson 9: The Scatter Plot componentOptions and sets

## Lesson 9: The Scatter Plot componentExample plot, all arrays

- Lesson 9: The Scatter Plot componentExample plot, all arrays

## The visual properties of a set of markers or arrays may be adjusted. From within the  Array or Marker component, right click a set and choose Change Visual Properties.

- A dialog opens that allows the shape and color to be changed for that set. These properties are honored in the Scatter Plot as well as other caWorkbench components

- Lesson 9: The Scatter Plot componentSet options

## Lesson 10: Expression Value Distribution

- Lesson 10: Expression Value Distribution

## Lesson 10: Expression Value DistributionFeatures

- A slider  (at bottom) can be used to step between each array in the current dataset.
- A subset of markers within a given expression range can be selected using movable sliders (**Select values from** and **Select values to**) and added to a **Marker Set** using the **Add to Set** button
- A **T-Test** can be used to detect markers with significantly different expression.  A **Case** set of arrays must be activated in the **Arrays** component (remaining arrays are by default Control).
**Image Snapshot** saves an image of the graph to the Project Folders component.
**Mouse-over **annotations can be activated by pressing the lightbulb
- An array from the Housekeeping Genes Normalization example is displayed in the following picture:

- Lesson 10: Expression Value DistributionFeatures

## Lesson 10: Expression Value DistributionExample graph

- Lesson 10: Expression Value DistributionExample graph

## Lesson 10: Expression Value DistributionDisplay options

- Right-click on the EVD diagram to obtain the following list of display  and manipulation options.

- Lesson 10: Expression Value DistributionDisplay options

## Lesson 10: Expression Value DistributionWorking with activated datasets

- Select a group (e.g. CCMP arrays) and right-click, select **Add to Set**

- Lesson 10: Expression Value DistributionWorking with activated datasets

## Lesson 10: Expression Value DistributionDisplaying an activated set

- The set CCMP is active. The **One color per array**” checkbox is checked, so each array is shown in a different color.
- The base array, shown in red, is selected using the array slider.

- Lesson 10: Expression Value DistributionDisplaying an activated set

## Lesson 10: Expression Value Distributiont-test

- Now both the CCMP and Normal array sets are active.  CCMP has been marked **Case**.
- The t-test button is active, showing the t-statistic distribution.

- Lesson 10: Expression Value Distributiont-test

## Lesson 11: Reverse Engineering

- Lesson 11: Reverse Engineering

## Lesson 11: Reverse EngineeringOverview

- The Reverse Engineering component uses the information theory concept of **mutual information** to find these interactions.
- Mutual information here means the information that the expression pattern of one gene carries about the expression of another gene - it is a pairwise calculation.
- Mutual Information is in principle more sensitive and flexible than a simple correlation calculation.
- It is also invariant under certain data transformations, such as log transformations.

- Lesson 11: Reverse EngineeringOverview

## Lesson 11: Reverse EngineeringOverview

- Full scale runs of reverse engineering  algorithms, comparing all markers against each other, and typically done on datasets containing several hundred microarrays, are typically performed on large cluster computers and are not feasible on a desktop machine.
- .** **

- Lesson 11: Reverse EngineeringOverview

## Lesson 11: Reverse EngineeringReverse Engineering in the context of geWorkbench

- In a second step, a subset containing the best M markers is chosen (with a current limit of 100), and a complete pair-wise MxM/2 mutual information calculation is performed between them.
- The network resulting from this calculation can be displayed as a branched tree of interactions within the **Cytoscape** component.

- Lesson 11: Reverse EngineeringReverse Engineering in the context of geWorkbench

## Lesson 11: Reverse EngineeringPrerequisites

- For this example we will load the tutorial dataset "webmatrix_quantile_log2_dev1.2_mv0.exp".
- This contains a set of  100 experiments on Affymetrix HG_U95Av2 chips.  This filtered dataset has been reduced to 2226 markers.

- Lesson 11: Reverse EngineeringPrerequisites

## Lesson 11: Reverse EngineeringProfiler - selecting a hub gene

- In the Markers component search box, on the left side of the geWorkbench interface, enter 1973 and hit enter. This will find the marker 1973 _s_at,which is the c-Myc gene, a well-known transcription factor with many interactions.
- Click on this marker in the list. This will enter the marker into the **Hub Gene Label **field of** **the** Profiler. **

- Lesson 11: Reverse EngineeringProfiler - selecting a hub gene

## Lesson 11: Reverse EngineeringProfiler – Analyze 2D

- selected, hit** Analyze(2D). **This will return a list of all markers having a MI
- score of greater than the cutoff value (the default is 0.2).

- Lesson 11: Reverse EngineeringProfiler – Analyze 2D

## Lesson 11: Reverse EngineeringProfiler - Options

**Pearson** - Uses a Pearson correlation function to calculate the interaction scores.

- Lesson 11: Reverse EngineeringProfiler - Options

## Lesson 11: Reverse EngineeringProfiler – data output

- Lesson 11: Reverse EngineeringProfiler – data output

## Lesson 11: Reverse EngineeringProfiler – adding returned markers to a set

- If a smaller network is desired, a set of markers can be highlighted in the list originally returned. Only this selected subset, up to 100 markers, will then be used if "Create Network" is pressed.
- By right-clicking and selecting "Add to Set", the selected group can also be added to the **Markers** component as a new set which can be used in other components (sequence retrieval, annotation retriever etc.).

- Lesson 11: Reverse EngineeringProfiler – adding returned markers to a set

## Lesson 11: Reverse EngineeringProfiler – Create Network

- A network will be displayed based on the top 100 markers interacting with c-Myc. As described above, the MI algorithm is run again on these M=100 markers, in order to measure interactions between each pair.
-  Each marker is then connected via an edge with the marker it most strongly interacts with, with the chosen hub-gene at the center.

- Lesson 11: Reverse EngineeringProfiler – Create Network

## Lesson 11: Reverse EngineeringCytoscape viewer

- The resulting network is displayed in the **Cytoscape** viewer.

- Lesson 11: Reverse EngineeringCytoscape viewer

## Lesson 11: Reverse EngineeringCytoscape viewer layout

- The visualization in **Cytoscape **can be improved** **by going to the **Layout **menu,**  **and choosing** yFiles->organic:**** **

- Lesson 11: Reverse EngineeringCytoscape viewer layout

## Lesson 11: Reverse EngineeringCytoscape viewer layout

- Within the network created in **Cytoscape**, one can select the central gene, and then on the **Cytoscape **menu chose** Select->Nodes->First Neighbors **of selected nodes

- Lesson 11: Reverse EngineeringCytoscape viewer layout

## Lesson 11: Reverse EngineeringCytoscape viewer – choosing genes

- the graph.

-  and also added as a new set in the
**Markers **component**.**

- Lesson 11: Reverse EngineeringCytoscape viewer – choosing genes

## Lesson 11: Reverse EngineeringMotif Location Histogram

- Select the first (highest MI score) marker on the list and the graph shown below is drawn in the **Motif Location Histogram **display. This shows a plot of the expression values on each array for the selected hub marker vs any other marker selected in the list.

- Lesson 11: Reverse EngineeringMotif Location Histogram

## Lesson 12: Gene Annotation and Pathway Viewing

- Lesson 12: Gene Annotation and Pathway Viewing

## Lesson 12: Gene Annotation and Pathway ViewingThe Marker Annotation component

- Links to CGAP annotation pages are listed under Gene.
- Links to BioCarta pathway diagrams are listed under Pathway.
- Clicking on the pathway links will display the SVG pathway diagrams in the caBIO Pathways viewer.

- Lesson 12: Gene Annotation and Pathway ViewingThe Marker Annotation component

## Lesson 12: Gene Annotation and Pathway ViewingMarker Annotations - retrieving

- Press **Load set**
- Locate the file “**cluster_tree_total_pearsons_84_markers.csv**” and load it.
- Activate the set by checking the box in front of its entry. Here it has been renamed to “Cluster tree”.
- In the **Marker Annotations** component, press **Retrieve annotations**.
- Click on an Gene or Pathway link to view the annotations.

- Lesson 12: Gene Annotation and Pathway ViewingMarker Annotations - retrieving

## Lesson 12: Gene Annotation and Pathway ViewingMarker Annotations - display

- The list of markers can be sorted by Gene or by Pathway name by clicking on the column headings.

- Lesson 12: Gene Annotation and Pathway ViewingMarker Annotations - display

## Lesson 12: Gene Annotation and Pathway ViewingCGAP annotations

- Lesson 12: Gene Annotation and Pathway ViewingCGAP annotations

## Lesson 12: Gene Annotation and Pathway ViewingBioCarta Pathway display

- Lesson 12: Gene Annotation and Pathway ViewingBioCarta Pathway display

## Part 3: Analysis and DisplayReview

- Part 3: Analysis and DisplayReview

- Part 3 described several tools for the analysis and display of microarray data.
- Having completed Part 3, you should be able to:
- Use the Scatter Plot and Expression Value Distribution components to examine microarray datasets.
- Run Reverse Engineering on a microarray dataset to find interactions with a chosen hub gene, and
- Retrieve gene annotations and pathway diagrams.

## For further information....

- For further information....

- For further information about geWorkbench, including complete online tutorials, please see:
- www.geworkbench.org