# Presentation: 941532

## Introduction to Data Mining of Microarrays using the  MicroArray Explorer

- Peter F. Lemkin
- Lab. Experimental & Computational Biology, CCR, NCI Frederick, MD 21702
- MAExplorer: **http://www.lecb.ncifcrf.gov/MAExplorer**
- Rev: 10-27-2001

## Topics to be covered

**Need for data mining                                                                                  **1. What do you do with all that data?                                                         2. How do you manipulate it and find interesting correlations between                                                                                                                                                                                              particular genes and experimental conditions?
**Capabilities  of MAExplorer                                                                   **1. Direct-manipulation data mining: graphics, statistics, clustering**                                                                   **2.** **Freely available for download from Web to run on your computer    3. Integrated with NCI/CIT mAdb server (nciarray.nci.nih.gov) to                                                                                                                                                                                                   analyze your data on that server.

## Outline

**I.    Data Mining of microarray data**
**II.   MicroArray Explorer**
**III.  Installing MAExplorer on your computer**
**IV.  Using NCI/CIT mAdb data with MAExplorer**

## I.  Data Mining of Microarrays

**Outline**
- 1. The problem
- 2. Types of experiments
- 3. Quantified data used
- 4. Normalization of data
- 5. Expression profiles
- 6. Clustering methods
- 7. Partition samples by 2 conditions or ordered list
- 8. Refine the search criteria

##  I. The Problem

- We assume we have a spreadsheet of quantified microarray spots and the genes they represent,** What do we do with all those spots?**
- Could look for **patterns of changes** of experimental conditions with quantitative gene expression.
**Correlation of gene expression changes** with biological state implies a relationship but does not imply cause and effect

##  Types of Experiments

- What **types of expression** could we analyze?
-  Look at expression patterns:                                                                                                                                                                                                                                      1)  of individual genes,                                                                                                                                                                                                                                       2)  of gene families and clusters of genes,                                                                                                                                                                                                                    3)  as a function of conditions:  development, time (eg. cell cycle),                                                                                                                                                                                               cell lines, disease progression, pathways models, etc.
- Finding genes with **similar gene expression** may help in understand-ing a gene’s functional behavior or pathways
- These are **statistical entities**.  The more data samples and replicates are available, the better these estimates will be

## Things To Consider in Data Mining:

- Initially,** don’t know what patterns to look for**
- Could **hypothesize experiments** where changes might be expected
- Then **look for the differences between patterns**
- How do these tools help **find patterns**?
- By visual, statistical and clustering methods

## Example: the fold-change problem

- A measure of difference between 2 samples is “fold change”                                                                                                                                                                                                        ***f(x,y)= x/y***
- However  ***f *** is sensitive to noise. If noise in all measurements is constant** *****e****, *then ***f******e******(x,y,e)**** *has a range of values                                         *                                                                                                                                                                          ***[** * ****(x-e)/(y+e)   ***to***   (x+e)/(y-e)*** **]**
***Example: ***for two points*** (x,y)*** = (6,3) & (600,300), and ***e ***= 0.5 then the range of fold change for these two points is                                                                                                                                                                                                                   ***f(6,3)              =  ***2.0                                                                                                                                                                                                                                     ***f******e******(6,3,.5)         =  ***[5.5/3.5 to 6.5/2.5] = [**1.57 to 2.6**],                     and                                                                                                                                                                                                                                                               ***f(600,300)      =  ***2.0                                                                                                                                                                                                                                         ***f******e******(600,300,.5) =  ***[559.5/300.5 to 600.5/299.5] = [**1.995  to 2.005**].                                                                                                                                                                                                                                                                                                         [I. Kohane, Apr, 2001]

##  Quantified Data Used in Microarray Analysis

- 1) **Sets of samples** using either intensity (33P radio-labeled) or ratio (Cy3/Cy5 fluorescent-labeled) DNA
- 2) Each **hybridized sample** contains thousands of spots correlated to spotted clones or oligonucleotides (denoted “genes” in MAExplorer)
**If  ****33****P,** then normalize data **between** hybridized array samples by large numbers of common clones
**If (Cy3, Cy5),** then use either Cy3 or Cy5 to normalized standard sample **within** an array sample

##  Dividing samples into 2-condition sets and ordered N-conditions sample lists

- The **2-class division allows using sets of replicates** for computing better gene expression estimates and allows using t-Tests etc. to determine statistical significance
- The **ordered N-list of samples** is used to represent an ordered time-series, development stages, drug-dose response, etc.
- [***In MAExplorer***]: 2-class data is represented by **HP-X and HP-Y** sets and an ordered list of N-samples data is represented by the **HP-E **expression profile list

##  Normalize intensity data (33P) between samples

- Assuming linearity, for each array sample** j **get an **estimate *****T*****j**** of total cDNA labeling **for a common subset of genes
**Methods for estimating *****T*****j**** **: mean, median, log median, Zscore, log Zscore, sum of calibration DNA, sum of gene set, etc.
**Compute *****T*****j**** over specific gene set**: calibration genes, all genes on the array, specific subset of genes
- Scale spot data **within** each sample (for samples 1 and 2, gene **k**):                                                                                                                                                                                                        **s*********1,k ****= s****1,k ****/ *****T*****1**                                                                                                                                                                                                                              and                                                                                                                                                                                                                                                                      **s*********2,k ****= s****2,k ****/ *****T*****2**
- Then, we may **compare** normalized **s*********1,k  and ****s*********2,k **values

##  Normalize ratio data (Cy3, Cy5) between samples

- Let Cy5-labeled spots be the **standard sample** hybridized to all arrays (could use Cy3 instead).  Independent samples are labeled with Cy3
- Cy3 Data **within** each sample is scaled by corresponding Cy5 spot values (samples 1 and 2, and all genes k) to compute ratio values **s****r **where Cy5 labeled samples are common between samples 1 and 2:                                                                                                                                                                                                           **s****r****1k ****= s****1k,cy3 / ****s****1k,cy5**                                                                                                                                                                                                                              and                                                                                                                                                                                                                                                                     **s****r****2k ****= s****2k,cy3 / ****s****2k,cy5**
- Then scale (**s*********1k**, **s*********2k**) from (**s****r****1k**, **s****r****2k**) as for Intensity data.
- Then, we may** compare**  the normalized **s*********1k** and **s*********2k** values

##  Definition: Gene Expression Profile

- An **expression profile e****j** of an ordered list of **N** normalized spot values samples **v****jk** (k=1 to **N**) for a particular gene **j**
- The expression profile** **for a particular gene** j **is:**                                                                                                                                                                                                                   e****j**** = (v****j1****, v****j2****, v****j3****, ..., v****jN****)**
- A **difference** between two genes **p** and **q** may be estimated as a        **N**-dimensional metric “**distance**” between** e****p**** **and** e****q**
**Euclidean distance:   d****pq****  =  (1/N  ********   (v****jp****-**** ****v****jq****)****2 ****) ****1/2                                                                                                                                                                                                                                                                                             j=1:N**
- Other distance measures: correlation coefficient, city-block, etc.
- If distance is scaled to [0:1], then** Similarity** measure:  **s****pq**** = 1 - d****pq**

## I.1 Expression profile plots - examples

## Why Do We Need to Cluster the Data?

- Clusters represent one way to **identify similar gene expression** across a set of experiment samples
**Many ways to cluster** the data:                                                                                                                                                                                                                                      C.1 Find genes with similar expression                                                                                                                                                                                                                         C.2 K-means clusters where the number of clusters K is fixed                                                                                                                                                                                                   C.3 Hierarchical clustering where a binary hierarchy is created                                                                                                                                                                                                C.*n* Other methods: Self Organizing Memory (SOM), fuzzy                                                                                                                                                                                                               clustering, Support Vector Machines (SVM), etc.

## C.1  Finding similar genes

- Find a **sorted list** of all genes **{g****j****}** **similar** to gene **g****s**
- We define **g****j** similar to seed gene **g****s **if distance** ****d****js ****<** threshold ***T***

## C.2  K-means Clustering

**K-means clustering** finds **K** clusters of similar genes. Could use variance of clusters to determine if split into sub-clusters by increasing  **K**
- Don’t need distance matrix - faster clustering large numbers of **N** genes
**Algorithm**:                                                                                                  1.  Pick seed gene **s** and put it into cluster 1 (let **k=1**)                                                                                            2.  For all clusters **j=1 to k**** **, find gene **q** such that **d****jq **is a maximum                    3.  Set **k=k+1**.  Put gene** q** into new cluster **k**                                                                                                     4.  For **j= k to K**, repeat steps 2 and 3 until there are **K** clusters                                                   5.  Then, assign (**N-K**) remaining genes **q** into one of the **K** clusters                                                                                                                                                                                                **j** with minimum **d****jq                                                                                                                                             **6.  Compute new *virtual* genes as means {**e****k**} for each of **K** clusters                                            7.  Reassign all **N** genes **q** into **K** new clusters with minimum **d****pq                                                                                                                                                                                                       **using virtual genes {**e****p**}                                                                         8. Variants: use multiple seed genes, range of K values, minimize COV

## I.2 Example of K-means clustering

## C.3  Hierarchical clustering

**Hierarchical clustering **requires a distance matrix. For **N** genes (terminal gene clusters), it generates **2N-1** clusters.
**Distance matrix **is upper diagonal matrix **D **of** d****pq **of size **N(N-1)/2**
**D** can get quite large for clustering a large number of genes **N   **          [for **N**=5000, this is > 50 Mbytes!]
**Algorithm**:                                                                                                        1.  Assign all **N** genes to clusters 1 to **N**,  set **n** to **N**                                                2.  Find two clusters **p** and **q** such that** d****pq **is a minimum                                                                                                                                                                                                          2.1  Compute a *virtual* cluster vector **e****p,q **= *average*** (e****p****,e****q****)**                                                                                                                                                                                                   2.2  Set **n = n+1**                                                                                                                                                                                                                                               2.3  Assign “virtual” cluster to new cluster **n**  with estimated value **e****p,q** 3.  Repeat step 2 until **n = 2N-1**.

## I.3 Example of Hierarchical Clustering

##  Data mining:

***Data mining***** *****is a ***_***pattern discovery activity***_*** - use all the tools you have****. *
*It is ****open-ended**** because of the variety of ways data may be partitioned, normalized, pre-filtered, clustered, and viewed.*
*When data mining microarray data, look at correlated genes from the point of view of ****what relationships might be interesting from a biological view****. I.e. check out the results with PubMed, genomic databases, other lab experiments, etc.*

##  I.4 The Data Mining Paradigm: the Refinement Process

-                           **Start**
**               ****|**
**               ****v**
**             ****Have initial model of what may be related**
**               ****|**
**               ****v**
**   ****+------> ****Organize samples into sets of conditions**
**   ****|        ****Set data pre-filters (normalization, stat. Filters, etc)**
**   ****|        ****Examine Plots (scatter, expression, histograms, etc)**** **
**   ****|        ****Cluster current gene subset and view cluster plots**
**   ****|           |**
**   ****|           |**
** ****Refine views****  |**
**   ****|           v**
**   ****+<------ ****Evaluate results for interesting data relationships**
**   ****^           |**
**   ****|           v**
**   ****+<------ ****Save interesting gene sets**
**               ****|**
**            ****Found interesting results, make reports, export results**
**               ****v**
**             ****Done**

##  A Possible Analysis Scenario

**1. Select set of samples **from database
- 2. **Organize samples** as 2-class (X vs Y) sets or ordered list of N samples
- 3. **Select normalization** method
- 4. **Preview the data** with scatter plots and histograms
- 5. **Restrict search** using data filter to pre-filter a robust set of genes
- 6.  **Cluster genes** & visualize with EP plots, clustergram, dendrogram, etc
- 7. **Make** **report** and **access genomic Web databases** with resulting genes
- 8. **Save results** for later use or continued investigation

## II.  MicroArray Explorer (MAExplorer)

**Outline**
- 1. Description
- 2. Importing data
- 3. Examples of analysis capabilities

##  II. What is the MicroArray Explorer?

**MAExplorer is a Java stand-alone (off-line) or applet (Web-based) microarray real-time data-mining tool**
- Install **stand-alone** from the Web site for MS Windows, MacOS, Solaris, Linux, Unix
- Helps makes sense of **large complex sample data sets **with replicates
- Data mining is accomplished using **data filtering** with **direct manipulation** of data in **graphics** and **spreadsheets**
- Data filtering includes set-operations, statistics and clustering
- MAExplorer **handles a variety of quantified microarray data**

## MAExplorer Home Page http://www.lecb.ncifcrf.gov/MAExplorer

## II.1 MAExplorer Menu Interface 

##  What is the MicroArray Explorer? (continued)

- Developed for **Mammary Genome Anatomy Program**                                                                                                                                                                                                                       http://www.lecb.ncifcrf.gov/mae
- First use **statistical data filters to** **pre-filter** data (eg. sets of genes) so remaining data is robust
- Then use methods such as **cluster analysis to discover patterns **observed with direct-manipulation graphical plots and reports
- Save, restore, and compare results using** gene sets **and** condition lists.  **Save current state of **data mining analyses** locally in files (i.e. “bookmark”)
- Access third-party** genomic data** such as UniGene using links to Web databases
**Online documentation** (HTML manual, tutorials, examples, etc.) on Web site

## II.2 Mammary Geneome Anatomy Program MAExplorer  http://www.lecb.ncifcrf.gov/mae

## Sample Organization

**Samples are organization by**:                                                                                                                                                                                                                                         1.  X-Y paired samples                                                                                                                                                                                                                                         2.  sets of X-Y replicate samples (X and Y-sets)                                                                                                                                                                                                               3.  ordered expression profile lists of samples (E-list)
**Dynamically choose** hybridized probe samples as **HP-X, HP-Y** and **HP-E**

## II.3 Choosing HP-X, HP-Y sets and HP-E lists

## Data Filters

**Data filters are used to help converge on genes of interest:**                                                                                                                                                                                                     1.  normalization methods                                                                                                                                                                                                                                      2.  gene sets                                                                                                                                                                                                                                                  3.  spot intensity and ratio ranges                                                                                                                                                                                                                            4.  statistics                                                                                                                                                                                                                                                 5.  clustering (similar-genes, K-means, hierarchical clustering)

## II.4 Select One or More Simultaneous Data Filters

##  Data Views Using Pop-up Plots and Reports

**Plots**:   pseudo-array images, scatter-plots, histograms, expression profiles, clustergrams, dendrograms, silhouette-plots
**Reports**:  dynamic genomic Web-accessible spreadsheets, tab-delimited data for Excel
**Report data**:  gene reports, array information, correlation of samples, statistics on subsets of genes or samples
**Direct manipulation**:  select genes from plots and reports, select samples, choose HP-X, HP-Y and HP-E
**Web linkage to genomic DB**:  hyperlinked plots and reports

##  Sources of Quantified Microarray Data

- MAExplorer **handles variety of quantified microarray data                  **
- Data is specified by array-specific tab-delimited files that include:                                                                                                                                                                                            1. **GIPO** file - Gene In Plate Order (i.e. Print) table listing spot grid                                                                                                                                                                                            coords, Clone Id, gene name, GenBank & UniGene Ids, etc.                                                                                                                                                                                                   2. **Configuration** file describing array geometry, spot labeling, etc.                                                                                                                                                                                           3. **Quantification** files of hybridized sample quantified spot data                                                                                                                                                                                              4. **Samples DB** file listing the names of the hybridized samples
- Download quantified data from **NCI/CIT-ATC** **mAdb** database                                                                                                                                                                                                            http://nciarray.nci.nih.gov/
- Developing Java tool **Cvt2Mae** to convert commercial & academic quantified array data (Incyte, Affymetrix, etc.) to MAExplorer format

## II.2a Download NCI/CIT mAdb Data for MAExplorer

## II.3 Gene Data Filter is Intersection of Tests

- Current set of genes is **intersection** of gene sets each passing selected filter tests
- Filtered gene subset is used as **pre-filter** for subsequent clustering, plots, and tables
- Changing any filter parameters causes the data filter to be re-computed

## II.4 Overview of MAExplorer Database System 

- (Steps in cyan are performed before MAExplorer analysis.)

## Examples of MAExplorer

- The following examples demonstrate some of its capabilities
- Note: many more examples and discussion of the various analysis plots and reports may be found in the online reference manual at                                                                                                                                                                                                              **http://www.lecb.ncifcrf.gov/MAExplorer/hmaeHelp.html**

## II.5. Opening a database from local disk

- In stand-alone mode, you may browse a project database containing many startup databases.

## II.6 Specify Gene or Gene Subset by Name

- Specify gene or gene subset by gene name guesser using wildcard sub-strings eg. “*ONCO*”  indicated by magenta boxes - saved in ‘Edited Gene List’. [MGAP DB]

## MAExplorer User Interface

- The **MAExplorer menus** are similar to most Windows PC applications where pull-down menu selections are used to invoke operations.
- The current hybridization sample is displayed as a **pseudo image of spot intensity**.
- Names of the current HP-X and HP-Y **samples** are listed above the pseudo image.
- The “**Enter gene name or Clone ID**” button pops up a dialog box to assign the current gene (or set of genes) by name or wildcard.
**Clicking** on spots, points in plots or cells in spreadsheet reports **assigns the current gene**, displays information on it, and accesses Web genomic databases.
- The MGAP microarrays (shown here) contain 1,700 duplicated 33P-labeled clones indicated as fields 1 and 2 in the array pseudo image.
- Duplicated grids of cDNA spots are labeled as 1-A, 2-A, 1-B, 2-B, etc.

## II.7a Named Genes and ESTs

- Specify sets of  genes for *all named genes* and* all ESTs *indicated in the microarray by white circles. [MGAP data]

## II.7b Named Genes

- Specify sets of  genes for *all named genes *indicated in ratio X/Y array plot by white circles

## II.7c ESTs similar to named genes

- Specify sets of  genes for *all ESTs similar to named genes *indicated in the microarray by white circles

## II.7d Unknown ESTs

- Specify sets of  genes for *unknown  ESTs *indicated in the microarray by white circles

## II.8a Scatter Plots of Two Conditions

- X-Y scatter plot of ‘sets’ of 2-probes C57B6 vs Stat5a (-,-) 13-day pregnancy in array  [MGAP]. Current gene (green circle) & Edited Gene List (magenta squares) in plot

## II.8b Zoomed X-Y Scatter Plot (of II.8a)

- Zoomed in on Raf-related oncogene using scrollbars. Genes not passing Filter are grayed out in the plot

- e

- e

## II.9a Genes Filtered by Gene Class Set

- Genes class subset *named genes and ESTs *in both array & scatter plot normalized by Zscore of log intensity.

## II.9b Genes Filtered by Ratio-Histogram Bin

- Genes filtered by HP-X/HP-Y  C57B5-preg / Stat5a(-,-)  ratio-histogram bin-range [2.5:1000]. Histogram is for all named genes and for ESTs.

## II.9c Genes Filtered by Intensity-Histogram Bin

- Genes filtered by intensity to remove low signal strength sample genes.

## II.10a Expression Profile Plots of N-conditions

- Expression profile plot of 38-conditions of current gene (green) . Note numbered list of probes. Intensity data for probe #4 is indicated in red - by clicking on a line in plot

## II.10b List of Expression Profile Plots

- Scrollable list of EP plots for onco and proto-oncogenes in EGL for MGAP database

## II.10.c Expression Profile Overlay Plots

- Overlay EP plots of multiple genes showing current gene for MGAP database

## II.10.d Expression Profile Overlay Plots

- Overlay EP plots for onco and proto-oncogenes in EGL for MGAP database

## II.11a Scrollable Dynamic Gene Reports

- Scrollable gene report of highest ratio genes & NCI mAdb pop up Web browser page (foreground) of particular gene. Clicking on blue hypertext cell in gene report (middle) invokes pop up web page (NCI mAdb Clone Report shown here)

## II.11a.1 Scrollable Dynamic Gene Reports - UniGene Report

## II.11b Gene Reports are Exportable to Excel

- Tab-delimited gene reports are exportable to Excel using cut & paste or SaveAs DB

## II.11c Sample Information Array Reports

- Details are available on all hybridized array samples

## II.11d Sample Web links Array Reports

- Hyper-links to Web databases describing the hybridized samples popup Web  browser (customizable for specific database projects)

## II.11e Samples Correlation Reports

- Sample vs. Sample correlation coefficient reports for set of currently Filtered genes

## Clustering Methods: (4 methods)II.12a Finding Genes With Similar Expression

- Genes that clustered to Raf-related oncogene with similar expression patterns

## II.12b EP Plots for  Similar Genes

- Sorted list of EP plots of similar genes that clustered to Raf-related oncogene

## II.12c Finding K-Clusters of Genes with Similar Expression Patterns  (similar to K-means)

## II.12d Expression Profiles of Clusters 

- Scrollable list of EP plots showing genes from clusters #1, #2, #3 (from figure II.12c)

## II.12e Mean Expression Profile Plots of Clusters

- Mean clusters and their statistics (from figure II.12c).  Error bars are standard-deviation of genes’ intensities in each cluster

## II.13a Hierarchical Clustering ClusterGrams of Expression Profiles 

## II.13b Hierarchical Clustering Dendrogram

- Clusters less than cluster distance from each other are shown in red (from figure II.12f)

## Summary of MAExplorer

- MAExplorer is used as a stand-alone application or as applet over the Web
- Accepts different array geometries, spot supports, 33P or Cy3/Cy5 labeling, scanners
- Analyzes multiple probes, X-Y replicate sets, expression profiles, replicate spots
- Provides direct manipulation of array pseudo images, scatter-plots, histograms, clustergrams, dendrograms, silhouette plots, spreadsheets
- Data filters genes by gene subsets, spot intensities and ratios, and statistical tests, etc.
- Set operations on gene subsets help manage search results
- Uses active Web links to genomic, histology and model Web databases
- Generates reports as Web-accessible spreadsheets or exportable to Excel
- Users may save their data-mining session state locally for later use or sharing
- Building tools to import commercial and academic quantified micro array data
- MAExplorer used to identify genes in MGAP DB preferentially expressed during lactation.  Results verified using northern blots (NIDDK), ***Nucleic Acids Res.***** 28:4452-4459 (2000).**
- Online documentation (manual, tutorials, examples, etc.) is available on Web site

## Some MAExplorer URL References

**Home Page**  **(includes the following and other links)**                                                                  http://www.lecb.ncifcrf.gov/MAExplorer/
**Reference Manual  (including tutorials, and use with other arrays sections)**                               http://www.lecb.ncifcrf.gov/MAExplorer/hmaeHelp.html         (online)  http://www.lecb.ncifcrf.gov/MAExplorer/MaeRefMan.zip        (download)
**Overview of MAExplorer**                                           http://www.lecb.ncifcrf.gov/MAExplorer/PDF/Overview-MAE.pdf
**Examples of data mining with MAExplorer**                                                       http://www.lecb.ncifcrf.gov/MAExplorer/Examples-MAE-session.pdf
**Using with mAdb with MAExplorer**                                                http://www.lecb.ncifcrf.gov/MAExplorer/Using-mAdb-with-MAExplorer.pdf
***Nucleic Acids Res*****. (2000) 28:4452 paper **http://www.lecb.ncifcrf.gov/MAExplorer/lemkin-NAR-2000-Vol28-pp4452.pdf
**Download MAExplorer  (includes 38 samples from MGAP DB)                               **http://www.lecb.ncifcrf.gov/MAExplorer/hmaeInstall.html

##  Using MAExplorer with mAdb data

- The **NCI/CIT mAdb Web microarray database server** is an array data repository and analysis facility for microarrays created in conjunction with the NCI-ATC facility.                                                                                                                                                                                                                                                            **http://nciarray.nci.nih.gov/**
- It can create a set of data files, downloaded as a Zip file from the mAdb, in a format compatible with MAExplorer
**Section III** describes the procedure for **downloading MAExplorer. **You should periodically check the MAExplorer Web site to see if there is a major revision that you might want to download
**Section IV** describes the procedure for **downloading a mAdb data set** and starting MAExplorer on that data.
**Help desk for MAExplorer :**  *mae@ncifcrf.gov*

## III.  Installing MicroArray Explorer on Your Computer

**Outline**
- 1. MAExplorer home page
- 2. Download installer to your                                                                                                                                                                                                                                      computer
- 3. Run the installer
- 4. Test it on MGAP sample database

## III. Procedure to download & install MAExplorer

- 1. Go to **http://www.lecb.ncifcrf.gov/MAExplorer** with your Web browser.
- 2. Select **Download **to start the install process. It uses the InstallAnywhereTM program. You have a choice of:
- 3.1 Allowing InstallAnywhereTM to select the installer and request where you want to install it (eg. in Windows this would be  _C:\Program Files\MAExplorer_), or
- 3.2 You may download the installer file and select where you want to install it.
- A) Find your computer **Platform** in the list. Click on the corresponding                                                                                                                                                                                              **Download** word and save the installer on your computer.
- B) Go to **View** for your platform in the same download Web page to see how to                                                                                                                                                                                         finish the installation for your particular platform.
- C) Now install MAExplorer on your computer in the location you desire.
- 4. You are ready to use MAExplorer.  In Windows Start menu, click on MAExplorer. After it starts,  select “Open file DB” in the File | Database menu.

## III.1  MAExplorer home page - press “download”http://www.lecb.ncifcrf.gov/MAExplorer

## III.2  Download Stand-alone version Web page - find your “Platform”, then select “Download”

## III.3  Save the installer on your local computer

## III.4  Start the installer - e.g. in Windows, click on installMAE.exe. Then answer questions, “OK” etc.

## III.5  Sucessive steps during installation of MAExplorer - press “Next”

## III.6  Finish installation of MAExplorer:      A) press “Install”,  B) press “Done”

## III.7  Directory structure of downloaded files

## III.8  Start MAExplorer from Windows PC “Start” menu. Initially starts with empty database .

## III.9 Open demo (MGAP) database from local disk

- Browse  demo project for startup database. Select _File_ menu, then _Open file DB_

## IV.  Using NCI/CIT mAdb data with MicroArray Explorer

**Outline**
- 1. Log into mAdb
- 2. Select your data
- 3. Export it as a Zip file to your computer
- 4. Unpack the Zip file
- 5. Click on the           Start.mae

##  IV. Procedure to use MAExplorer on mAdb data

- 1. Install MAExplorer if not already installed (see previous Procedure 1).
- 2. Go to **http://nciarray.nci.nih.gov/** with your Web browser
- 3. Go to "**Gateway**"
- 4. Go to "**Tool**s"
- 5. Select the set of projects to be exported from the scrollable list.
- 6. Select "**BETA formated array data retrieval tool**".
- 7. Select "**LECB/NCI** **MAExplorer**" for the "Retrieval format".
- 8. **Submit**. This will eventually replace the Web page with a new page containing a numbered (number related to date and time of day) file ending in **.zip**. The file will be purged after a while, so it should not be  treated as a permanent link.
- 9. Click on the **.zip** file and save it locally to your disk.
- 10. Unpack the **.zip** file to a new directory, for example “_*myData*_”
- 11. On Windows systems, double click on _Start.mae_ in the  _*myData*__\MAE\_  directory. This will start up MAExplorer.

## IV.1 NCI/CIT mAdb Web server home pagehttp://nciarray.nci.nih.gov/

## IV.2 Press “Gateway” & Log on to mAdb server

## IV.3 Select: a) Projects, b) “Formated Array data Retrieval Tool”, c) then press “Continue”

## IV.4  Set a) Format option to “MAExplorer”, b) select arrays to be analyzed, c) press  “Submit”

## IV.5 It will contact the mAdb server to get data

## IV.6  Click on Zip file (e.g. 319-103653.zip) result to download to your computer.  

## IV.7  Save the Zip data file on your local disk

## IV.8  Unzipping the Zip data file

- (WinZip is available from the mAdb download Web site)

## IV.9  Inspecting the unzipped data files

## IV.10 Click “       Start.mae” to start MAExplorer

## IV.11 Explore data using data filters, plots, etc.

## Summary of Downloading a mAdb data set

- This procedure downloads one or more projects into a directory on your local computer.
- At this point, data mining may proceed using MAExplorer independent of the Internet connection to mAdb.
- If you want to add additional hybridized samples, you should download all of the samples again (this will be resolved in the future). Currently, you can’t easily merge data from several downloaded data sets.