# Presentation: 721096

## Course #412 Analyzing Microarray Data using the mAdb System  March 21-22, 2006  1:00 pm - 4:00pmmadb-support@bimas.cit.nih.gov

- Intended for users of the mAdb system who are familiar with mAdb basics
- Focus on analysis of multiple array experiments

-  Esther Asaki, Yiwen He

## Agenda

- mAdb system overview
- mAdb dataset overview
- mAdb analysis tools for dataset
- Class Discovery - clustering, PCA, MDS
- Class Comparison - statistical analysis
- t-test
- ANOVA
- Significance Analysis of Microarrays - SAM
- Class Prediction - PAM
- Various Hands-on exercises

## 1. mAdb system overview

**Notes:**
If you have used mAdb before, you should have a general knowledge about how to work with mAdb.  
Let’s  show a hands as how many people have worked with mAdb for your microarray data? 
How many people have taken the Introduction training class?

## mAdb Data Workflow

** ****Upload Data**

**mAdb Data Workflow**

**Quality Control**

**Prepare Dataset**

**Analysis/Model**

**Review Annotation**

**File Format**
** ****GenePix**
** ****MAS5**
** ****GCOS 1.1**
** ****ArraySuite**

## 2. mAdb dataset overview

## What is a dataset?

- mAdb Dataset
- Collection of data from multiple experiments
- Genes as rows and experiments as columns

- Genes

- Gene expression level

**=**

- (normalized) Log( Red signal / Green signal)

- 	**sample1****	****sample2****	****sample3****	****sample4****	****sample5 ****	****...**
- 1	 0.46	 0.30	 0.80	 1.51	 0.90	...
- 2	-0.10 	 0.49	 0.24	 0.06	 0.46	...
- 3	 0.15	 0.74	 0.04	 0.10	 0.20	...
- 4	-0.45	-1.03	-0.79	-0.56	-0.32	...
- 5	-0.06	 1.06	 1.35	 1.09	-1.09	...

## Slide 7

## Dataset Display Page

**Dataset Display Page**

## Dataset Display

**Dataset Display**

- Dataset display options dynamic
- Integrated gene information

## mAdb Dataset Display

- Group label
- Sample name

- genes

## Dataset Group Assignment

- Array Order Designation/Filtering
- Array Group Assignment/Filtering
- Filter/Group by Array Properties

## Dataset group assignment tools

**Dataset group assignment tools**

## Array Order Designation/Filtering

**Array Order Designation/Filtering**

- Order arrays in dataset
- Delete/Add back arrays in dataset
- Subsequent analysis will be ordered by groups first and then ordered within each group
- Does not group arrays

## Array Group Assignment/Filtering

**Array Group Assignment/Filtering**

- One click per array for additional group
- Not convenient for large dataset
- Can not order within group

## Filter/Group by Array Properties

- Array properties include Name and Short Description
- Identify consistent pattern

## Filter/Group by Array Properties

- Convenient for large dataset
- Can not order arrays within group

## Group Assignment 

**Group Assignment **

- Group assignment information is carried into relevant analysis
- Dataset is independent from microarray platforms

## Examples for using groups

- Additional Filtering per Group
- Correlation summary report
- Average arrays within groups
- Calculate statistics within groups

## Filter by Group Properties

- Ensures each group has sufficient number of non-missing values

## Correlation Summary Report

- Pair wise correlation between 2 samples in dataset
- Individual scatter plot available
- Group pattern for quality control

## Visual Bivariate Data Analysis

## Average Arrays within Groups

- Averages calculated using log ratios regardless of linear or log display options chosen

## Calculate statistics within Groups

- All values calculated using log ratios regardless of linear or log display options chosen

## Dataset ISmall Round Blue Cell Tumors (SRBCTs) 

- Khan et al. *Nature Medicine *2001
- 4 tumor classifications
- 63 training samples, 25 testing samples, 2308 genes
- Neural network approach

## Hands-on Session 1

- Lab 1- Lab 4
- Read the questions before starting, then answer them in the lab.
- Use web site: _[http://mAdb-training.cit.nih.gov](http://madb-training.cit.nih.gov/)_
- Avoid maximizing web browser to full screen.
- Total time: 20 minutes

## 3. mAdb dataset analysis tools

- Class Discovery: clustering, PCA, MDS
- Class Comparison: statistical analysis
- Class Prediction: PAM

## Analysis Overview

| Class Discovery
- Unsupervised |  Clustering – Hierarchical, K-means, SOMs
 Principal components Analysis (PCA)
 Multidimensional Scaling (MDS) |
| --- | --- |
| Class Comparison
- Supervised
 |  paired t-tests 
 t-test pooled (equal) variance
 t-test separate (unequal) variance
 Significance Analysis of Micro- arrays (SAM) 
 One way ANOVA
 Wilcoxon Rank-Sum (Mann Whitney U)
 Wilcoxon Matched-pairs Signed Rank
 Kruskal-Wallis |
| Class Prediction
- Supervised | Prediction Analysis for Microarrays (PAM) |

## Class Discovery Example

- Discover cancer subtypes by gene expression profiles
- Identify genes which have different expression patterns in different groups
- Tools: Cluster Analysis, PCA and MDS

## Class Comparisons Example

- Find genes that are differentially expressed among cancer groups
- Find genes up/down regulated by drug treatment
- Tools:
- Group comparison
- Statistics Results filtering

## Class Prediction Example

- Identify an expression profile which correlates with survival in certain cancers
- Identify an expression profile which can be used to diagnose different types of lymphomas
- Tools: Prediction Analysis for Microarrays (PAM)

## 3. mAdb dataset analysis tools

- Class Discovery: clustering, PCA, MDS
- Class Comparison: statistical analysis
- Class Prediction: PAM

## Class Discovery

- Dataset with large amount of data
- Dataset not organized
- Visualization with Clustering, PCA, MDS

## Cluster Analysis

- Organize large microarray dataset into meaningful structures
- Visualize and extract expression patterns

## What to Cluster?

- Genes - identify groups of genes that have correlated expression profiles
- Samples - put samples into groups with similar overall gene expression profiles

## Hierarchical clustering
Partitional clustering
K-means
Self-Organizing Maps (SOM)

- Partitional clustering
- K-means
- Self-Organizing Maps (SOM)
**               **

**Clustering Methods**

## Cluster Example on Genes

- Clustering

**Cluster Example on Genes**

- Much easier to look at large blocks of similarly expressed genes
- Dendogram helps show how ‘closely related’ expression patterns are

- A. Cholesterol syn.
- B. Cell cycle
- C. Immediate-early
-      response
- D. Signaling
- E. Tissue remodeling

## 2 Steps

- Pick a distance method
- Correlation
- Euclidian
- Pick the linkage method
- Average linkage
- Complete linkage
- Single linkage

## Correlation

**Correlation**

- Compares shape of expression curves (-1 to 1)
- Can detect inverse relationships (absolute correlation)

## Correlation (centered-classical Pearson)
Correlation ( un-centered)
assume the mean of the data is 0, penalize if not
Measures both similarity of shape and the offset from 0

- Correlation ( un-centered)
- assume the mean of the data is 0, penalize if not
- Measures both similarity of shape and the offset from 0

**Two Flavors of correlation**

## Euclidean Distance

## Similarity/Distance Metric Summary

## Hierarchical Clustering Example

## Tree Cutting

**Tree Cutting**

- Degrees of
- dissimilarity

## Hierarchical Clustering Summary

- Detection of patterns for both genes and samples
- Good visualization with tree graphs
- Dataset size limitations
- No partition in results, require tree cutting

## Partitional clustering : K-means

- Partition data into K clusters, with number K supplied by user.
- Produce cluster membership as results.

## Divide observations into K clusters.
Use cluster averages (means) to represent clusters
Maximize the inter-cluster distance Minimize intra-cluster distance.

- Use cluster averages (means) to represent clusters
- Maximize the inter-cluster distance Minimize intra-cluster distance.

**K-means Algorithm**

## K-means Algorithm

**K-means Algorithm**

- k1

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- k3

- k4

## K-means Algorithm

- X1

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**K-means Algorithm**

## K-means Algorithm

- X1

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**K-means Algorithm**

## K-means Algorithm

- X1

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**K-means Algorithm**

## mAdb K-means Options

**mAdb K-means Options**

## K-means Clustering Example

- Save as input to TreeView

- Create new subset of genes

- Show hierarchical clustering

## Fast algorithm
Partitions features into smaller, manageable groups
mAdb allows hierarchical clustering within each K-mean cluster

Must supply reasonable number of K
No relationship among partitions

- Partitions features into smaller, manageable groups
- mAdb allows hierarchical clustering within each K-mean cluster
- Must supply reasonable number of K
- No relationship among partitions

**Summary**

## Self-Organizing Maps (SOM)

- Partitions data into 2 dimensional grid of nodes
- Clusters on the grid have topological relationships
- 2 numbers for the dimension of grid supplied by user

## mAdb SOM options

- Set number of iteration

- Hierarchical within SOM clusters

- Activate Randomized Partition

## SOM Clustering Example

- Save as input to TreeView

- Create new subset of genes

- Show hierarchical clustering

## SOM Summary

- Neighboring partitions similar to each other
- Partitions features into smaller groups
- mAdb allows hierarchical clustering within each SOM cluster
- Results may depend on initial partitions

## Summary of mAdb Clustering Tools

- Hierarchical

- K-means

- SOM

- Relationship visualization

- Data Size

- Performance

- Small

- Large

- Large

- Slow

- Fast

- Middle

- Cluster Type

- Gene/Array

- Gene

- Gene

- Tree
- Structure

- partition
- Membership

- Partition
- 2-D topology

## Cluster Analysis

- Normalization is important
- Reduce data points by variance
- Use K-mean or SOM to partition dataset
- Use biological information to interpret results

## Hands-on Session 2

- Lab 5 - lab 6 (Lab 7 optional)
- Total time: 15 minutes

## Principal Component Analysis

- How different samples are from each other
- Project high-dimensional data into lower dimensions, which captures most of the variance
- Display data in 2D or 3D plot to reveal the data pattern

## Principal Component Analysis

- Hypothesis - there exist unobservable or “*hidden*” variables (complex traits) which have given rise to the ***correlation*** among the observed objects (genes or microarrays or patients)
- The Principal Components (PC) Model is a straightforward model  that seeks to achieve this objective

## PCA 3D plot

- Axes represent the first 3 components
- The first 3 components should explain most of the variance
- Formation of clusters
- Relationship of clusters.

## Raw Data

- n is the number of genes (gene probes); m is the number of arrays (experiments)

- A correlation matrix is a symmetric matrix of correlation coefficients
-  (                          and                                                        )

- A Structure of Correlation Matrix is the **Major Object for PCA**

**Basic Idea** **of PCA** is a Data Reduction Method Based on  Analysis of Correlation Pattern(s) That Can Exist Among the  Observed Random Variables (i.e.  Expression values of Genes).

## The Results of PCA are a small set of the orthogonal (independent)

- Variables  Grouping of the Variables

- From a purely mathematical viewpoint the purpose of PCA is to transform **n** correlated random variables to an orthogonal set which reproduces the original variance/covariance structure.

- x2

- x1

- y1

- y2

*r**12**=0.90*

- (The First) Principal Component y1 can “explain” the major fraction
- (~90%) of a dispersion of variables x1 and x2 for all of the 10 observed
- objects.

## Sample:Small Round Blue Cell Tumors (SRBCTs) 

-  63 Arrays representing 4 groups
-  BL (Burkitt Lymphoma, n1=8)
-  EWS (Ewing, n2=23)
-  NB (neuroblastoma, n3=12)
-  RMS (rhabdomyosarcoma, n4=20)
-  There are  2308 features (distinct gene probes)

##  PCA Detailed Plot

- ”Scree” plot
- 2-D plots

## PCA 2-D plots

-   First 2 components separate 3  groups well

## An alternative for PCA 
Non-linear projection methodology
Tolerates missing values

- Non-linear projection methodology
- Tolerates missing values

**MDS overview ****(Multidimensional Scaling)**

## Summary of PCA and MDS

- Dimension reduction tools
- Graphic representation to help explain patterns
- Quality control for experimental variance

## Hands-on Session 3

- Lab 8
- Total time: 15 minutes
- Next class tomorrow at 1:00 pm