Microarray data analysis
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Microarray Data Analysis. Data preprocessing and visualization Supervised learning Machine learning approaches Unsupervised learning Clustering and pattern detection Gene regulatory regions predictions based co-regulated genes

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Microarray Data Analysis

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Microarray Data Analysis

  • Data preprocessing and visualization

  • Supervised learning

    • Machine learning approaches

  • Unsupervised learning

    • Clustering and pattern detection

  • Gene regulatory regions predictions based co-regulated genes

  • Linkage between gene expression data and gene sequence/function databases


Unsupervised learning

  • Supervised methods

    • Can only validate or reject hypotheses

    • Can not lead to discovery of unexpected partitions

  • Unsupervised learning

    • No prior knowledge is used

    • Explore structure of data on the basis of corrections and similarities


DEFINITION OF THE CLUSTERING PROBLEM

Eytan Domany


CLUSTER ANALYSIS YIELDS DENDROGRAM

T (RESOLUTION)

Eytan Domany


BUT WHAT ABOUT THE OKAPI?

Eytan Domany


Centroid methods – K-means

Data points at Xi , i= 1,...,N

Centroids at Y , = 1,...,K

Assign data point i to centroid  ; Si = 

Cost E:

E(S1 , S2 ,...,SN ; Y1 ,...YK ) =

MinimizeE over Si , Y

Eytan Domany


K-means

  • “Guess” K=3

Eytan Domany


K-means

  • Start with random

    positions of centroids.

Iteration = 0

Eytan Domany


K-means

  • Start with random

    positions of centroids.

  • Assign each data point

    to closest centroid.

Iteration = 1

Eytan Domany


K-means

  • Start with random

    positions of centroids.

  • Assign each data point

    to closest centroid.

  • Move centroids to

    center of assigned

    points

Iteration = 2

Eytan Domany


K-means

  • Start with random

    positions of centroids.

  • Assign each data point

    to closest centroid.

  • Move centroids to

    center of assigned

    points

  • Iterate till minimal cost

Iteration = 3

Eytan Domany


K-means - Summary

  • Fast algorithm: compute distances from data points to centroids

  • Result depends on initial centroids’ position

  • Must preset K

  • Fails for “non-spherical” distributions


2

4

5

3

1

1

3

2

4

5

Agglomerative Hierarchical Clustering

Need to define the distance between thenew cluster and the other clusters.

Single Linkage: distance between closest pair.

Complete Linkage: distance between farthest pair.

Average Linkage: average distance between all pairs

or distance between cluster centers

at each step merge pair of nearestclusters

initially – each point = cluster

Distance between joined clusters

The dendrogram induces a linear ordering of the data points

Dendrogram

Eytan Domany


Hierarchical Clustering -Summary

  • Results depend on distance update method

  • Greedy iterative process

  • NOT robust against noise

  • No inherent measure to identify stable clusters

  • Average Linkage – the most widely used clustering method in gene expression analysis


nature 2002 breast cancer

Heat map


Cluster both genes and samples

  • Sample should cluster together based on experimental design

    • Often a way to catch labelling errors or heterogeneity in samples


Epinephrine Treated Rat Fibroblast Cell


Correlation coeff

Heap map

Normalized across each gene


  • Pearson distance

Distance Issues

  • Euclidean distance

g1

g3

g2

g4


Exercise

  • Use Average Linkage Algorithm and Manhattan distance.


Exercise


Issues in Cluster Analysis

  • A lot of clustering algorithms

  • A lot of distance/similarity metrics

  • Which clustering algorithm runs faster and uses less memory?

  • How many clusters after all?

  • Are the clusters stable?

  • Are the clusters meaningful?


Which Clustering Method Should I Use?

  • What is the biological question?

  • Do I have a preconceived notion of how many clusters there should be?

  • How strict do I want to be? Spilt or Join?

  • Can a gene be in multiple clusters?

  • Hard or soft boundaries between clusters


The End

  • Thank you for taking this course. Bioinformatics is a very diverse and fascinating subject. We hope you all decide to continue your pursuit of it.

  • We will be very glad to answer your emails or schedule appointments to talk about any bioinformatics related questions you might have.

  • We wish you all have a wonderful summer break!


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