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Course Work Project

Course Work Project. Project title “Data Analysis Methods for Microarray Based Gene Expression Analysis” Sushil Kumar Singh (batch 2002-03) IBAB, Bangalore Done at Siri Technologies Pvt. Ltd. Bangalore. Outline. Introduction Overview of Data Analysis Normalization

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Course Work Project

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  1. Course Work Project Project title “Data Analysis Methods for Microarray Based Gene Expression Analysis” Sushil Kumar Singh (batch 2002-03) IBAB, Bangalore Done at Siri Technologies Pvt. Ltd. Bangalore

  2. Outline • Introduction • Overview of Data Analysis • Normalization • Clustering Algorithms • Future work • Acknowledgements • Questions ???

  3. Introduction

  4. Overview of Data Analysis

  5. Normalization • An attempt to remove systematic variation from data. • Sources of systematic variation – • Biological source • Influenced by genetic or environmental factors, Age, sex etc. • Technical source • Induced during extraction, labelling, and hybridization of samples • Printing tip problems • Measurement source • Different DNA conc. • Scanner problem

  6. Why Normalize Data • To recognize the biological information in data. • To compare data from one array to another. • In practice we do not understand the data – inevitably some biology will be removed too.

  7. Normalization methods • Methods of elements selections • Housekeeping genes • All elements • Using Spiked control • Methods to calculate normalization factor • Log ratio • Lowess • Ratio statistics

  8. Clustering • For a sample of size “n” described by a d-dimensional feature space, clustering is a procedure that • Divides the d-dimensional features in K-disjoint groups in such a way that the data points within each group are more similar to each other than to any other data point in other group.

  9. Clustering algorithms • Unsupervised – without a priory biological information • Agglomerative – Hierarchical • Divisive – K-means, SOM • Supervised – a priory biological knowledge • Support vector machine (SVM)

  10. Hierarchical clustering (HC) • Agglomerative technique • steps • The pair-wise distance is calculated between all genes. • The two genes with shortest distance are grouped together to form a cluster. • Then two closest cluster are merged together, to form a new cluster. • The distances are calculated between this new cluster and all other clusters • Steps 2 to 4 are repeated until all the objects are in one cluster.

  11. HC contd. • Data table

  12. HC contd. • Calculation of distance matrix using data table. Experiment » Axis Log ratio of genes » Coordinates • For n-experiments n dimensional space

  13. HC contd. • Distance between genes • Euclidean distance • Pearson correlation • Semi-metric distance – Vector angle • Metric distance – Manhattan or City block

  14. HC contd. • Distance between clusters • Single linkage clustering • Complete linkage clustering • Average linkage clustering • UPGMA • Weighted pair-group average • Within-groups clustering • Ward’s method

  15. HC contd. • The result of HC displayed as branching tree diagram called “Dendrogram”. • Pros and cons of HC • Easy to implement, quick visualization of data set. • Ignores negative associations between genes, falls in category of greedy algorithms.

  16. K-means Clustering • Divisive approach • Steps • Specify K-initial clusters and find their centroid. • For each data point the distance to each centroid is calculated. • Each data point is assigned to its nearest centroid. • Centroids are shifted to the center of data points assigned to it. • Steps 2-4 is iterated until centroid are not shifted anymore.

  17. K-means clustering contd. • Pros and Cons • No dendrogram • It is a powerful method if one has prior idea about the no. of cluster, so it works well with PCA.

  18. Future Work • It includes similar analysis on • Self Organizing Map (SOM) • Support Vector Machine (SVM) • Relevance Network • Gene Shaving • Self Organizing Tree Analysis (SOTA) • Cluster Affinity Search Technique (CAST)

  19. Acknowledgements • Institute of Bioinformatics and Applied Biotechnology (IBAB), Bangalore • Dr. Ashwini K Heerekar (Siri Technologies Pvt. Ltd, Bangalore) • Dr. Jonnlagada Srinivas (Siri Technologies Pvt. Ltd, Bangalore) • Mr. Kiran Kumar (Siri Technologies Pvt. Ltd, Bangalore) • Mr. Mahantha Swamy MV. (Siri Technologies Pvt. Ltd, Bangalore)

  20. Selected references: • A Biologist Guide to Analysis of DNA Microarray DATA, by Steen Knudsen • DNA Microarrays And Gene Expression from experiment to data analysis and modeling, by P. Baldi and G. Wesely • Papers: • Computational Analysis of Microarray Data by John Quackenbush, Nature Genetics Review, June 2001, vol2. • The use and analysis of Microarray Data by Atul Butte, Nature Review drug discovery, Dec 2002, vol1. • Microarray Data Normaliation and Transformation by John Quackenbush, Nature Genetics.

  21. Questions ???

  22. Thank You

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