MicroArray Data Analysis Candice Quadros & Amol Kothari

MicroArray Data Analysis Candice Quadros & Amol Kothari

MicroArray Data Analysis Candice Quadros & Amol Kothari Neural Network for classification Harnessing the power of a neural network for classifying samples. Neural Network for

classification Reduce the no. of genes : We have to reduce the data dimensionality, i.e. reduce the no. of genes to consider. PCA can be used to select most informative genes, but it is computationally expensive to obtain the Eigen vectors for high dimensional

data. Use the method suggested by Golub et al. to obtain the informative genes. Neural Network for classification Steps in classification

Obtain the informative genes using Golubs method. Normalize the genes by shifting them to the mean & dividing by the standard deviation. Train the neural network by using the training data & targets, and get the weights. Classify the test data using the weights obtained above. Neural Network for classification

Results obtained: Inform. Genes No. of Hidden Units NN: Accuracy Golub: Accuracy 100

3 70.55 61.76 200 13 76.74 58.82

Hierarchical Merging: When to stop? Question: When to stop the merging? Suggested Solutions: Diameter(C) MaxD

Avg(sim(Oi,Oj)) (Oi,Oj C) Difficult to estimate the parameters in high dimensions. Hierarchical Merging: When to stop?

Another solution: When m clusters are present, stop merging. Problem: The m clusters might contain single point clusters. Use the concept of MinPts (from DBScan). A set of points is a significant cluster only if the set has MinPts. When there are m significant clusters, then stop. Hierarchical Merging: When to stop? No. of Significant Clusters

No. of iterations Visualization of data: Vizstruct Visualization of data: Vizstruct Equation used: How do weigh each dimension, i.e. how do

we select ? Default value = 0.5 Use the Eigen Values of each dimension to obtain the value of . Visualization of data: Vizstruct Steps for visualization:

Project the data into Eigen space. The Eigen values of each dimension i = i Now use the same formulae for calculating the 2D point: Where i = Eigen value of the ith dimension Visualization of data: Vizstruct Results:

The visualization obtained by this method is more representative of the data, compared to Vizstruct. Demo Thank You

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