Classification of microarray samples Tim Beißbarth Mini-Group Meeting 8.7.2002.

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Classification of microarray samples Tim Beißbarth Mini-Group Meeting

Papers in PNAS May 2002  Diagnosis of multiple cancer types by shrunken centroids of gene expression Robert Tibshirani,Trevor Hastie, Balasubramanian Narasimhan, and Gilbert Chu  Selection bias in gene extraction on the basis of microarray gene- expression data Christphe Ambroise, and Geoffrey J. McLachlan

DNA Microarray Hybridization

Tables of Expression Data Tissue information Gene 1 Expression levels Gene 2 Table of expression levels:

The Classification Problem Classification Methods: Support Vector Machines, Neural Networks, Fishers linear descriminant, etc.

Heat map of the chosen 43 genes.

Steps in classification  Feature selection  Training a classification rule Problem:  For microarray data there are many more features (genes) than there are training samples and conditions to be classified.  Therefore usually a set of features which discriminates the conditions perfectly can be found (overfitting)

Feature selection  Criterion is independent of the prediction rule (filter approach)  Criterion depends on the prediction rule (wrapper approach) Goal:  Feature set must not be to small, as this will produce a large bias towards the training set.  Feature set must not be to large, as this will include noise which does not have any discriminatory power.

Methods to evaluate classification  Split Training-Set vs. Test-Set: Disadvantage: Looses a lot of training data.  M-fold cross-validation: Divide in M subsets, Train on M-1 subsets, Test on 1 subset Do this M-times and calculate mean error Special case: m=n, leave-one out cross-validation  Bootstrap Important!!!  Feature selection needs to be part of the testing and may not be performed on the complete data set. Otherwise a selection bias is introduced.

Tibshirani et al, PNAS, 2002

Conclusions  One needs to be very carefull when interpreting test and cross-validation results.  The feature selection method needs to be included in the testing.  10-fold cross-validation or bootstrap with external feature selection.  Feature selection has more influence on the classification result than the classification method used.

The End