Computational Genomics and Proteomics

1. Computational Genomics and Proteomics • Lecture 9 • CGP Part 2: Introduction Based on: Martin Bachlerslides

2. Overview CGP part 2 • Lesson 9: Introduction, description of assignment, short overview papers • Lesson 10: Mass Spectrometric Data Analysis (Guest lecturer: Thang Pham, VUmc) • Lesson 11: Team+E.M. meeting 1 • Lesson 12: Team+E.M. meeting 2 • Lesson 13: Presentation/Evaluation Part 2

3. CGP part 2 • Main focus of CGP2: Feature Selection • Three papers available for teams’ study • Each team (pair of students) chooses one paper • Critical study of topic/method of the paper (meeting with E.M.) • Proposal of method changes (meeting with E.M.) • Public presentation, discussion, evaluation (all teams) • One paper review on Feature Selection techniques in Bioinformatics: • Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics Bioinformatics. 2007 Oct 1;23(19):2507-17

4. Papers • K. Jong, E. Marchiori, M. Sebag, A. van der Vaart. Feature Selection in Proteomic Pattern Data with Support Vector Machines. CIBCB , pp. 41-48, IEEE, 2004. • K. Ye, A. Feenstra, A.Ijzerman, J. Heringa, E. Marchiori. Multi-RELIEF: a method to recognize specificity determining residues from multiple sequence alignments using a Machine Learning approach for feature weighting.Bioinformatics, 2007. • K. Ye, E.W. Lameijer, M.W. Beukers, and A.P. IJzerman (2006) A two-entropies analysis to identify functional positions in the transmembrane region of class A G protein-coupled receptors. Proteins, Structure, Function and Bioinformatics 63, 1018-1030.

5. Students’ Evaluation • Based on: • Discussions • Report describing topic/method and proposed changes • Presentation

6. Problem: Where to focus attention ? • A universal problem of intelligent (learning) agents is where to focus their attention. • What aspects of the problem at hand are important/necessary to solve it? • Discriminate between the relevant and irrelevant parts of experience.

7. What is Feature selection ? • Feature selection: Problem of selecting some subset of a learning algorithm’s input variables upon which it should focus attention, while ignoring the rest (DIMENSIONALITY REDUCTION) • Humans/animals do that constantly!

8. Feature Selection in ML ? Why even think about Feature Selection in ML? • The information about the target class is inherent in the variables! • Naive theoretical view: More features => More information=> More discrimination power. • In practice: many reasons why this is not the case! • Also:Optimization is (usually) good, so why not try to optimize the input-coding ?

9. Feature Selection in ML ? YES! • Many explored domains have hundreds to tens of thousands of variables/features with many irrelevant and redundant ones! - In domains with many features the underlying probability distribution can be very complex and very hard to estimate (e.g. dependencies between variables) ! • Irrelevant and redundant features can „confuse“ learners! • Limited training data! • Limited computational resources! • Curse of dimensionality!

10. Curse of dimensionality

11. Curse of dimensionality • The required number of samples (to achieve the same accuracy) grows exponentionally with the number of variables! • In practice: number of training examples is fixed! => the classifier’s performance usually will degrade for a large number of features! In many cases the information that is lost by discarding variables is made up for by a more accurate mapping/sampling in the lower-dimensional space !

12. Example for ML-Problem Gene selection from microarray data • Variables: gene expression coefficients corresponding to the amount of mRNA in a patient‘s sample (e.g. tissue biopsy) • Task: Separate healthy patients from cancer patients • Usually there are only about 100 examples (patients) available for training and testing (!!!) • Number of variables in the raw data: 6.000 – 60.000 • Does this work ? ([8]) [8] C. Ambroise, G.J. McLachlan: Selection bias in gene extraction on the basis of microarray gene-expresseion data. PNAS Vol. 99 6562-6566(2002)

13. Example for ML-Problem Text-Categorization • Documents are represented by a vector of dimension the size of the vocabulary containing word frequency counts • Vocabulary ~ 15.000 words (i.e. each document is represented by a 15.000-dimensional vector) • Typical tasks: • Automatic sorting of documents into web-directories • Detection of spam-email

14. Motivation • Especially when dealing with a large number of variables there is a need for dimensionality reduction! • Feature Selection can significantly improve a learning algorithm’s performance!

15. Approaches • Wrapper • feature selection takes into account the contribution to the performance of a given type of classifier • Filter • feature selection is based on an evaluation criterion for quantifying how well feature (subsets) discriminate the two classes • Embedded • feature selection is part of the training procedure of a classifier (e.g. decision trees)

16. Embedded methods • Attempt to jointly or simultaneously train both a classifier and a feature subset • Often optimize an objective function that jointly rewards accuracy of classification and penalizes use of more features. • Intuitively appealing Example: tree-building algorithms Adapted from J. Fridlyand

17. Approaches to Feature Selection Filter Approach Feature Selection by Distance Metric Score Input Features Model Train Model Wrapper Approach Feature Set Feature Selection Search Model Train Model Input Features Importance of features given by the model Adapted from Shin and Jasso

18. Filter methods Feature selection p s Classifier design R R s << p • Features are scored independently and the top s are used by • the classifier • Score: correlation, mutual information, t-statistic, F-statistic, • p-value, tree importance statistic etc Easy to interpret. Can provide some insight into the disease markers. Adapted from J. Fridlyand

19. Problems with filter method • Redundancy in selected features: features are considered independently and not measured on the basis of whether they contribute new information • Interactions among features generally can not be explicitly incorporated (some filter methods are smarter than others) • Classifier has no say in what features should be used: some scores may be more appropriates in conjuction with some classifiers than others. Adapted from J. Fridlyand

20. Dimension reduction: a variant on a filter method • Rather than retain a subset of s features, perform dimension reduction by projecting features onto s principal components of variation (e.g. PCA etc) • Problem is that we are no longer dealing with one feature at a time but rather a linear or possibly more complicated combination of all features. It may be good enough for a black box but how does one build a diagnostic chip on a “supergene”? (even though we don’t want to confuse the tasks) • Those methods tend not to work better than simple filter methods. Adapted from J. Fridlyand

21. Wrapper methods Feature selection p s Classifier design R R s << p • Iterative approach: many feature subsets are scored based • on classification performance and best is used. • Selection of subsets: forward selection, backward selection, • Forward-backward selection, tree harvesting etc Adapted from J. Fridlyand

22. Problems with wrapper methods • Computationally expensive: for each feature subset to be considered, a classifier must be built and evaluated • No exhaustive search is possible (2 subsets to consider) : generally greedy algorithms only. • Easy to overfit. p Adapted from J. Fridlyand

23. Example: Microarray Analysis “Labeled” cases (38 bone marrow samples: 27 AML, 11 ALL Each contains 7129 gene expression values) Train model (using Neural Networks, Support Vector Machines, Bayesian nets, etc.) key genes 34 New unlabeled bone marrow samples Model AML/ALL

24. Microarray Data Challenges : • Few samples for analysis (38 labeled) • Extremely high-dimensional data (7129 gene expression values per sample) • Noisy data • Complex underlying mechanisms, not fully understood

25. Some genes are more useful than others for building classification models Example: genes 36569_at and 36495_at are useful

26. Some genes are more useful than others for building classification models Example: genes 36569_at and 36495_at are useful AML ALL

27. Some genes are more useful than others for building classification models Example: genes 37176_at and 36563_atnot useful

28. Importance of Feature (Gene) Selection • Majority of genes are not directly related to leukemia • Having a large number of features enhances the model’s flexibility, but makes it prone to overfitting • Noise and the small number of training samples makes this even more likely • Some types of models, like kNN do not scale well with many features

29. Classification: CV error N samples • Training error • Empirical error • Error on independent test set • Test error • Cross validation (CV) error • Leave-one-out (LOO) • N-fold CV splitting 1/n samples for testing N-1/n samples for training Count errors Summarize CV error rate

30. Two schemes of cross validation CV1 CV2 N samples N samples LOO feature selection Train and test the feature-selector and the classifier LOO Train and test the classifier Count errors Count errors

31. Difference between CV1 and CV2 • CV1 gene selection within LOOCV • CV2 gene selection before before LOOCV • CV2 can yield optimistic estimation of classification true error • CV2 used in paper by Golub et al. : • 0 training error • 2 CV error (5.26%) • 5 test error (14.7%) • CV error different from test error!

32. Significance of classification results • Permutation test: • Permute class label of samples • LOOCV error on data with permuted labels • Repeat process a high number of times • Compare with LOOCV error on original data: • P-value = (# times LOOCV on permuted data <= LOOCV on original data) / total # of permutations considered

33. Feature Selection techniques in a nutshell WHY ? WHAT ? HOW ? Saeys Y, Inza I, Larrañaga P. A review of feature selection techniques in bioinformatics Bioinformatics. 2007 Oct 1;23(19):2507-17

34. Now it is team’s work! • Meetings: 29/11, 6/12: • each team comes to discuss with E.M. once a week for 30 minutes • In the last lecture (13 Dec): • Report due date • Public talk by each team See you on the 29/11!