Proteomic Mass Spectrometry

1. Proteomic Mass Spectrometry

2. Outline • Previous Research • Project Goals • Data and Algorithms • Experimental Results • Conclusions • To Do List

3. Motivation • MS spectra has high dimension • Most ML algorithms are incapable of handling such high dimensional data • Dimensionality Reduction (DR) • Preserve as much information as possible, while reducing the dimensionality. • Feature Extraction (FE) • Removal of irrelevant and/or redundant features (information)

4. Previous research • Usually applies DR then FE • Does Order matter ? DR: Down Sampling, PCA, Wavelets FE: T-Test, Random Forests, Manual Peak Extraction • In [conrads03] show that high resolution MS spectra produces better classification accuracy. • Most previous research down samples spectra CONJECTURE: Down Sampling detrimental to performance.

5. Project Goals • Test Down Sampling Conjecture • Compare FE algorithms (NOTE: Optimal FE is NP-hard !) • Use a simple but fast classifier to test a number of FE approaches • Test across different data sets • Are there any clearly superior FE algorithms ?

6. Three Data Sets Heart/Kidney (100/100) • 164,168 features, 2 classes Ovarian Cancer (91/162) • 15,154 features, 2 classes Prostate Cancer (63/190/26/43) • 15,154 features, 4 classes • Normal, Benign, Stage 1, Stage 2 Cancer • Transformed into Normal/Benign Vs Cancer (1&2)

7. Algorithms Centroid Classifier • given class means P, Q and sample point s C = argmin (d(P,s), d(Q,s)) P Q d(Q,s) d(P,s)

8. Algorithms • T-test – do the means of 2 distr. Differ ? • KS-test – do the cdf differ ? • Composite – (T-test)*(KS-test) • IFE - Individual Feature Evaluation using the centroid classifier • DPCA – discriminative principle component analysis

9. Preliminary Experiments • Compare normalization approaches • Compare similarity metrics • Cross correlation • (-L1) • Angular • Across 3 data sets => 27 configurations

10. Preliminary Experiments (cont) • No single norm/metric clearly superior on all data sets • 2-5% increase in performance if suitable normalization and similarity metric chosen (can be up to 10% increase) • L1-norm with angle similarity metric worked well on Heart/Kidney and Ovarian Cancer sets (easy sets) • L1-norm and L1-metric best on Prostate 2-class problem (hard set).

11. Down Sampling

12. Statistical Tests • T-test, KS-test, Composite • Ranks features in terms of relevance • SFS – Sequential Forward Selection • Selects ever increasing feature sets • I.e., {1}; {1,2}; {1,2,3}; {1,2,3,4}

13. Heart/Kidney

14. Ovarian Cancer

15. Prostate Cancer

16. Single Feature Classification • Use each feature to classify test samples • Rank features in terms of performance • SFS

17. Performance Comparison

18. Performance Comparison

19. Performance Comparison

20. Summary • For each data set, for each FE algorithm ran 15,000 3-fold cross validation experiments. • Total of 810,000 FE experiments ran • DE experiments ~ 100,000 experiments • Additional 50,000 experiments using DPCA classifier • did not produce significantly different results than the centroid classifier

21. Conclusions • HK and Ovarian Data sets considerably easier to classify than Prostate Cancer • Feature Extraction (in general) significantly improves performance on all data sets • No single technique superior on all data sets. • Best Performance using SFS with feature weighting • Smallest feature set with T-test of KS-Test • Composite test inferior to all others. • Down Sampling appears to be detrimental • What about other Dim. Red. Techniques ? • E.g. PCA and Wavelets

22. Conclusions • Down Sampling appears to be detrimental • What about other Dim. Red. Techniques ? • E.g. PCA and Wavelets • What about FE after Down Sampling ? • On Prostate data performance appears to drop w.r.t. to best single feature.

23. To Do List • Check PCA, Wavelets and other DR techniques • Use other (better) classifiers • General Hypothesis • Use a simple fast classifier together with FE techniques to extract a good feature set • Replace classifier with a more effective one. • Need to verify that other classifiers respond well to the extracted features.