1. Object Orie’d Data Analysis, Last Time Gene Cell Cycle Data Microarrays and HDLSS visualization DWD bias adjustment NCI 60 Data Today: More NCI 60 Data & Detailed (math ’ cal) look at PCA

2. Last Time: Checked Data Combo, using DWD Dir ’ ns

3. DWD Views of NCI 60 Data Interesting Question: Which clusters are really there? Issues: DWD great at finding dir’ns of separation And will do so even if no real structure Is this happening here? Or: which clusters are important? What does “important” mean?

4. Real Clusters in NCI 60 Data Simple Visual Approach: Randomly relabel data (Cancer Types) Recompute DWD dir’ns & visualization Get heuristic impression from this Deeper Approach Formal Hypothesis Testing (Done later)

5. Random Relabelling #1

6. Random Relabelling #2

7. Random Relabelling #3

8. Random Relabelling #4

9. Revisit Real Data

10. Revisit Real Data (Cont.) Heuristic Results: Strong Clust’s Weak Clust’s Not Clust’s Melanoma C N S NSCLC Leukemia Ovarian Breast Renal Colon Later: will find way to quantify these ideas i.e. develop statistical significance

11. NCI 60 Controversy Can NCI 60 Data be normalized? Negative Indication: Kou, et al (2002) Bioinformatics, 18, 405- 412. –Based on Gene by Gene Correlations Resolution: Gene by Gene Data View vs. Multivariate Data View

12. Resolution of Paradox: Toy Data, Gene View

13. Resolution: Correlations suggest “ no chance ”

14. Resolution: Toy Data, PCA View

15. Resolution: PCA & DWD direct ’ ns

16. Resolution: DWD Adjusted

17. Resolution: DWD Adjusted, PCA view

18. Resolution: DWD Adjusted, Gene view

19. Resolution: Correlations & PC1 Projection Correl ’ n

20. Needed final verification of Cross-platform Normal ’ n Is statistical power actually improved? Will study later

21. DWD: Why does it work? Rob Tibshirani Query: Really need that complicated stuff? (DWD is complex) Can’t we just use means? Empirical Fact (Joel Parker): (DWD better than simple methods)

22. DWD: Why does it work? Xuxin Liu Observation: Key is unbalanced sub-sample sizes (e.g biological subtypes) Mean methods strongly affected DWD much more robust Toy Example

23. DWD: Why does it work?

24. Xuxin Liu Example Goals: –Bring colors together –Keep symbols distinct (interesting biology) Study varying sub-sample proportions: –Ratio = 1: Both methods great –Ratio = 0.61: Mean degrades, DWD good –Ratio = 0.35: Mean poor, DWD still OK –Ratio = 0.11: DWD degraded, still better Later: will find underlying theory

25. PCA: Rediscovery – Renaming Statistics: Principal Component Analysis (PCA) Social Sciences: Factor Analysis (PCA is a subset) Probability / Electrical Eng: Karhunen – Loeve expansion Applied Mathematics: Proper Orthogonal Decomposition (POD) Geo-Sciences: Empirical Orthogonal Functions (EOF)

26. An Interesting Historical Note The 1 st (?) application of PCA to Functional Data Analysis: Rao, C. R. (1958) Some statistical methods for comparison of growth curves, Biometrics, 14, 1-17. 1 st Paper with “Curves as Data” viewpoint

27. Detailed Look at PCA Three important (and interesting) viewpoints: 1. Mathematics 2. Numerics 3. Statistics 1 st : Review linear alg. and multivar. prob.

28. Review of Linear Algebra Vector Space: set of “vectors”,, and “scalars” (coefficients), “closed” under “linear combination” ( in space) e.g., “ dim Euclid’n space”

29. Review of Linear Algebra (Cont.) Subspace: subset that is again a vector space i.e. closed under linear combination e.g. lines through the origin e.g. planes through the origin e.g. subsp. “generated by” a set of vector (all linear combos of them = = containing hyperplane through origin)

30. Review of Linear Algebra (Cont.) Basis of subspace: set of vectors that: span, i.e. everything is a lin. com. of them are linearly indep’t, i.e. lin. Com. is unique e.g. “unit vector basis” since

31. Review of Linear Algebra (Cont.) Basis Matrix, of subspace of Given a basis,, create matrix of columns:

32. Review of Linear Algebra (Cont.) Then “linear combo” is a matrix multiplicat’n: where Check sizes:

33. Review of Linear Algebra (Cont.) Aside on matrix multiplication: (linear transformat’n) For matrices, Define the “matrix product” (“inner products” of columns with rows) (composition of linear transformations) Often useful to check sizes:

34. Review of Linear Algebra (Cont.) Matrix trace: For a square matrix Define Trace commutes with matrix multiplication:

35. Review of Linear Algebra (Cont.) Dimension of subspace (a notion of “size”): number of elements in a basis (unique) (use basis above) e.g. dim of a line is 1 e.g. dim of a plane is 2 dimension is “degrees of freedom”

36. Review of Linear Algebra (Cont.) Norm of a vector: in, Idea: “length” of the vector Note: strange properties for high, e.g. “length of diagonal of unit cube” =

37. Review of Linear Algebra (Cont.) Norm of a vector (cont.): “length normalized vector”: (has length one, thus on surf. of unit sphere & is a direction vector) get “distance” as:

38. Review of Linear Algebra (Cont.) Inner (dot, scalar) product: for vectors and, related to norm, via

39. Review of Linear Algebra (Cont.) Inner (dot, scalar) product (cont.): measures “angle between and ” as: key to “orthogonality”, i.e. “perpendicul’ty”: if and only if

40. Review of Linear Algebra (Cont.) Orthonormal basis : All ortho to each other, i.e., for All have length 1, i.e., for

41. Review of Linear Algebra (Cont.) Orthonormal basis (cont.): “Spectral Representation”: where check: Matrix notation: where i.e. is called “transform (e.g. Fourier, wavelet) of ”

42. Review of Linear Algebra (Cont.) Parseval identity, for in subsp. gen’d by o. n. basis : Pythagorean theorem “Decomposition of Energy” ANOVA - sums of squares Transform,, has same length as, i.e. “rotation in ”

43. Gram-Schmidt Ortho-normalization Idea: Given a basis, find an orthonormal version, by subtracting non-ortho part Review of Linear Algebra (Cont.)

44. Projection of a vector onto a subspace : Idea: member of that is closest to (i.e. “approx’n”) Find that solves: (“least squares”) For inner product (Hilbert) space: exists and is unique Review of Linear Algebra (Cont.)

45. Projection of a vector onto a subspace (cont.): General solution in : for basis matrix, So “proj’n operator” is “matrix mult’n”: (thus projection is another linear operation) (note same operation underlies least squares) Review of Linear Algebra (Cont.)

46. Projection using orthonormal basis : Basis matrix is “orthonormal”: So = = Recon(Coeffs of “in dir’n”)

47. Review of Linear Algebra (Cont.) Projection using orthonormal basis (cont.): For “orthogonal complement”,, and Parseval inequality:

48. Review of Linear Algebra (Cont.) (Real) Unitary Matrices: with Orthonormal basis matrix (so all of above applies) Follows that (since have full rank, so exists …) Lin. trans. (mult. by ) is like “rotation” of But also includes “mirror images”

49. Review of Linear Algebra (Cont.) Singular Value Decomposition (SVD): For a matrix Find a diagonal matrix, with entries called singular values And unitary (rotation) matrices, (recall ) so that

50. Review of Linear Algebra (Cont.) Intuition behind Singular Value Decomposition: For a “linear transf’n” (via matrix multi’n) First rotate Second rescale coordinate axes (by ) Third rotate again i.e. have diagonalized the transformation

51. Review of Linear Algebra (Cont.) SVD Compact Representation: Useful Labeling: Singular Values in Increasing Order Note: singular values = 0 can be omitted Let = # of positive singular values Then: Where are truncations of

52. Review of Linear Algebra (Cont.) Eigenvalue Decomposition: For a (symmetric) square matrix Find a diagonal matrix And an orthonormal matrix (i.e. ) So that:, i.e.

53. Review of Linear Algebra (Cont.) Eigenvalue Decomposition (cont.): Relation to Singular Value Decomposition (looks similar?): Eigenvalue decomposition “harder” Since needs Price is eigenvalue decomp’n is generally complex Except for square and symmetric Then eigenvalue decomp. is real valued Thus is the sing’r value decomp. with: