1 / 31

Independent components analysis of starch deficient pgm mutants

Independent components analysis of starch deficient pgm mutants. GCB 2004 M. Scholz, Y. Gibon, M. Stitt, J. Selbig. Overview. Introduction Methods PCA – Principal Component Analysis ICA – Independent Component Analysis Kurtosis Results Summary. Introduction – techniques.

moeshe
Download Presentation

Independent components analysis of starch deficient pgm mutants

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Independent components analysis of starch deficient pgm mutants GCB 2004 M. Scholz, Y. Gibon, M. Stitt, J. Selbig Matthias Maneck - Journal Club WS 04/05

  2. Overview • Introduction • Methods • PCA – Principal Component Analysis • ICA – Independent Component Analysis • Kurtosis • Results • Summary Matthias Maneck - Journal Club WS 04/05

  3. Introduction – techniques • visualization techniques • supervised • biological background information • unsupervised • present major global information • General questions about the underlying data structure. • Detect relevant components independent from background knowledge. Matthias Maneck - Journal Club WS 04/05

  4. Introduction – techniques • PCA • dimensionality reduction • extracts relevant information related to the highest variance • ICA • Optimizes independence condition • Components represent different non-overlapping information Matthias Maneck - Journal Club WS 04/05

  5. Introduction - experiments • Micro plate assays of enzymes form Arabidopsis thaliana. • pgm mutant vs. wild type • continuous night • data Matthias Maneck - Journal Club WS 04/05

  6. Introduction – workflow Data PCA ICA Kurtosis ICs Matthias Maneck - Journal Club WS 04/05

  7. PCA – principal component analysis Matthias Maneck - Journal Club WS 04/05

  8. PCA – principal component analysis 1. Principal Component 2. Principal Component Matthias Maneck - Journal Club WS 04/05

  9. PCA – principal component analysis Matthias Maneck - Journal Club WS 04/05

  10. PCA – calculation Matthias Maneck - Journal Club WS 04/05

  11. PCA – dimensionality reduction Selected Components Data Matrix Reduced Data Matrix = Matthias Maneck - Journal Club WS 04/05

  12. PCA – principal component analysis 1. Principal Component 2. Principal Component Matthias Maneck - Journal Club WS 04/05

  13. PCA – principal component analysis Matthias Maneck - Journal Club WS 04/05

  14. PCA – principal component analysis • Minimizes correlation between components. • Components are orthogonal to each other. • Delivers transformation matrix, that gives the influence of the enzymes on the principal components. • PCs ordered by size of eigenvalues of cov-matrix Reduced Data Matrix Selected Components Data Matrix = Matthias Maneck - Journal Club WS 04/05

  15. ICA – independent component analysis • microphone signals are mixed speech signals Matthias Maneck - Journal Club WS 04/05

  16. ICA – independent component analysis Microphone Signals X Mixing Matrix A Speech Signals S = Demixing matrix A-1 Microphone signals X Speech signals S = Matthias Maneck - Journal Club WS 04/05

  17. ICA – independent component analysis The sum of distribution of the same time is more Gaussian. Matthias Maneck - Journal Club WS 04/05

  18. ICA – independent component analysis • Maximizes independence (non Gaussianity) between components. • ICA doesn’t work with purely Gaussian distributed data. • Components are not orthogonal to each other. • Delivers transformation matrix, that gives the influence of the PCs on the independent components. • ICs are unordered ICs Demixing Matrix Data Matrix = Matthias Maneck - Journal Club WS 04/05

  19. Kurtosis – significant components • measure of non Gaussianity • z – random variable (IC) • μ – mean • σ – standard deviation • positive kurtosis  super Gaussian • negative kurtosis  sub Gaussian Matthias Maneck - Journal Club WS 04/05

  20. Kurtosis – significant components Matthias Maneck - Journal Club WS 04/05

  21. Influence Values • Which enzymes have most influence on ICs? Reduced Data Matrix Selected Components Data Matrix = ICs Demixing Matrix Data Matrix = Matthias Maneck - Journal Club WS 04/05

  22. Influence Values Influence Matrix Demixing Matrix Selected Components = ICs Influence Matrix Data Matrix = Matthias Maneck - Journal Club WS 04/05

  23. Results • pgm mutant • compares wild type and pgm mutant • 17 enzymes,125 samples • wild type, pgm mutant • continuous night • response to carbon starvation • 17 enzymes, 55 samples • +0, +2, +4, +8, +24, +48, +72, +148 h Matthias Maneck - Journal Club WS 04/05

  24. Results – pgm mutant Matthias Maneck - Journal Club WS 04/05

  25. Matthias Maneck - Journal Club WS 04/05

  26. Results – continuous night Matthias Maneck - Journal Club WS 04/05

  27. Results – combined Matthias Maneck - Journal Club WS 04/05

  28. Results – combined Matthias Maneck - Journal Club WS 04/05

  29. Results – combined Matthias Maneck - Journal Club WS 04/05

  30. Summary • ICA in combination with PCA has higher discriminating power than only PCA. • Kurtosis is used for selection optimal PCA dimension and ordering of ICs. • pgm experiment, 1st IC discriminates between mutant and wild type. • Continuous night, 2nd IC represents time component. • The two most strongly implicated enzymes are identical. Matthias Maneck - Journal Club WS 04/05

  31. References • Scholz M., Gibon Y., Stitt M., Selbig J.: Independent components analysis of starch deficient pgm mutants. • Scholz M., Gatzek S., Sterling A., Fiehn O., Selbig J.: Metabolite fingerprinting: an ICA approach. • Blaschke, T., Wiskott, L.: CuBICA: Independent Component Analysis by Simultaneous Third- and Fourth-Order Cumulant Diagonalization. IEEE Transactions on Signal Processing, 52(5):1250-1256.http://itb.biologie.hu-berlin.de/~blaschke/ • Hyvärinen A., Karhunen J., Oja E.: Independent Component Analysis. J. Wiley. 2001. Matthias Maneck - Journal Club WS 04/05

More Related