independent components analysis of starch deficient pgm mutants n.
Download
Skip this Video
Download Presentation
Independent components analysis of starch deficient pgm mutants

Loading in 2 Seconds...

play fullscreen
1 / 31

Independent components analysis of starch deficient pgm mutants - PowerPoint PPT Presentation


  • 132 Views
  • Uploaded on

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.

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about 'Independent components analysis of starch deficient pgm mutants' - questa


Download Now 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.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript
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

Matthias Maneck - Journal Club WS 04/05

overview
Overview
  • Introduction
  • Methods
    • PCA – Principal Component Analysis
    • ICA – Independent Component Analysis
    • Kurtosis
  • Results
  • Summary

Matthias Maneck - Journal Club WS 04/05

introduction techniques
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

introduction techniques1
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

introduction experiments
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

introduction workflow
Introduction – workflow

Data

PCA

ICA

Kurtosis

ICs

Matthias Maneck - Journal Club WS 04/05

pca principal component analysis
PCA – principal component analysis

Matthias Maneck - Journal Club WS 04/05

pca principal component analysis1
PCA – principal component analysis

1. Principal Component

2. Principal Component

Matthias Maneck - Journal Club WS 04/05

pca principal component analysis2
PCA – principal component analysis

Matthias Maneck - Journal Club WS 04/05

pca calculation
PCA – calculation

Matthias Maneck - Journal Club WS 04/05

pca dimensionality reduction
PCA – dimensionality reduction

Selected Components

Data Matrix

Reduced Data Matrix

=

Matthias Maneck - Journal Club WS 04/05

pca principal component analysis3
PCA – principal component analysis

1. Principal Component

2. Principal Component

Matthias Maneck - Journal Club WS 04/05

pca principal component analysis4
PCA – principal component analysis

Matthias Maneck - Journal Club WS 04/05

pca principal component analysis5
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

ica independent component analysis
ICA – independent component analysis
  • microphone signals are mixed speech signals

Matthias Maneck - Journal Club WS 04/05

ica independent component analysis1
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

ica independent component analysis2
ICA – independent component analysis

The sum of distribution of the same time is more Gaussian.

Matthias Maneck - Journal Club WS 04/05

ica independent component analysis3
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

kurtosis significant components
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

kurtosis significant components1
Kurtosis – significant components

Matthias Maneck - Journal Club WS 04/05

influence values
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

influence values1
Influence Values

Influence Matrix

Demixing Matrix

Selected Components

=

ICs

Influence Matrix

Data Matrix

=

Matthias Maneck - Journal Club WS 04/05

results
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

results pgm mutant
Results – pgm mutant

Matthias Maneck - Journal Club WS 04/05

results continuous night
Results – continuous night

Matthias Maneck - Journal Club WS 04/05

results combined
Results – combined

Matthias Maneck - Journal Club WS 04/05

results combined1
Results – combined

Matthias Maneck - Journal Club WS 04/05

results combined2
Results – combined

Matthias Maneck - Journal Club WS 04/05

summary
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

references
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