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Achim Tresch Computational Biology ‘Omics’ - Analysis of high dimensional Data

Achim Tresch Computational Biology

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‘Omics’ - Analysis of high dimensional Data. Achim Tresch Computational Biology. Topics. Hypergeometric test [Khatri and Draghici 2005] Kolmogorov-Smirnov test [Subramanian et al. 2005]. Gene Set Enrichment. Fisher‘s exact test, once more. Fisher‘s exact test, once more. - PowerPoint PPT Presentation

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Page 1: Achim Tresch Computational Biology

Achim TreschComputational Biology

‘Omics’

- Analysis of high

dimensional Data

Page 2: Achim Tresch Computational Biology

Topics

• Hypergeometric test [Khatri and Draghici 2005]

• Kolmogorov-Smirnov test [Subramanian et al. 2005]

Page 3: Achim Tresch Computational Biology

Gene Set Enrichment

Page 4: Achim Tresch Computational Biology

Fisher‘s exact test, once more

Page 5: Achim Tresch Computational Biology

Fisher‘s exact test, once more

Page 6: Achim Tresch Computational Biology

Gene Ontology Example

559

Page 7: Achim Tresch Computational Biology

Gene Ontology Example

(immune response) (macromolecule biosynthesis)

Page 8: Achim Tresch Computational Biology

Kolmogorov-Smirnov Test

< 10-10

• Move 1/K up when you see a gene from group a

• Move 1/(N-K) down when you see a gene not in group a

Page 9: Achim Tresch Computational Biology

Topics

Page 10: Achim Tresch Computational Biology

GO scoring: general problem

Page 11: Achim Tresch Computational Biology

GO Independence Assumption

light yellow

GO sets

Page 12: Achim Tresch Computational Biology

GO Independence Assumption

light yellow

Page 13: Achim Tresch Computational Biology

The elim method

Page 14: Achim Tresch Computational Biology

The elim method

Top 10 significant nodes (boxes) obtained with

the elim method

Page 15: Achim Tresch Computational Biology

The weight method

Page 16: Achim Tresch Computational Biology

The weight method

Page 17: Achim Tresch Computational Biology

The weight method

(x)

(x)}

Page 18: Achim Tresch Computational Biology

The weight method

Top 10 significant nodes (boxes) obtained with

the elim method

Page 19: Achim Tresch Computational Biology

Algorithms Summary

Page 20: Achim Tresch Computational Biology

Topics

Page 21: Achim Tresch Computational Biology

Top scoring GO term

Significant GO terms in

the ALL dataset

Page 22: Achim Tresch Computational Biology

Advantages & Disadvantages for ALL

Page 23: Achim Tresch Computational Biology

Prostate cancer progression

838210)log( chromhypochromhypo IIIIg

Page 24: Achim Tresch Computational Biology

Prostate cancer progression

Page 25: Achim Tresch Computational Biology

Prostate cancer progression

Page 26: Achim Tresch Computational Biology

Influence of the p-values adjustment

Page 27: Achim Tresch Computational Biology

Simulation Study

Introduce noise

Page 28: Achim Tresch Computational Biology

Simulation Study

Page 29: Achim Tresch Computational Biology

Simulation Study

Page 30: Achim Tresch Computational Biology

Quality of GO scoring methods

10% noise level 40% noise level

Page 31: Achim Tresch Computational Biology

Summary

Page 32: Achim Tresch Computational Biology

Acknowledgements

Adrian AlexaMPI Saarbrücken