Learning rule based models from gene expression time profiles annotated with gene ontology terms
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Learning rule-based models from gene expression time profiles annotated with Gene Ontology terms. Jan Komorowski and Astrid Lägreid. Joint work with. Torgeir R. Hvidsten, Herman Midelfart, Astrid Lægreid and Arne K. Sandvik. Selected Challenges in Gene-expression Analysis.

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Learning rule-based models from gene expression time profiles annotated with Gene Ontology terms

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Learning rule based models from gene expression time profiles annotated with gene ontology terms

Learning rule-based models from gene expression time profiles annotated with Gene Ontology terms

Jan Komorowski and

Astrid Lägreid


Joint work with

Joint work with

  • Torgeir R. Hvidsten, Herman Midelfart, Astrid Lægreid and Arne K. Sandvik

J. Komorowski and A. Lägreid


Selected challenges in gene expression analysis

Selected Challenges in Gene-expression Analysis

  • Function similarity corresponds to expression similarity but:

    • Functionally corelated genes may be expression-wise dissimilar (e.g. anti-coregulated)

    • Genes usually have multiple function

    • Measurements may be approximate and contradictory

  • Can we obtain clusters of biologically related genes?

  • Can we build models that classify unknown genes to functional classes, that are human legible, and that handle approximate and often contradictory data?

  • How can we re-use biological knowledge?

J. Komorowski and A. Lägreid


Learning rule based models from gene expression time profiles annotated with gene ontology terms

Data

  • Data material

    • Serum starved fibroblasts, 8,613 genes

      • Added serum to medium at time = 0

      • Used starved fibroblasts as reference

      • Measured gene activity at various time points

    • 493 genes found to be differentially expressed

  • Results

    • 278 genes known (3 repeats)

    • 212 genes unknown, (uncharacterized)

    • 211 genes given hypothetical function with 88% quality

J. Komorowski and A. Lägreid


Fibroblast serum response

0

1

4

8

24

quiescent

non-proliferating

proliferating

Fibroblast - serum response

samples for

microarray

analysis

serum

J. Komorowski and A. Lägreid


Processes

0

1

4

8

24

quiescent

non-proliferating

proliferating

Processes

re-entry

cell cycle

stress response

protein synthesis

organelle

biogenesis

transcription

cell

motility

lipid synthesis

J. Komorowski and A. Lägreid


Dynamic processes

0

1

4

8

24

quiescent

non-proliferating

proliferating

Dynamic processes

delayed

immediate

early

late

immediate

early

intermediate

primary

secondary

tertiary

J. Komorowski and A. Lägreid


Protein appears after the transcript

0

1

4

8

24

quiescent

non-proliferating

Protein appears after the transcript

primary

secondary

tertiary

proliferating

J. Komorowski and A. Lägreid


Protein dynamics are not always similar to transcript dynamics

0

1

4

8

24

Protein dynamics are not always similar to transcript dynamics

gene

transcript

protein

J. Komorowski and A. Lägreid


Molecular mechanisms of transcriptional response

Molecular mechanisms of transcriptional response

serum

= signal

effectors

= cellular

response

secondary

transcription

factors

immediate early

response factors

intermediate/late

response genes

delayed

immediate early

response genes

immediate early

response genes

J. Komorowski and A. Lägreid


Learning rule based models from gene expression time profiles annotated with gene ontology terms

The dynamics of cellular processes

stress response

cell motility

cell adhesion

DNA synthesis

energy metabolism

protein synthesis

cell cycle regulation

1

4

8

24

DNA synthesis

cell motility

lipid synthesis

cell proliferation, negative regulation

quiescent

non-proliferating

proliferating

J. Komorowski and A. Lägreid


Methodology

Methodology

1. Mining functional classes from an ontology

2. Extracting features for learning

3. Inducing minimal decision rules using rough sets

0 - 4(Increasing) AND 6 - 10(Decreasing)

AND 14 - 18(Constant) => GO(cell proliferation)

!

4. The function of unknown genes is predicted using the rules

J. Komorowski and A. Lägreid


Gene ontology

Gene Ontology

J. Komorowski and A. Lägreid


Biological processes from go

Biological processes from GO

Amino acid and derivative metabolism

Protein targeting

Energy pathways

DNA metabolism

Lipid metabolism

Transport

Ion hemostasis

Intracellular traffic

Organelle organization and biogenesis

Cell death

Cell motility

Stress response

Cell surface receptor linked signal transduction

Oncogenesis

Cell cycle

Cell adhesion

Intracellular signaling cascade

Developmental processes

Blood coagulation

Circulation

J. Komorowski and A. Lägreid


Hierchical clustering of the fibroblast data

Hierchical Clustering of the Fibroblast Data

It’s not a cluster!

J. Komorowski and A. Lägreid


Gene ontology vs clusters found by iyer et al

Gene Ontology vs. Clusters found by Iyer et al.

J. Komorowski and A. Lägreid


Template based feature synthesis

Template-based feature synthesis

12 measurement points, 55 possible intervals of length >2

J. Komorowski and A. Lägreid


Examples of template definitions

Examples of template definitions

J. Komorowski and A. Lägreid


Rule example 1

Rule example 1

J. Komorowski and A. Lägreid


Rule example 2

Rule example 2

J. Komorowski and A. Lägreid


Classification using template based rules

Classification using template-based rules

IF … THEN …

IF … THEN …

IF … THEN …

IF … THEN …

IF … THEN …

IF … THEN …

IF … THEN …

IF 0 - 4(Constant) AND 0 - 10(Increasing) THEN GO(prot. met. and mod.) OR …

IF … THEN

IF … THEN …

IF … THEN …

IF … THEN …

IF … THEN …

IF … THEN …

IF … THEN …

IF … THEN …

IF … THEN …

+4

Votes are normalized and processes with vote fractions higher than a selection-threshold are chosen as predictions

J. Komorowski and A. Lägreid


Cross validation estimates iyer et al

Cross validation estimates Iyer et al.

A:

Coverage: 84%

Precision: 50%

B:

Coverage: 71%

Precision: 60%

C:

Coverage: 39%

Precision: 90%

Coverage = TP/(TP+FN)

Precision = TP/(TP+FP)

J. Komorowski and A. Lägreid


Cross validation estimates cho et al

Cross validation estimates Cho et al.

Coverage: 58%

Precision: 61%

Coverage = TP/(TP+FN)

Precision = TP/(TP+FP)

J. Komorowski and A. Lägreid


Protein metabolism and modification

Protein Metabolism and Modification

A

B

C

D

E

A – annotations

B – false negatives

C – false positives

D – true positives

E – pred. unknown gene

J. Komorowski and A. Lägreid


Re classification of the known genes

Re-classification of the Known Genes

J. Komorowski and A. Lägreid


Co classifications for the unknown genes

Co-classifications for the Unknown Genes

J. Komorowski and A. Lägreid


Conclusions

Conclusions

  • Our methodology

    • Incorporates background biological knowledge

    • Handles well the noise and incompleteness in the microarray data

    • Can be objectively evaluated

    • Predicts multiple functions per gene

    • Can reclassify known genes and provide possible new functions of the known genes

    • Can provide hypotheses about the function of unknown genes

  • Experimental work needs to be done to confirm our predictions

J. Komorowski and A. Lägreid


Genomic rosetta http www idi ntnu no aleks rosetta

Genomic ROSETTA:http://www.idi.ntnu.no/~aleks/rosetta

J. Komorowski and A. Lägreid


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