Nested Effects Models

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Nested Effects Models

• Rainer Spang
• 22.11.2007 Annual Conference of the Working Group
  
• Statistical Methods in
  Bioinformatics

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Signal transduction in the cell
External Signal
Non-transcriptional signaling Difficult to observe
Transcription / Gene Expression Easy to observe
with Microarrays
Apoptosis
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Signaling and External Interventions
Introducing constitutive signals by transfecting
activated genes into the cells
Blocking signaling flow by RNAi
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Traces of a Bifurcation
Module 1
E-Genes
Module 2
From Boutros M, Agaisse H, Perrimon N.
Sequential activation of signaling pathways
during innate immune responses in drosophila.
Developmental Cell, 3(5)711722, 2002
S-Genes
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Nested effects models (NEMs)
Negative Controls (C-) Positive Controls
(C) Interventions in S-Genes (RNAi) Observations
in E-Genes (Microarray) Silencing Effect An
E-gene goes from a C level back to a C- level
Data Binary matrix D (eik), where eik 1 if
E-gene Ei shows in experiment k the same
expression as in the negative controls.
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The core model and the extended model
We only want to infer the core topology The
position of the yellow edges is unkown too, but
we treat it as a nuisance parameter
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How does data generated by the model look like?

• Model Assumptions
• The core model is transitive
• Every E-gene is connected to exactly one S- gene
• - Independent binary noise

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Scoring observed silencing effects
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Marginalization
Markowetz F., Bloch J., Spang R.,
Non-transcriptional pathway features
reconstructed from secondary effects of RNA
interference. Bioinformatics 21, 4026-4032, 2005
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The model search space
We have a likelihood for each core model ? find
the maximum likelihood model
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Pairs of genes
Fit a model for every pair of genes Pick the best
of the four possible models
Problem transitivity lost
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Triples of genes
Fit one model for each triple of genes Pick the
best from the 29 possible models
Edgewise model averaging
Edges that are frequently used in the triple
models are included in the final model
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Reconstruction accuracy in simulations
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The Boutros et al Data
ML-Model
Estimated Effects
Raw Data
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The Likelihood Landscape
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Parameter Dependence
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Nested Effects and Clustering
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BCR-signaling in immature B-cells
Tze et al PLoS Biology 2005
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Posterior E-gene position
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Posterior E-gene position
NF-KB
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Posterior E-gene position
Burkitt Lymphoma Marker
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Acknowledgements
Claudio Lottaz Juby Jacob Stefan Bentink
BCR-Signaling, COMAPs Benedikt Anchang Inka
Appelt Christian Kohler Mohammad Sadeh Mathias
Maneck Maria Bartolim ----------------------------
--------------------------- Florian Markowetz
NEMs Jörn Tödling Xinan Yang Jochen Jäger
Stefanie Scheid Dennis Kostka
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Thank You