Title: Nested Effects Models
1Nested Effects Models
- Rainer Spang
- 22.11.2007 Annual Conference of the Working Group
- Statistical Methods in
Bioinformatics
2Signal transduction in the cell
External Signal
Non-transcriptional signaling Difficult to observe
Transcription / Gene Expression Easy to observe
with Microarrays
Apoptosis
3Signaling and External Interventions
Introducing constitutive signals by transfecting
activated genes into the cells
Blocking signaling flow by RNAi
4Traces 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
5Nested 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.
6The 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
7How 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
8Scoring observed silencing effects
9Marginalization
Markowetz F., Bloch J., Spang R.,
Non-transcriptional pathway features
reconstructed from secondary effects of RNA
interference. Bioinformatics 21, 4026-4032, 2005
10The model search space
We have a likelihood for each core model ? find
the maximum likelihood model
11Pairs of genes
Fit a model for every pair of genes Pick the best
of the four possible models
Problem transitivity lost
12Triples 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
13Reconstruction accuracy in simulations
14The Boutros et al Data
ML-Model
Estimated Effects
Raw Data
15The Likelihood Landscape
16Parameter Dependence
17Nested Effects and Clustering
18BCR-signaling in immature B-cells
Tze et al PLoS Biology 2005
19Posterior E-gene position
20Posterior E-gene position
NF-KB
21Posterior E-gene position
Burkitt Lymphoma Marker
22Acknowledgements
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
23Thank You