Modelling Transcriptional Networks in Plant Senescence · in Plant Senescence 1. Introduction...
Transcript of Modelling Transcriptional Networks in Plant Senescence · in Plant Senescence 1. Introduction...
Modelling Transcriptional Networksin Plant Senescence
1. Introduction❖ Senescence is a highly important process of cell
death and nutrient recycling.❖ It is far more complicated than localised processes
such as cell development.❖ It can be quantified phenotypically by measuring
RUBISCO or chlorophyll levels; but little is known about gene interactions.
4. Data Manipulation❖ Check that no slides show heavy bias between
dyes (possible misprinted slide).❖ Check that the intensity of spots arenʼt saturating
the scanning equipment.❖ Check that technical replicates are representative
of each other.❖ Remove unbalanced dye bias using lowess
transformations across pin-tips and slides.❖ Visually inspect the slides for anomalies.❖ Repeat slides where necessary.
5. Using ANOVA❖ Define an ANOVA model to fit to the data:
Exp. ~ Dye + Array + (Day * ToD) / BioRep
❖ MAANOVA groups data from the experiment, finding contribution levels of each term towards final gene expression.
❖ F-tests on the terms identify which genes change expression significantly across the levels.
❖ Accounting for biological replicate variability gives strength to any conclusions drawn about gene significance.
2. Aims❖ Find genes involved in plant
senescence.❖ Identify interactions between them.❖ Produce and verify accurate
network models of these genes.
3. Planning❖ Use MAANOVA to normalise data.❖ Use MAANOVA to identify significantly changing
genes.❖ Use SplineCluster to find similar expression
profiles between genes.❖ Use Variational Bayes State Space Modelling to
quantify interactions.
Stuart David James McHattie
Systems Biology PhD [email protected]
6. SplineCluster❖ Significantly changing genes are clustered using
SplineCluster.❖ Each gene is placed in its own cluster and these
are combined where significant similarity exists.❖ A wrapper for R in MAANOVA arranges the data
for SplineCluster input.❖ The same wrapper annotates the SplineCluster
output.
7. VBSSM❖ Genes of similar expression are selected for
Variational Bayes State Space Modelling.❖ These are combined with genes suspected of
being involved in senescence processes.❖ VBSSM looks for relationships between
neighbouring time points to find interactive genes.❖ The output can be visually displayed in a software
package called Cytoscape.
8. Future Work❖ By selecting genes of interest from my ANOVA
model, I intend to produce several network maps.❖ The hubs of these networks, I intend to test using
under and over expresser mutants.❖ Expression of dependent genes can be checked
using lab techniques such as qPCR.❖ This information will provide priors for further runs
of VBSSM.
References❖ Kerr, M., Martin, M., Churchill, G., Analysis of Variance for
Gene Expression Microarray Data, 2000, The Jackson Laboratory, Maine, USA.
❖ Cui, X., Churchill, G., Statistical Tests for Differential Expression in cDNA Microarray Experiments, 2002, The Jackson Laboratory, Maine, USA.
❖ Beal, M., Falciani, F., Ghahramani, Z., A Bayesian approach to reconstructing genetic regulatory networks with hidden factors, 2005, Bioinformatics, (21), pp. 349-356
AbstractDuring natural senescence of Arabidopsis thaliana, a number of phenotypical changes can be seen as the plant tries to reabsorb nutrients from ageing leaves. Very little, however, is known about the gene interactions during senescence. A large quantity of highly replicated microarray timecourse data has been collected from leaves of Arabidopsis which will make it possible to quantify these interactions. MAANOVA makes it possible to analyse this microarray data and obtain a normalised data set over the timecourse which contains no undesirable variability. Variational Bayes State Space Modelling can then identify likely between gene interactions from select lists of genes. The result will be a network map within which some genes act as nodes. These genes can be verified by experimental testing, using over and under expresser mutants.
Day ToD
Day:ToD 20348
5
1086
19
7764
11
1074
29
The flow diagram above represents some of the stages from microarray data to a network model. The top image represents normalising the data, the centre represents selecting significant genes due to different terms of the ANOVA model and the bottom image is an example of a network map from Cytoscape after VBSSM.