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Case Study: NASCarray 157 Effect of CO2 and Light

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Correlations between Stomata and External Signals. Stomatal density Light ... S. A. Coupe, B. G. Palmer, J. A. Lake, S. A. Overy, K. Oxborough, F. I. Woodward, ... – PowerPoint PPT presentation

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Title: Case Study: NASCarray 157 Effect of CO2 and Light


1
Case Study NASCarray 157Effect of CO2 and
(Light)
MRes in AGPS
  • Hung-Ming Lai
  • stxhml_at_nottingham.ac.uk
  • ID 4087152
  • 31 October 2008

2
Outline
  • Prior Knowledge
  • Data Preparation
  • Visually Comparing Expression Data
  • Data Analysis
  • Hunting Information/Uncovering

3
Prior Knowledge
  • Stomata
  • Environmental Signals
  • Plant Development
  • Correlations between Stomata and External Signals
  • Stomatal density ? Light intensity
  • CO2 concentration ? ?2 ? Stomatal density ? 30
  • Lake, J.A., Quick, W.P., Beerling, D.J. and
    Woodward, F.I. (2001). Signals from mature to new
    leaves. Nature, 411, 154.
  • http//www.nature.com/nature/journal/v411/n6834/fu
    ll/411154a0.htmlf1
  • Looking at genetic level
  • Systemic signalling of environmental cues in
    Arabidopsis leaves
  • S. A. Coupe, B. G. Palmer, J. A. Lake,
    S. A. Overy, K. Oxborough, F. I. Woodward, J. E.
    Gray and W. P. Quick. (2006). Journal of
    Experimental Botany, Vol. 57, No. 2, pp. 329341

4
What does the data set look like
5
Description and Interpretation of the data
6
Visually Comparing Expression Data 1
2-fold change for all comparison
7
Visually Comparing Expression Data 2
2, 24
12, 24
VS
Time independent ?
What about this ?
24, 96
48, 96
VS
8
Data analysis 1
  • Factor in gene regulation
  • CO2 ambient, high
  • Light ambient, low (shade)
  • Time Point 2,4,12,24,48,96
  • Approach
  • Complicated or Naïve
  • Efficient and Effective
  • The other thinking Clustering
  • How?

9
Data analysis 2
  • Pick up informative profiles regarding CO2
  • Filtering out genes which doesnt change their
    expression level in any time/CO2 condition
  • Get relevant genes by Venn Diagram
  • Dual-clustering
  • Clustering on genes Gene tree
  • Effect of CO2 on molecular level
  • Potential regulatory sequence
  • Time dependent or independent
  • Clustering on sample Condition tree
  • Which factor is more significant?

10
Filter on expression level
Genes
22,810
  • Put all genes into expression profiles
  • Get rid of irrelevant data
  • Venn Diagram
  • Collect those informative genes

22,602
208
11
Gene Tree
CO2 Experiment (14 conds)
Default Experiment (28 conds)
Time Dependent
12
Gene Expression Change
13
More Finding
  • K-mean Algorithm
  • Regulatory Sequence
  • (CO2 Exp)

14
Condition Tree
  • Again, uncovering the informative genes relative
    to CO2 and Light , i.e. CO2 ? Light, based on
    expression level
  • Significance Light gt Time gt CO2
  • Interaction?

15
Uncovering Gene Ontology
Up-regulated DL
Informative DL
Informative ML
16
Uncovering more likely 2-factor Genes
2-way ANOVA Analysis
17
Uncovering - Pathway
  • Gene 254283_s_at
  • One of 2-factor genes
  • Pathway Flavonoid biosynthesis

18
Uncovering find similar gene(254283-s-at)
19
Conclusion
  • A Feasible Approach
  • Not fragmental but Integrative instead
  • Exploring the effect of CO2 concentration on gene
    expression level
  • Finding some slight correlations between CO2,
    time points, and light intensity
  • Uncovering on ontology, pathway and the similar
    gene

20
Future Work
  • Further analyses/work on the interaction
  • Does any key gene/promoter/enhancer dominating
    the interaction
  • Model the effect of those external signals on the
    system level
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