computing co-expression relationships wen-dar lin
Post on 14-Jan-2016
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Contents
• Motivation• Basic Idea• Case Studies
– An Example of Single Experiment
– An Example of Time-Course Experiment
• Potential Applications• Availability• Future Works
Motivation
• Given a set of differentially displayed genes that are reported by an array experiment.– We would like to know relationships among
these genes.– These relationships may recover important
modules or motifs with respect to the experiment.
Motivation
• Co-expression relationships are one kind of the most biologically meaningful and easily computable relationships.– Co-expression relationships form modules that
may infer important biological information.– They can be computed from a large amount of
publicly available array data.
Basic Idea
• Array data can be retrieved from publicly available data repository– like the NASCarrays, NCBI GEO, EMBL-EBI
ArrayExpress
• They should be normalized before computing the co-expression relationships.– e.g. normalized by the RMA method
Basic Idea
• Defining co-expression relationships– We define that a co-
expression relationship between two genes exists if the pearson correlation coefficient between their normalized expression levels is greater than or equal to a certain threshold.
slide # 1 2 3 4 …
gene X 1 2 10 3 …
gene Y 5 2 12 4 …
X
Y
Basic Idea
• Properties of pearson correlation coefficient– Let Correl(A, B) be the
pearson correlation coefficient between normalized expression levels of gene A and gene B.
– 0 Correl(A, B) 1
from http://www.gseis.ucla.edu/courses/ed230bc1/notes1/var1.html
negative correlation
Basic Idea
• The computational assistance– Given a set of interested genes– Compute co-expression relationships among
them– Identify co-expression clusters
Case Studies
• We have implemented aforementioned ideas into a tool kit and applied it to two case studies.– A single experiment– A time-course experiment
A Single Experiment
• In this example, an array experiment was performed– 178 differentially displayed genes were
identified.– Based on RMA array data of 300 ATH1 slides
downloaded from the NASCarrays• sample of each slide was derived nonexclusively
from roots• Threshold for pearson correlation coefficient = 0.7
A Single Experiment
• We may compute co-expression relationships based on all kinds of array experiment data– Based on RMA array data of 1436 ATH1 slides
downloaded from the TAIR, co-expression relationships were identified
• Threshold for pearson correlation coefficient = 0.7
A Single Experiment
• Is there any difference between the graphs based on root-array data and that based on all-array data?– By differentially marking clusters of one graph
onto the other graph.
A Single Experiment
Two clusters mapped by the other graph
One cluster that should be root-specific
A Single Experiment
• Some remarks– The number of differentially displayed genes reported
by the experiment is 178
– The number of clustered genes is 47+14+9 = 70• Reduced by more than 50%
– The co-expression relationships are recovered• Each cluster may be a module that usually work together.
– Finding tissue-specific co-expression relationships• Can be done by mapping the graph based on all-array data
onto the graph based on tissue-related-array data.
A Single Experiment
• In addition to cluster genes according to co-expression relationships, we also fished genes that may potentially co-expressed.– These genes may not be identified as
differentially displayed in the experiment.
A Single Experiment
• A GO enrichment analysis was also carried out– using the GOBU software (gobu.iis.sinica.edu.tw)– which should give a conceptual view of clustered
genes.
A time-course experiment
• In this example, a time-course array experiment was performed– Three time points– About 800 genes differentially displayed at
least one time point.– Based on array data of 300 ATH1 slides
extracted from RMA array data of about 2600 ATH1 slides downloaded from the NASCarrays
• Threshold for pearson correlation coefficient = 0.8
A time-course experiment
Time point 1
About 100 genes
About 100 genes
A time-course experiment
Time point 2
About 100 genes
About 100 genes
A time-course experiment
Time point 3
About 100 genes
About 100 genes
A time-course experiment
• Though this clustering and time-course expression data shows some biological meaning,– this size of clustered genes (more than 200)
• makes the graph too complex and
• is too large to be realized in a short time.
A time-course experiment
• Reducing the size of clustered genes may help– reducing complexity of the graph and
– realizing revealed co-expression module
• We reduced the graph by removing co-expression relationships that generally exist in the entire plant– based on RMA array data of about 2600 ATH1 slides
downloaded from the NASCarrays
– Threshold for pearson correlation coefficient = 0.7
A time-course experiment
• Edges (relationships) to be removedY
root-related
others
X
A time-course experiment
• Edges (relationships) to be retainedY
root-related
others
X
A time-course experiment
About 20 genes
About 60 genes
About 50 genes
Time point 1
A time-course experiment
About 20 genes
About 60 genes
About 50 genes
Time point 2
A time-course experiment
About 20 genes
About 60 genes
About 50 genes
Time point 3
A time-course experiment
• Some remarks– The number of differentially displayed genes at least
one time point is about 800.
– The number of clustered genes is about 60+50+20 = 130
• Reduced by more than 80%
– The retained graph contains edges, i.e., gene pairs, that are co-expressed in root but not in the entire plant
• The recovered clusters should be root specific.
Potential Applications
• We have created a tool kit that– computes co-expression relationships based on array
data• where probe names can be replaced by aliases made by
something like orthologous mapping• can be used for studying non-model organism using array data
of a model organism.
Potential Applications
• We have created a tool kit that– fills colors according to graphs by
• intensity fold-changes, or
• clusters in another graph
Potential Applications
• We have created a tool kit that– removes/retains co-expression relationships in
another graph– finds specific or common co-expression
relationships200 genes 120 genes
Potential Applications
• We have created a tool kit that– fishes genes that are
potentially co-expressed with assigned bait
Future Works
• Incorporate pathway database– like the AraCyc– for finding relationships between co-expression
clusters and known pathways
• A user-friendly interface which would – facilitate using this tool kit and– help manage output data
Availability
• The tool kit is now an open-source project– http://maccu.sourceforge.net– Project name: MACCU
• Multi-Array Correlation Computation Utility
– A detailed description of each program module has been created.
– A running script with example is provided.
Special Thanks
• I would like to thank– Drs. Chang (Bill), Schmidt & Wu
• for raising this idea,
• the initial implementation, and
• valuable comments.
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