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Exploring gene pathway
interactions using SOM
Keala Chan
SoCalBSI
August 20, 2004
Microarray data analysis
Gene expression data
Annotate and partition genes using
functional terms
Idea: Study relationships between
functional terms or pathways
Interacting Gene Pathways
Hypothesis: Some
relationship exists
between Pathway 1
and Pathway 4
Network of pathways
Pathway 35
Pathway 3
Pathway 1
Pathway 2
Pathway 4
Pathway 12
Pathway 18
Why use Self-Organizing Map?
(SOM)
• Serves as a data structure to represent the
network
• Maps the network onto a 2-D grid, preserving
the topological relationship between input
vectors
Pathway 3
Pathway
Pathway 3535
Pathway 4
Pathway 4
Pathway 3
Pathway 1
Pathway
Pathway
2 1,
Pathway 2
Pathway
12
Pathway 12
Pathway 18
Pathway 18
What is SOM?
• Tool for mapping similar input patterns
onto contiguous locations in the output
space
The SOM
has two
major
effects:
1. Clustering, or the creation
of abstractions of the input
space
2. Visualization of high-dimensional
data in two-dimensional display
Example
Recall: SOM maps similar input patterns onto contiguous locations
in the output space, resulting in clustering of the input space and
2-D visualization of the input space
Each circle represents a number of input vectors. Hence, the input vectors
have been clustered, or abstracted. Also, the topology has been
preserved: neighboring representative vectors are similar.
Representative vectors
2-D representative vector
x
x
x
x
The best-matching (closest)
representative vector and its
neighbors are pulled towards
the highlighted input vector
x
x
x
x
x
x
xx
x
x
x
x
x
x
x
x
x
The representative vector
comes to represent this
group of similar input
vectors
Method
Recall: The general goal is to train a SOM on a large dataset to
form a network of pathways for further study.
Affymetrix
data
Data:
Human healthy tissue
from 31 adult sources
(brain, kidney, skin,
etc…), 108 replicants
Partition
genes into
GO terms
Apply GSEA
Baseline: average
Method (continued)
GSEA scores
GSEA scores
normalized so
mean=0 and stdev=1
Train SOM on
the pathway
dataset
Visualizing first results
These terms all
map to, or are
represented by, the
same hexagon.
These pathways are
most activated in the
liver
Biological_Process_glycolysis_(10)
Molecular_Function_3-oxo-5-alpha-steroid_4-dehydrogenase_(4)
Molecular_Function_ATP-binding_cassette_(ABC)_transporter_(65)
Molecular_Function_blood_coagulation_factor_IX_(3)
Molecular_Function_blood_coagulation_factor_VII_(4)
Molecular_Function_blood_coagulation_factor_X_(3)
Molecular_Function_fructose-bisphosphate_aldolase_(9)
Molecular_Function_interleukin_receptor_(6)
Molecular_Function_pyruvate_kinase_(3)
Molecular_Function_sodium:phosphate_symporter_(5)
Molecular_Function_transaminase_(24)
K-means clustering
k-means (15) clustering of the
representative vectors groups
pathways that are often
activated at the same time
Next: Examine which k-means
clusters are activated under
each condition.
Projecting a new dataset
To test for pathways that
interact consistently, I
projected GSEA scores
for 16 different brain
tumor types onto the
SOM
Mapped pathways and GSEA
scores to the same location in
the SOM
Biological_Process_glycolysis_(10)
Molecular_Function_3-oxo-5-alpha-steroid_4-dehydrogenase_(4)
Molecular_Function_ATP-binding_cassette_(ABC)_transporter_(65)
Molecular_Function_blood_coagulation_factor_IX_(3)
Molecular_Function_blood_coagulation_factor_VII_(4)
Molecular_Function_blood_coagulation_factor_X_(3)
Molecular_Function_fructose-bisphosphate_aldolase_(9)
Molecular_Function_interleukin_receptor_(6)
Molecular_Function_pyruvate_kinase_(3)
Molecular_Function_sodium:phosphate_symporter_(5)
Molecular_Function_transaminase_(24)
Brain tumor data
Questions to ask:
What is the best we can do
with respect to the visual
smoothness of the projection?
What characterizes a “good”
projection?
Next: Plot histogram of
distances between any two
pathways mapping to the
same hexagon.
Calculate activation scores
for kmeans clusters trained
on healthy data.
Fetal tissue
Next?
• Validation by biologists
• Choose parameters wisely (projection
data, normalization, distance metric)
• Study k-means clustering of SOM
• More projections on SOM
Acknowledgments
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SOM Toolbox
All BioDiscovery software
Stan Nelson Lab microarray data
Michael Sneddon
Dr. Bruce Hoff
Dr. Soheil Shams
Everyone at SoCalBSI