Exploring gene pathway interactions using SOM Keala Chan SoCalBSI August 20, 2004

Microarray data analysis Idea: Study relationships between functional terms or pathways Gene expression data Annotate and partition genes using functional terms

Interacting Gene Pathways Hypothesis: Some relationship exists between Pathway 1 and Pathway 4

Network of pathways Pathway 18 Pathway 4 Pathway 3 Pathway 2 Pathway 1 Pathway 35 Pathway 12

Pathway 18 Pathway 4 Pathway 3 Pathway 2 Pathway 1 Pathway 35 Pathway 12 Why use Self-Organizing Map? Serves as a data structure to represent the network Maps the network onto a 2-D grid, preserving the topological relationship between input vectors (SOM) Pathway 12Pathway 18 Pathway 1, Pathway 2 Pathway 4 Pathway 3Pathway 35

What is SOM? Tool for mapping similar input patterns onto contiguous locations in the output space 1. Clustering, or the creation of abstractions of the input space 2. Visualization of high-dimensional data in two-dimensional display The SOM has two major effects:

Example 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. 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

Representative vectors x x x x x x x x x x x x xx x x x x x x x The representative vector comes to represent this group of similar input vectors The best-matching (closest) representative vector and its neighbors are pulled towards the highlighted input vector 2-D representative vector

Method Partition genes into GO terms Apply GSEA Affymetrix data Recall: The general goal is to train a SOM on a large dataset to form a network of pathways for further study. Data: Human healthy tissue from 31 adult sources (brain, kidney, skin, etc … ), 108 replicants Baseline: average

Method (continued) GSEA scores Train SOM on the pathway dataset GSEA scores normalized so mean=0 and stdev=1

Visualizing first results These terms all map to, or are represented by, the same hexagon. 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) These pathways are most activated in the liver

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 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) Mapped pathways and GSEA scores to the same location in the SOM

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 SOM Toolbox All BioDiscovery software Stan Nelson Lab microarray data Michael Sneddon Dr. Bruce Hoff Dr. Soheil Shams Everyone at SoCalBSI