Identify biological pathways that are associated with different types of tumour

1. A pathway and SVM
based tool for
tumor classification
A.A. 2016/2017
Candidato:
Luca Vitale
Matricola: 0522500362
Relatori:
Prof: Roberto Tagliaferri
Dr.ssa: Angela Serra

2. Goals:
Classify with
pathways
1
Identify relations
among pathways
2
Build a graph of
interactions
between pathways
3

3. The Data
• Lung Squamous Cell Carcinoma (LSCC)
• 106 patients
• 11837 genes
• 23074 methylation values
• 352 miRNAs
• Survival information

4. Pipeline

5. Similarity Network Fusion - SNF
• SNF is a intermediate multi-view clustering methodology for patients sub-typing.
Patients similarity
network
Fusion iterations Fused patients
similarity network
miRNAs
methy

6. SNF - Grid search
• The algorithm is run different time with the following parameters:
• Number of iterations: 200
• K: 10 to 30 step by 1
• Number of nearest neighbors
• 𝛼 : 0.3 to 0.8 step by 0.1
• Variance for local model
• For each combination of K and 𝛼, the number of clusters was evaluated through
two heuristics: eigen-gaps K12 and eigen-gaps K2.
• Each clustering was evaluated through the survival analysis by using the log-rank
test.

7. SNF - The results
P-Value = 0.0015 K = 23 𝛼 = 0.6 Number of Iteration = 200

8. Feature selection
• Identify discriminant genes
Discriminant
Fuzzy Pattern
• Identify which pathways are
significantly represented by the
genes selected by the DFP algorithm
Enrichment
Analysis

9. Skip factor à 0, 1, 2, 3
• The skipFactor value to
skip the outliers. Higher
values imply that less
gene are considered
outliers. skipFactor
equal to 0 does not
skip;
1
Zeta à 0.35, 0.4, 0.45,
0.5
• The zeta parameter that
sets the threshold value
which controls the
activation of a linguistic
label;
2
piVal à 0.4 to 0.8 step by
0.05
• The piVal parameter is
equal to the percentage of
values of a class to
determine the fuzzy
patterns. It can take values
in the interval [0,1];
3
Overlapping à 1, 2
• Determines the
number of discrete
labels;
4
Discriminant Fuzzy Pattern – Grid search

10. Enrichment Analysis
• For each group of genes selected by DFP parameters the enrichment
analysis was performed
• The p-value is calculated based on the hypergeometric model
• We only used KEGG and Reactome Database

11. Evaluation of
DFP results
• We selected the dataset which
reached the maximum number of
pathways, containing only the
genes selected with the DFP.
• The selected combination has
1384 genes, 67 pathways and
piVal 0.6.

12. The pathways
• The selected pathways are 67: 28 KEGG and
39 Reactome
• The selected genes are 1384. The DFP
parameters are:
• skip factor 2
• zeta 0.3
• piVal 0.6
• overlapping 1

13. Classification with SVM
• For each pathway a Linear SVM was executed on each pair of classes
• Two level cross-validation
• 3 outer folds
• 2 inner folds
• C: 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6

15. Permutation test
• The goal of permutation test is to identify the
pathways that are statistically significant for the
classification

16. name pathway id pathway p-value Accuracy Size classes
Cytokine-cytokine receptor interaction K _hsa04060 0.04 0.93 21 5vs2
Cell cycle K _hsa04110 0.03 0.89 12 1vs2
Cell cycle K _hsa04110 0.02 0.97 13 2vs3
Cell cycle K _hsa04110 0.05 0.90 11 5vs3
Osteoclast differentiation K _hsa04380 0.03 1.00 10 1vs4
Antigen processing and presentation K _hsa04612 0.03 1.00 8 1vs4
Antigen processing and presentation K _hsa04612 0.05 0.92 7 2vs3
Antigen processing and presentation K _hsa04612 0.03 1.00 7 5vs4
T cell receptor signaling pathway K _hsa04640 0.04 1.00 11 1vs4
T cell receptor signaling pathway K _hsa04640 0.01 0.81 12 5vs1
Th1 and Th2 cell differentiation K _hsa04658 0.04 1.00 9 1vs4
Th1 and Th2 cell differentiation K _hsa04658 0.04 0.93 9 2vs3
Th17 cell differentiation K _hsa04659 0.04 1.00 11 1vs4
Th17 cell differentiation K _hsa04659 0.04 0.96 12 2vs3
T cell receptor signaling pathway K _hsa04660 0.03 1.00 8 1vs4
T cell receptor signaling pathway K _hsa04660 0.04 1.00 8 5vs4
B cell receptor signaling pathway K _hsa04662 0.04 0.84 7 1vs2
B cell receptor signaling pathway K _hsa04662 0.03 0.89 8 5vs2
Leukocyte transendothelial migration K _hsa04670 0.01 0.91 12 1vs2
Leukocyte transendothelial migration K _hsa04670 0.01 0.90 13 1vs3
Leukocyte transendothelial migration K _hsa04670 0.04 0.92 14 5vs3

18. Second step of classification: pathway
probabilities combinations
• For each pairs of class, we combine the pathways using the class
probabilities of SVM as new features.
• We try all the combination of pathways using linear SVM
• C: 1e-5, 1e-4, 1e-3, 1e-2, 1e-1, 1e0, 1e1, 1e2, 1e3, 1e4, 1e5, 1e6

19. Graph Interaction
• We create a graph interaction for each combination of classes
• The vertices of the graph are the genes in pathways
• The size is equal at SVM weight, if a genes is in common
between the pathways we pick the max weight.
• For the edges:
1. We calculated the correlation between the genes
2. Split the correlation in positive and negative and
calculate the MST
3. Only the edges belonged to MST are in the final graph
• We highlighted the pathways with shapes of different colours

20. Conclusion
• The pathways are good features for the classification problem.
• The pipeline can be tested on other datasets to test it’s generalization
ability.