ApathwayandSVMbasedtoolfor

tumorclassification

A.A.2016/2017

Candidato:LucaVitaleMatricola:0522500362

Relatori:Prof:RobertoTagliaferriDr.ssa:AngelaSerra

Goals:

Classifywithpathways

1

Identifyrelationsamongpathways

2

Buildagraphofinteractions

betweenpathways

3

TheData

• LungSquamousCellCarcinoma(LSCC)• 106patients• 11837genes• 23074methylationvalues• 352miRNAs• Survivalinformation

Pipeline

SimilarityNetworkFusion- SNF

• SNFisaintermediatemulti-viewclusteringmethodologyforpatientssub-typing.

Patients similaritynetwork

Fusioniterations Fused patientssimilarity network

miRNAs

methy

SNF- Gridsearch• Thealgorithmisrundifferenttimewiththefollowingparameters:

• Numberofiterations:200• K:10to30stepby1• Numberofnearestneighbors

• 𝛼 :0.3to0.8stepby0.1• Varianceforlocalmodel

• ForeachcombinationofKand𝛼, thenumberofclusterswasevaluatedthroughtwoheuristics:eigen-gapsK12andeigen-gapsK2.

• Eachclusteringwasevaluatedthroughthesurvivalanalysisbyusingthelog-ranktest.

SNF- Theresults

P-Value=0.0015 K=23 𝛼 =0.6 Number of Iteration =200

Featureselection

• IdentifydiscriminantgenesDiscriminantFuzzyPattern

• IdentifywhichpathwaysaresignificantlyrepresentedbythegenesselectedbytheDFPalgorithm

EnrichmentAnalysis

Skipfactorà 0,1,2,3• TheskipFactor valuetoskiptheoutliers.Highervaluesimplythatlessgeneareconsideredoutliers.skipFactorequalto0doesnotskip;

1Zetaà 0.35,0.4,0.45,0.5• Thezetaparameterthatsetsthethresholdvaluewhichcontrolstheactivationofalinguisticlabel;

2piValà 0.4to0.8step by0.05• ThepiVal parameterisequaltothepercentageofvaluesofaclasstodeterminethefuzzypatterns.Itcantakevaluesintheinterval[0,1];

3Overlappingà 1,2• Determines thenumber ofdiscretelabels;

4

DiscriminantFuzzyPattern– Gridsearch

EnrichmentAnalysis

• ForeachgroupofgenesselectedbyDFPparameterstheenrichmentanalysiswasperformed• Thep-valueiscalculatedbasedonthehypergeometricmodel

• WeonlyusedKEGGandReactome Database

EvaluationofDFPresults• Weselectedthedatasetwhichreachedthemaximumnumberofpathways,containingonlythegenesselectedwiththeDFP.

• Theselectedcombinationhas1384genes,67pathwaysandpiVal 0.6.

Thepathways

• Theselectedpathwaysare67:28KEGGand39Reactome

• Theselectedgenesare1384.TheDFPparametersare:• skipfactor2• zeta0.3• piVal 0.6• overlapping1

ClassificationwithSVM

• Foreach pathway aLinear SVMwas executed oneach pairofclasses

• Two level cross-validation• 3outer folds• 2inner folds

• C:1e-5,1e-4,1e-3,1e-2,1e-1,1e0,1e1,1e2,1e3,1e4,1e5,1e6

Permutationtest

• Thegoalofpermutationtestistoidentifythepathwaysthatarestatisticallysignificantfortheclassification

namepathway idpathway p-value Accuracy Size classesCytokine-cytokinereceptorinteraction K_hsa04060 0.04 0.93 21 5vs2

Cellcycle K_hsa04110 0.03 0.89 12 1vs2 Cellcycle K_hsa04110 0.02 0.97 13 2vs3 Cellcycle K_hsa04110 0.05 0.90 11 5vs3

Osteoclastdifferentiation K_hsa04380 0.03 1.00 10 1vs4 Antigenprocessingandpresentation K_hsa04612 0.03 1.00 8 1vs4 Antigenprocessingandpresentation K_hsa04612 0.05 0.92 7 2vs3 Antigenprocessingandpresentation K_hsa04612 0.03 1.00 7 5vs4 Tcellreceptorsignalingpathway K_hsa04640 0.04 1.00 11 1vs4 Tcellreceptorsignalingpathway K_hsa04640 0.01 0.81 12 5vs1 Th1andTh2celldifferentiation K_hsa04658 0.04 1.00 9 1vs4 Th1andTh2celldifferentiation K_hsa04658 0.04 0.93 9 2vs3

Th17celldifferentiation K_hsa04659 0.04 1.00 11 1vs4 Th17celldifferentiation K_hsa04659 0.04 0.96 12 2vs3

Tcellreceptorsignalingpathway K_hsa04660 0.03 1.00 8 1vs4 Tcellreceptorsignalingpathway K_hsa04660 0.04 1.00 8 5vs4 Bcellreceptorsignalingpathway K_hsa04662 0.04 0.84 7 1vs2 Bcellreceptorsignalingpathway K_hsa04662 0.03 0.89 8 5vs2

Leukocytetransendothelialmigration K_hsa04670 0.01 0.91 12 1vs2 Leukocytetransendothelialmigration K_hsa04670 0.01 0.90 13 1vs3 Leukocytetransendothelialmigration K_hsa04670 0.04 0.92 14 5vs3

Secondstepofclassification:pathwayprobabilitiescombinations• Foreachpairsofclass,wecombinethepathwaysusingtheclassprobabilitiesofSVMasnewfeatures.• WetryallthecombinationofpathwaysusinglinearSVM

• C:1e-5,1e-4,1e-3,1e-2,1e-1,1e0,1e1,1e2,1e3,1e4,1e5,1e6

GraphInteraction

• Wecreateagraphinteractionforeachcombinationofclasses• Theverticesofthegrapharethegenesinpathways

• ThesizeisequalatSVMweight,ifagenesisincommonbetweenthepathwayswepickthemaxweight.

• Fortheedges:1. Wecalculatedthecorrelationbetweenthegenes2. Splitthecorrelationinpositiveandnegativeand

calculatetheMST3. OnlytheedgesbelongedtoMSTareinthefinalgraph

• Wehighlightedthepathwayswithshapesofdifferentcolours

Conclusion

• Thepathwaysaregoodfeaturesfortheclassificationproblem.

• Thepipelinecanbetestedonotherdatasetstotestit’sgeneralizationability.