Progetto bioinformatica università degli studi di salerno anno 2016/2017

1. Pathway based
OMICs data
classification
Bioinformatics - 2016/2017

2. Goals
• Classification with pathways - Group of genes that are involved in the
same biological functions
• Identify relations among pathways
• Build a graph of interactions between pathways and miRNAs

3. Data
• Breast Cancer (BC)
• 151 patients
• RNA (20501 genes)
• miRNA (1046)
• 4 classes
• LumA - 55
• LumB - 59
• Basal - 24
• Her2 - 13
Pathways by MSigDB à KEGG, Reactome, Biocarta, C6 …
• Glioma
• 167 patients
• RNA (12042 genes)
• miRNA (534)
• 4 classes
• Proneural - 52
• Classical - 37
• Mesenchymal - 54
• Neural - 24

4. Introduction
Discriminant
Fuzzy
Patterns
Enrichment
Analysis
Classification
linear SVM
Permutation
Test
Genes
miRNAsMSigDB
Interaction
Graph

5. First step
• BC Data set à Training Set (70%) and Test Set (30%)
• Glioma Data set à Training Set (75%) and Test Set (25%)

6. Feature selection
• Discriminant fuzzy pattern
• Too many features à Identify discriminant genes
• Enrichment à Grouping genes in pathways (MSigDB)
• Identify which pathways are significantly represented by the genes selected
with the DFP algorithm

7. Discriminant Fuzzy Pattern – Grid search (BC)
• Skip factor à 0, 1, 2, 3
• Factor to skip outliers. Lower value à more values skipped (0: don’t skip)
• Zeta à 0.35, 0.4, 0.45, 0.5
• Threshold used in the membership functions to label the float values with a discrete
value
• piVal à 0.4, 0.45, 0.5, 0.55, 0.6, 0.65, 0.7, 0.75, 0.8
• Percentage of values of a class to determine the fuzzy patterns
• Overlapping à 1, 2
• Determines the number of discrete labels
• Genes after DFP à 578 with Skip Factor 1, Zeta 0.4, piVal 0.65 and
Overlapping 1

8. Discriminant Fuzzy Pattern – Grid search
(Glioma)
• Skip factor à 1, 2, 3
• Zeta à 0.35, 0.4, 0.45, 0.5
• piVal à 0.6, 0.65, 0.7
• Overlapping à 1, 2
Genes after DFP à 635 with Skip Factor 1, Zeta 0.35, piVal 0.65 and
Overlapping 1

9. Enrichment
• Breast Cancer
• Number of pathways selected through enrichment: 1585
• Number of pathways with more than 10 genes: 859
• Glioma
• Number of pathways by enrichment: 1612 p-value 0.0001
• First 1000 pathways with more than 10 genes and lowest p-value

10. Classification with SVM
• Linear SVM
• 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

11. First step of classification
Patients
genes
pathway
1
genes
pathway
2
genes
pathway
i
genes
pathway
3
Linear SVM
1
Linear SVM
2
Linear SVM
3
Linear SVM
i
Class prob.
PatientsPatientsPatientsPatients
Class prob.
Class prob.
Class prob.

12. Size vs Pathways Accuracy (BC)
Correlation: 0.028

13. Size vs Pathways Accuracy (Glioma)
Correlation: 0.342

14. Pathways after permutation test
• 1000 permutation tests on the pathways with best accuracies
• Breast cancer
• Number of pathways that passed permutation test: 36
• Lowest accuracy 77.9%
• Highest accuracy 84.6%
• Glioma
• Number of pathways that passed permutation test: 278
• Lowest accuracy 80%
• Highest accuracy 88%

15. • ACEVEDO_FGFR1_TARGETS_IN_PROSTATE_CANCER_MODEL_UP
• DEBIASI_APOPTOSIS_BY_REOVIRUS_INFECTION_DN
• DELACROIX_RARG_BOUND_MEF
• ENK_UV_RESPONSE_EPIDERMIS_UP
• ENK_UV_RESPONSE_KERATINOCYTE_DN
• FARMER_BREAST_CANCER_APOCRINE_VS_BASAL
• GO_CELLULAR_RESPONSE_TO_LIPID
• GO_CIRCULATORY_SYSTEM_PROCESS
• GO_GLAND_DEVELOPMENT
• GO_REGIONALIZATION
• GO_REGULATION_OF_CELL_CYCLE_PHASE_TRANSITION
• GO_REGULATION_OF_PROTEIN_SERINE_THREONINE_KINASE_ACTIVI
TY
• GO_RESPONSE_TO_ALCOHOL
• GO_RESPONSE_TO_ESTROGEN
• GO_RESPONSE_TO_STEROID_HORMONE
• GO_UROGENITAL_SYSTEM_DEVELOPMENT
• GSE1460_NAIVE_CD4_TCELL_ADULT_BLOOD_VS_THYMIC_STROMAL
_CELL_DN
• GSE21927_SPLEEN_VS_4T1_TUMOR_MONOCYTE_BALBC_DN
• GSE23502_WT_VS_HDC_KO_MYELOID_DERIVED_SUPPRESSOR_CELL
_COLON_TUMOR_DN
• GSE26351_WNT_VS_BMP_PATHWAY_STIM_HEMATOPOIETIC_PROGE
NITORS_UP
• HALLMARK_ESTROGEN_RESPONSE_LATE
• LEI_MYB_TARGETS
• LIU_PROSTATE_CANCER_DN
• MODULE_18
• MODULE_255
• MODULE_52
• NFE2L2.V2
• SATO_SILENCED_BY_METHYLATION_IN_PANCREATIC_CANCER_1
• SHEN_SMARCA2_TARGETS_DN
• SMID_BREAST_CANCER_RELAPSE_IN_BONE_DN
• V$ALPHACP1_01
• V$TEF1_Q6
• V$ZIC2_01
• VANTVEER_BREAST_CANCER_ESR1_DN
• VECCHI_GASTRIC_CANCER_EARLY_DN
• ZHANG_BREAST_CANCER_PROGENITORS_UP
BC Pathways

16. Glioma Pathways
• MEISSNER_NPC_HCP_WITH_H3K4ME2
• YYCATTCAWW_UNKNOWN
• RIGGI_EWING_SARCOMA_PROGENITOR_UP
• DEURIG_T_CELL_PROLYMPHOCYTIC_LEUKEMIA_D
N
• MODULE_169
• GO_REGULATION_OF_MEMBRANE_POTENTIAL
• GSE24574_BCL6_LOW_TFH_VS_NAIVE_CD4_TCEL
L_UP
• GSE25677_MPL_VS_R848_STIM_BCELL_DN
• REACTOME_AXON_GUIDANCE
• MODULE_19
• HELLER_HDAC_TARGETS_SILENCED_BY_METHYLA
TION_UP
• GO_ACTIN_BINDING
• GSE3982_EOSINOPHIL_VS_BASOPHIL_UP
• GSE3982_MAC_VS_TH2_UP
• V$TATA_C
• GO_REGULATION_OF_ANATOMICAL_STRUCTURE_
SIZE
• MODULE_52
• SCHAEFFER_PROSTATE_DEVELOPMENT_48HR_UP
•
DAVICIONI_TARGETS_OF_PAX_FOXO1_FUSIONS_U
P
•
DEURIG_T_CELL_PROLYMPHOCYTIC_LEUKEMIA_U
P
• GSE21063_CTRL_VS_ANTI_IGM_STIM_BCELL_NFA
TC1_KO_16H_UP
• KAECH_NAIVE_VS_MEMORY_CD8_TCELL_DN
• SANSOM_APC_TARGETS_DN
• GO_SINGLE_ORGANISM_CELL_ADHESION
• HOLLMANN_APOPTOSIS_VIA_CD40_DN

17. • GSE22025_TGFB1_VS_TGFB1_AND_PROGESTERONE_TREATE
D_CD4_TCELL_DN
• GO_CELL_SUBSTRATE_JUNCTION
• GSE3982_NEUTROPHIL_VS_EFF_MEMORY_CD4_TCELL_UP
• HIRSCH_CELLULAR_TRANSFORMATION_SIGNATURE_UP
• GSE21927_SPLENIC_C26GM_TUMOROUS_VS_BONE_MARR
OW_MONOCYTES_DN
• GSE3982_BASOPHIL_VS_CENT_MEMORY_CD4_TCELL_UP
• MCBRYAN_PUBERTAL_BREAST_4_5WK_UP
• GO_CELL_CELL_JUNCTION
• GSE13411_NAIVE_BCELL_VS_PLASMA_CELL_UP
• GO_AXON
• GO_REGULATION_OF_INTRACELLULAR_PROTEIN_TRANSPOR
T
• GO_TELENCEPHALON_DEVELOPMENT
• GSE13484_UNSTIM_VS_12H_YF17D_VACCINE_STIM_PBMC_
DN
• LEF1_UP.V1_DN
• CASORELLI_ACUTE_PROMYELOCYTIC_LEUKEMIA_UP
• GO_ACTIVATION_OF_IMMUNE_RESPONSE
• GO_EPITHELIAL_CELL_DIFFERENTIATION
• GO_POSITIVE_REGULATION_OF_CELL_ADHESION
• GSE15735_2H_VS_12H_HDAC_INHIBITOR_TREATED_CD4_TC
ELL_UP
• MODULE_8
• BLALOCK_ALZHEIMERS_DISEASE_INCIPIENT_UP
• GO_DENDRITE
• GSE3982_CENT_MEMORY_CD4_TCELL_VS_TH2_UP
• KIM_WT1_TARGETS_UP
• GO_REGULATION_OF_NEURON_PROJECTION_DEVELOPMEN
T
Glioma Pathways

18. Graph - (1)
• Build interaction graph between pathway and miRNA communities
• We first compute interactions between pathways
• Interaction Score matrix
• We then add miRNAs connecting them to pathways
• Correlation matrix between miRNAs and genes
• Fisher's exact test
• We add edges between miRNAs
• Weighted network projection

19. Graph - (2)
• Group miRNAs in communities
• Walktrap algorithm
• We replace miRNAs with nodes representing miRNA communities
• We finally identify communities in the whole interaction graph

20. Interaction Score
Relations among pathways: interaction score (IS)
!" =
|%& − %(|
"& + "(
M and S are respectively mean and standard deviation of the two pathways x
and y
We apply a cutoff on the resulting interaction matrix

21. miRNA and Pathways interaction
• We evaluate Pearson correlation between the miRNA and all the
genes in the pathway. We then apply a cutoff to select strong
correlations.
• Then for each miRNA and pathway we use Fisher’s exact test, to
determine if the miRNA is significantly linked to the pathway (i.e. we
check if there is a significant number of genes in common)

22. The Goal

23. The Goal (2)

24. Final classification
Patients
Stackingwith pathway
SVM
Classes for
each patient
Patients
genes
pathways
Linear SVM
Linear SVM
Linear SVM
Linear SVM
Class prob.
Patients
Patients
miRNAsand pathway
connected
Linear SVM
Linear SVM
Linear SVM
Linear SVM
Class prob.
Patients
Patients
Stackingwith
pathwayand miRNAs
SVM
Classes for
each patient

25. Fine