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Finding Consistent Subnetworks across Microarray dataset

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This article details a methodology for identifying consistent subnetworks across various microarray datasets, addressing the inconsistency often observed in differential gene expression studies. By leveraging gene-gene relationships and relevant biological insights, the proposed method enhances the detection of significant subnetworks related to specific phenotypes. Using a robust scoring and significance evaluation system, the methodology overcomes limitations of traditional approaches. The findings demonstrate improved reliability and insight into disease mechanisms, paving the way for novel treatment strategies.

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Finding Consistent Subnetworks across Microarray dataset

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  1. Finding Consistent Subnetworks across Microarray dataset Fan Qi GS5002 Journal Club

  2. Outline • Introduction • Methodology • Results & Discussions • Conclusions

  3. Introduction • Identify Differential Gene Expression • Identify significant genes w.r.t a phenotype • Importance: • Testing effectiveness of treatment • Biological insights of diseases • Develop new treatment • Disease Prophylaxis • Any others ?

  4. Current Methods • Individual Genes • Search for individual differentially expressed genes • Fold-change, t-test, SAM • Gene Pathway Detection • Looking at a set of genes instead of individual genes • Bayesian learning and Boolean network learning • Gene Classes • Adding existing biological insights • Over-representation analysis (ORA), Functional Class Scoring(FCS), GSEA, NEA, ErmineJ

  5. Challenge • Different Results from Different Dataset of the SAME disease! • Zhang M [1] demonstrated inconsistency in SAM: Inconsistency among datasets Reconstruct from Table 1 in [1]

  6. New Approach • SNet [2] • Proposed in 2011 • Utilize gene-gene relationship in analysis • Gene-gene relationship • Activates VS. Inhibits • Gene Subnetwork • Gene is the Vertex, Relationship is an edge From Fig 1 in [2] RHOA VAV PIK3R2 RAC1 IQGAP1 ARHGEF1 Partially adapted from Fig 2 in [2]

  7. Methodology • Input: • Genes labeled with phenotype • Gain from microarray experiment • Third-party Info: • Gene Pathway Info • Gene Reaction Info • Attributes of Subnetwork • Size, Score • Output: • A set of significant sub-network Subnetwork Scoring Subnetwork Significance Subnetwork Extraction

  8. Methodology –Step 1 Phenotypes Patient’s Gene Ranked List ……..

  9. Methodology –Step 1 for patient Only top genes is kept Repeat for every phenotype group

  10. Methodology –Step 1 ……. select genes occur in of patients Select one phenotype as others as

  11. Methodology –Step 1 A list of Subnetworks w.r.t ……… Partition into multiple pathways Generate Subnetwork

  12. Methodology – Step 2 • For each Subnetwork in in the and Patient , compute overall expression level: • = , where • a gene in that is highly expressed in • # patients in who have highly expressed • : total # patients in • For Patients and compute t-test T test Assign to each Subnetwork

  13. Methodology – Step 3 • Randomly Swap Phenotype labels of patient, recreating subnetworks and t-test scores (step 1-2) • Repeat [A] for 1,000 permutations. • Forms a 2-D histogram () • Estimate the nominal p-value of each Subnetwork • Select Subnetwork with - Null-hypo: subnetwork with is not significant Fig 5 in original paper

  14. Results and Discussions • Dataset: • Leukemia: Golub VS Armstrong • ALL: Ross VS Yeoh • DMD: Haslett VS Pescatori • Lung: Bhattacharjee VS Garber • Performance Comparison: • Subnetwork Overlap (with GSEA) • Gene Overlap (GSEA, SAM, t-Test) • Other Comparisons: • Network Size, Gene Validity with t-Test

  15. Results and Discussions • Subnetwork Overlap Synthesized from Table 1, 2 from [2] Higher the better

  16. Results and Discussions • Gene Overlap Synthesized from Table 3, 4,5 from [2] Higher the better

  17. Results and Discussions • Size of subnetworks Reconstructed from Table 6 from [2]

  18. Results and Discussions • Validity • Compare the genes in EACH Subnetwork with those in t-test • Genes in each Subnetwork appears in T-Test is around 70%- 100% • Selected Results (too large to present full) Selected from Table 7,8,9,10 in[2]

  19. Conclusions • Traditional Methods have inconsistency problem across different dataset of the same disease • SNet utilize Biological insights to mitigate the gap • Gene-to-Gene relationship • Gene Pathway knowledge • SNet shows better results than established algorithms • More consistent

  20. References • [1] Zhang M, Zhang L, Zou J, Yao C, Xiao H, Liu Q, Wang J, Wang D, Wang C, GuoZ: Evaluating reproducibility of differential expression discoveries in microarray studies by considering correlated molecular changes. • [2]Donny Soh, Difeng Dong1, YikeGuo, LimsoonWong Finding consistent disease subnetworks across microarray datasets

  21. Thank you!!

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