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Improving gene expression similarity measurement using pathway-based analytic dimension

Improving gene expression similarity measurement using pathway-based analytic dimension. Changwon Keum BMDRC. Accumulated gene expression data in public repository. GEO, NCBI. Search the database. * Search by annotation. * Search by contents. Dataset vs. individual sample( profile).

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Improving gene expression similarity measurement using pathway-based analytic dimension

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  1. Improving gene expression similarity measurement using pathway-based analytic dimension Changwon Keum BMDRC

  2. Accumulated gene expression data in public repository • GEO, NCBI

  3. Search the database * Search by annotation * Search by contents

  4. Dataset vs. individual sample( profile) Data set Microarray database search Case Control Data set level Similarity measure search Profile level Similarity measure Individual profiles

  5. Raw gene expression profile based similarity search • Used by Cellmontage • Spearman correlation coefficient • Limitation • Cross-platform comparison • Cross-experiment comparison

  6. Pathway expression profile based similarity measure Step1. Converting to pathway Expression profile Step2. Spearman Correlation Test

  7. Samples with cell type Annotated by Cellmontage group For 42 cell type with multiple samples Cell type classification Query Cross-platform Cross-experiment

  8. Classification accuracy

  9. CGSEP vs. PEPC Thalamus (all) Liver(Cross-platform)

  10. Similarity score for TP?

  11. Details of cross-experiment classification

  12. GEFERENCE • Reference database of gene expression • Search similar gene expression profile • Meta analysis

  13. Gene expression profiling Extract sample Patient GEFERENCE Search Matched reference individual with Clinical information Marker Validation

  14. Acknowledgement • Jung Hoon Woo • Members at BMDRC • KFDA for funding Thanks for your attention!!

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