MetaCore data analysis suite and functional analysis

1. MetaCore data analysis suite and functional analysis Ying-Fan Chen, Ph. D. Feb. 5, 2010

2. Outline • Introduction • 由成大生資中心進入：使用前設定以及注意事項 • 簡介使用方法  Upload Data  Analyze Data  快速workflow  MapEditor • 試用帳號 實際操作

3. Outline • Introduction 由成大生資中心進入：使用前設定以及注意事項 簡介使用方法  Upload Data  Analyze Data  快速workflow  MapEditor 試用帳號 實際操作

4. Knowledge Base: • - protein interactions • - causative associations (gene-disease, cpd-disease) • - pathways, protein complexes • - ontologies Experimental data depository Data analysis tools: EA, networks, interactome “Knowledge-based” functional data analysis Cancer relevant annotations, datatabases, Active cpds analysis screening Data parsing, normalization HTS, HCS Targets Compound scoring Biomarkers

5.  eg, GeneSpring GX “Cut off” setting ( eg, log ratio; fold change) Analysis: Networks, pathways, Statistics on functional categories  eg, MetaCore Prioritized gene lists Genes are functionally connected 一般Array Data 分析流程 Differentially expressed genes, proteins (normalization, QC)

6. Functional analysis tools Enrichment analysis for gene, protein, compound sets Hyper G, GSEA, GSA etc. Multidimensional analysis: multiple ontologies GO processes GG processes Canonical pathways Diseases Export of sub-sets for network analysis Low resolution Experiment filters • Species, orthologs, localizations, tissues etc. • Custom list of targets, IDs Resolution 1000 genes; Multiple sets Interactome analysis • Whole-set analysis • Over- and underconnected nodes in the dataset • Interactions neighborhood • TFs, kinases, receptors, etc. • Scoring for interactions within set: FDR Network analysis • Multiple pre-filters (species, interactions mechanisms, organelles etc.) • Parameters: enrichment with genes from set, canonical pathways, specific protein classes • Algorithms: SP, DI, AN, TFs, Receptors etc. • Statistics: hubs, preferred pathways etc. • Highest resolution: individual proteins or isoforms “Most important” genes - Highly connected TFs, receptors, etc. • Hubs from important networks • Highest expressed/mutated genes

7. Concurrent visualization of different data types, experiments Agilent Affymetrix Proteomic SAGE

8. Outline Introduction 由成大生資中心進入：使用前設定以及注意事項 簡介使用方法  Upload Data  Analyze Data  快速workflow  MapEditor 試用帳號 實際操作 http://www.binfo.ncku.edu.tw/2010_genomics/

10. 使用者電腦設定

12. Outline Introduction 由成大生資中心進入：使用前設定以及注意事項 簡介使用方法  Upload Data  Analyze Data  快速workflow  MapEditor 試用帳號 實際操作

19. 上傳成功！

21. early s phase list

32. http://training.genego.com/

33. http://training.genego.com/ • 上傳檔案 藍色資料夾 • 下載 gene list

34. Outline Introduction 由成大生資中心進入：使用前設定以及注意事項 簡介使用方法  Upload Data  Analyze Data  快速workflow  MapEditor 試用帳號 實際操作

35. Analysis data 分析前注意事項

36. Remove data from Exp. File to yourself file

39. GS875 Active Data

41. Analysis data GeneGo Pathway Maps

44. 如何找出這些genes list?

45. 往下拉！

46. 12 genes

48. Analysis data GeneGo Diseases (by Biomarkers)