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Microarray Data Analysis Using BASE

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  1. Microarray Data Analysis Using BASE Danny Park MGH Microarray Core March 15, 2004

  2. You’ve got data! • What was I asking? – remember your experimental design • How do I analyze the data? • How do I find interesting stuff? – learn some analysis tools • How do I trust the results? – statistics is key

  3. What was I asking? • Typically: “which genes changed expression levels when I did ____” • Common ____: • Binary conditions: knock out, treatment, etc • Continuous scales: time courses, levels of treatment, etc • Unordered discrete scales: multiple types of treatment or mutations • This tutorial’s focus: binary experiments

  4. How do I analyze the data? • BASE – BioArray Software Environment • Data storage and distribution • Simple filtering, normalization, averaging, and statistics • Export/Download results to other tools • MS Excel • TIGR Multi Experiment Viewer (TMEV) • This tutorial’s focus: using BASE

  5. Today’s Presentation • Demonstrate the most basic analysis techniques • Using our most frequently used software (BASE) • For the most common kind of experiments

  6. QC & label RNA Labeled cDNA hybridize Slides Researcher scan, segment BASE Images & data files upload Work Flow analysis

  7. The Most Common experiment • Two-sample comparison w/N replicates • KO vs. WT • Treated vs. untreated • Diseased vs. normal • Etc • Question of interest: which genes are (most) differentially expressed?

  8. A B Experimental Design – naïve From Gary Churchill, Jackson Labs

  9. A B Experimental Design – tech repl From Gary Churchill, Jackson Labs

  10. A A B B Experimental Design – bio repl • Treatment • Biological Replicate • Technical Replicate • Dye • Array From Gary Churchill, Jackson Labs

  11. The Most Common Analysis • Filter out bad spots • Adjust low intensities • Normalize – correct for non-linearities and dye inconsistencies • Filter out dim spots • Calculate average fold ratios and p-values per gene • Rank, sort, filter, squint, sift data • Export to other software

  12. BASE @ MGH • BASE is a microarray data storage and analysis package • BASE resides on our web server • Data is stored at our facility • Computation is performed on our machines • All you need is a web browser • https://base.mgh.harvard.edu/ • A Microarray Core technician will provide you with a username, password, and experiment name

  13. BASE – Login page

  14. BASE – Login page

  15. BASE – Login page

  16. BASE – Login page

  17. BASE – Logged in

  18. BASE – Logged in

  19. Reporters BASE – Sidebar

  20. Reporters BASE – Sidebar

  21. Array LIMS BASE – Sidebar

  22. Array LIMS BASE – Sidebar

  23. Biomaterials BASE – Sidebar

  24. Biomaterials BASE – Sidebar

  25. Hybridizations BASE – Sidebar

  26. Hybridizations BASE – Sidebar

  27. Analyze Data BASE – Sidebar

  28. Analyze Data BASE – Sidebar

  29. Users BASE – Sidebar

  30. Users BASE – Sidebar

  31. BASE – My Account Change your password and access defaults

  32. BASE – My Account Change your password and access defaults

  33. BASE – My Account Change your password and access defaults

  34. BASE – My Account Change your password and access defaults

  35. Find your experiment

  36. Find your experiment

  37. Find your experiment

  38. Find your experiment

  39. Experiment view: Four Tabs

  40. Experiment view: Four Tabs

  41. Experiment view: Four Tabs

  42. Experiment view: Four Tabs

  43. Experiment view: Four Tabs

  44. Experiment view: Four Tabs

  45. Experiment view: Four Tabs

  46. Experiment view: Four Tabs

  47. Group slide data together

  48. Group slide data together Select the slides that measure the same thing. Later in analysis, they will be averaged together. In this experiment, all ten slides are replicates, so there is only one grouping.

  49. Group slide data together Select the slides that measure the same thing. Later in analysis, they will be averaged together. In this experiment, all ten slides are replicates, so there is only one grouping.

  50. Group slide data together Select the slides that measure the same thing. Later in analysis, they will be averaged together. In this experiment, all ten slides are replicates, so there is only one grouping.