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Educational Initiatives and Data Analysis in the Microarray Core

Educational Initiatives and Data Analysis in the Microarray Core. Danny Park Bioinformatics (Sidney St) Lipid Metabolism Unit (Freeman). Hats I Wear. Software development, maintenance, support for Microarray Core Learning & recommending analysis techniques, algorithms, and software tools

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Educational Initiatives and Data Analysis in the Microarray Core

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  1. Educational Initiatives and Data Analysis in the Microarray Core Danny Park Bioinformatics (Sidney St) Lipid Metabolism Unit (Freeman)

  2. Hats I Wear • Software development, maintenance, support for Microarray Core • Learning & recommending analysis techniques, algorithms, and software tools • Training, teaching, educating researchers • Misc. bioinformatics

  3. Today’s Presentation • Demonstrate the most basic analysis techniques • Using our most commonly used software (BASE) • For the most common kind of experiments • While pretending you’re a typical audience (biologists, inexperienced with microarrays)

  4. QC & label RNA Labeled cDNA hybridize Slides Researcher Me scan, segment BASE Images & data files upload Work Flow training / feedback development analysis

  5. Researcher Me Work Flow, distilled training / feedback analysis BASE

  6. Researchers BASE storage analysis BASE for Bioinformatics People“BioArray Software Environment” • Typical web/client-server model: • Clients: javascript-enabled browser • Web server: Apache/PHP, some C++, Perl, R code for analysis • DB server: MySQL for storage • Open source, headed by thep.lu.se • Functions: • Data storage, archival • Basic analysis: filters, transformations, normalizations, graphs, etc • Not so good: clustering, visualization

  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

  9. A B Experimental Design – tech repl

  10. A A B B Experimental Design – bio repl • Treatment • Biological Replicate • Technical Replicate • Dye • Array

  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. Live Demo this space intentionally left blank

  13. Mason Freeman Harry Bjorkbacka Glenn Short Jocelyn Burke Najib El Messadi Jing Wang Chuck Cooper Xiaowei Wang Xiaoman Li (HSPH) Acknowledgements Stolen powerpoint figures from Gary Churchill (Jackson Labs)

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