I Just Received My Microarray Data, Now What Danny Park MGH-PGA (ParaBioSys) Sa

I Just Received My Microarray Data, Now What Danny Park MGH-PGA (ParaBioSys) Sa www.phwiki.com

I Just Received My Microarray Data, Now What Danny Park MGH-PGA (ParaBioSys) Sa

Greenberg, Paul, Contributing Writer has reference to this Academic Journal, PHwiki organized this Journal I Just Received My Microarray Data, Now What Danny Park MGH-PGA (ParaBioSys) Sat April 24, 2004 I Just Received My Microarray Data Where did this come from A description of the process from RNA to raw data What do I do now A description of how to analyze your data Demystifying the Core Facility Microarray Core Facility (black magic) Strange numbers in addition to pictures RNA (precious)

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Demystifying the Core Facility Images & data files scan, segment upload DB Labeled cDNA Slides QC, RT & label hybridize RNA analysis Approximately 20 mg total RNA required AAAAAAAA TTTT cDNA Aminoallyl-dUTP, dATP, dCTP, dGTP, oligo-dT primer, reverse transcriptase TTTT NH2 NH2 NH2 mRNA TTTT N-hydroxysuccinimide activated fluorescent dye RNase treatment or NaOH hydrolysis of RNA AAAAAAAA TTTT cDNA TTTT NH2 NH2 NH2 mRNA TTTT Cy3 Cy5 Reference sample Test sample Reference labeled cDNA Test labeled cDNA RT & Labeling Genomic Solutions Hybridization Station (PerkinElmer) Robotic printing Hybridization Microarray Hybridization Combine Synthesized oligonucleotides in 384 well plates Reference labeled cDNA Test labeled cDNA

Axon Instruments GenePix 4000B Scanning Hybridized Microarray Laser 2 Laser 1 Monochrome pictures combined Emission Excitation 20 mg total RNA macrophage RAW Cy5: 100 ng/mL LPS 2 h Cy3: no treatment Scanning Segmentation Scanned Image Numerical Data Segmentation Software

Segmentation Segmentation Segmentation

Segmentation Core Facility – Demystified! Images & data files scan, segment upload DB Labeled cDNA Slides QC, RT & label hybridize RNA analysis Core Facility – Demystified Images & data files scan, segment upload DB Labeled cDNA Slides QC, RT & label hybridize RNA analysis

What Do I Do Now (data analysis) What was I asking Remember your experimental design How do I analyze the data Learn some typical filters, trans as long as mations, in addition to statistics Learn the necessary software tools Consult biostatistician What Was I Asking Typically: “which genes changed expression patterns when I did -” Common -’s: Binary conditions: knock out, treatment, etc Unordered discrete scales: multiple types of treatment or mutations Continuous scales: time courses, levels of treatment, etc My focus: binary conditions (aka “diagnostic experiments”) Diagnostic Experiments Two-sample comparison w/N replicates KO vs. WT Treated vs. untreated Diseased vs. normal Etc Question of interest: which genes or groups are (most) differentially expressed

Software Tools BASE – BioArray Software Environment Data storage in addition to distribution Simple filtering, normalization, averaging, in addition to statistics Export/Download results to other tools R, Bioconductor (complex statistics) MS Excel (general) TIGR Multi Experiment Viewer (clustering) GenMAPP (ontologies) Analyzing a Diagnostic Experiment Filter out bad spots Adjust low intensities Normalize – correct as long as non-linearities in addition to dye inconsistencies Calculate average ratios in addition to significance values per gene Rank, sort, filter, squint, sift data Validate results Filtering bad spots – Why

Filtering bad spots Filtering bad spots Filter Adjusting low intensities – Why T C

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Adjusting low intensities – Why Poof! LOWESS Normalization Data is gone! log(T), log(C) T C Adjusting low intensities T C T C Adjusting low intensities Int Limit

Normalization – Why Not perfectly centered around zero Implies that nearly all genes down regulated There are dye effects Normalization – Why Regional variations Up (red) in addition to down (green) regulated genes should be r in addition to omly distributed across the slide (but they’re not) Green corner! Normalization LOWESS

The End! – What Have We Not Covered Different flavors of filters, normalizations in addition to stat. significance metrics (10:45a) Analysis of time course & multiple treatment experiments (1:00p) Clustering, visualization methods (1:00p) Step by step tutorial of software (1:45p) Acknowledgements MGH Lipid Metabolism Unit Mason Freeman Harry Björkbacka BU BioMolecular Engineering Research Center Temple Smith Gabriel Eichler Sean Quinlan Prashanth Vishwanath MGH Microarray Core Glenn Short Jocelyn Burke Najib El Messadi Jason Frietas Zhiyong Ren MGH Molecular Biology Bioin as long as matics Group Chuck Cooper Xiaowei Wang Harvard School of Public Health Biostatistics Xiaoman Li

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