TechnologyChallengesData AnalysisData DepositoriesR and BioConductorHomework AssignmentMicroarray AnalysisThe BasicsThomas GirkeDecember 9, 2011Microarray Analysis Slide 1/42TechnologyChallengesData AnalysisData DepositoriesR and BioConductorHomework AssignmentMicroarray Analysis Slide 2/42OutlineTechnologyChallengesData AnalysisData DepositoriesR and BioConductorHomework AssignmentMicroarray Analysis Technology Slide 3/42Microarray and Chip TechnologyDefinitionHybridization-based technique that allows simultaneousanalysis of thousands of samples on a solid substrate.ApplicationsTranscriptional ProfilingGene copy numberResequencingGenotypingSingle-nucleotide polymorphismDNA-protein interaction (e.g.: ChIP-on-chip)Gene discovery (e.g.: Tiling arrays)Identification of new cell linesEtc.Related technologiesProtein arraysCompound arraysMicroarray Analysis Technology Slide 4/42Why Microarrays?Simultaneous analysis of thousands of genesDiscovery of gene functionsGenome-wide network analysisAnalysis of mutants and transgenicsIdentification of drug targetsCausal understanding of diseasesClinical studies and field trialsMicroarray Analysis Technology Slide 5/42Different Types of MicroarraysSingle channel approachesAffymetrix gene chipsMacroarraysMultiple channel approachesDual color (cDNA) microarraysSpecialty approachesBead arrays: Lynx, Illumina, ...PCR-based profiling: CuraGen, ...Microarray Analysis Technology Slide 6/42Dual Color MicroarraysMicroarray Analysis Technology Slide 7/42Affymetrix DNA ChipsMicroarray Analysis Technology Slide 8/42OutlineTechnologyChallengesData AnalysisData DepositoriesR and BioConductorHomework AssignmentMicroarray Analysis Challenges Slide 9/42Profiling Chips Monitor Differences of mRNA LevelsEfficient strategy for down-stream follow-up experimentsimportant!Microarray Analysis Challenges Slide 10/42Strategies to Validate Array HitsReal-time PCR, Northern, etc.Transgenic testsKnockout plants and/or activation tagged linesProtein profilingMetabolic profilingOther tests: in situ hybs, biochemical and physiological testsIntegration with sequence, proteomics and metabolicdatabasesMicroarray Analysis Challenges Slide 11/42Sources of Variation in Transcriptional ProfilingExperimentsEvery step in transcriptional profiling experiments cancontribute to the inherent ’noise’ of array data.Variations in biosamples, RNA quality and target labeling arenormally the biggest noise introducing steps in arrayexperiments.Careful experimental design and initial calibration experimentscan minimize those challenges.Microarray Analysis Challenges Slide 12/42Experimental DesignBiological questions:Which genes are expressed in a sample?Which genes are differentially expressed (DE) in a treatment, mutant,etc.?Which genes are co-regulated in a series of treatments?Selection of best biological samples and referenceComparisons with minimum number of variablesSample selection: maximum number of expressed genesAlternative reference: pooled RNA of all time points (saves chips)Develop validation and follow-up strategy for expected expression hitse.g. real-time PCR and analysis of transgenics or mutantsChoose type of experimentcommon reference, e.g.: S1 x S1+T1, S1 x S1+T2paired references, e.g.: S1 x S1+T1, S2 x S2+T1loop & pooling designsmany other designsAt least three (two) biological replicates are essentialBiological replicates: utilize independently collected biosamplesTechnical replicates: utilize often the same biosample or RNA poolMicroarray Analysis Challenges Slide 13/42OutlineTechnologyChallengesData AnalysisData DepositoriesR and BioConductorHomework AssignmentMicroarray Analysis Data Analysis Slide 14/42Basic Data Analysis StepsImage Processing: transform feature and background pixelinto intensity valuesTransformationsRemoval of flagged values (optional)Detection limit (optional)Background subtractionTaking logarithmsNormalizationIdentify EGs and DEGsWhich genes are expressed?Which genes are differentially expressed?Cluster analysis (time series)Which genes have similar expression profiles?Promoter analysisIntegration with functional information: pathways, etc.Microarray Analysis Data Analysis Slide 15/42Image AnalysisOverall slide qualityGrid alignment (linkage between spots and feature IDs)Signal quantification: mean, median, threshold, etc.Local backgroundManual spot flaggingExport to text fileImage analysis software (selection)ScanAlyze (http://rana.lbl.gov/EisenSoftware.htm)TIGR SpotFinder (http://www.tigr.org/software/)Microarray Analysis Data Analysis Slide 16/42Background CorrectionFiltering (optional)Intensities below detection limitNegative intensitiesSpacial quality issuesBackground correctionBG consists of non-specific hybridization and backgroundfluorescenceIf BG is higher than signal: (1) remove values, (2) set signal tolowest measured intensity, (3) many other approachesBG subtractionLocal backgroundGlobal backgroundNo background subtractionBackground subtraction can cause ratio inflation, thereforebackground corrected intensities below threshold are often setto threshold or similar value.Microarray Analysis Data Analysis Slide 17/42NormalizationNormalization is the process of balancing the intensities of thechannels to account for variations in labeling and hybridizationefficiencies. To achieve this, various adjustment strategies are usedto force the distribution of all ratios to have a median (mean) of 1or the log-ratios to have a median (mean) of 0.Microarray Analysis Data Analysis Slide 18/42Log Transformation: Scatter PlotsReasons for working with log-transformed intensities and ratios(1) spreads features more evenly across intensity range(2) makes variability more constant across intensity range(3) results in close to normal distribution of intensities and experimental errorsMicroarray Analysis Data Analysis Slide 19/42Log Transformation: HistogramsDistribution of log transformed data is closer to being bell-shapedMicroarray Analysis Data Analysis Slide 20/42Normalization If Large Fraction of Genes IS DEMinimize normalization requirements (dynamic range limits)Pre-scanning: hybridize equal amounts of labelDuring scanning: balance average intensities through laserpower and PMP adjustmentsNormalization if large fraction of genes is DESpike-in controlsHousekeeping controlsDetermine constant feature setMicroarray Analysis Data Analysis Slide 21/42Normalization If Large Fraction of Genes IS NOT DEGlobal Within-Array NormalizationMultiply one channels