Preprocessing of cDNA microarray dataPowerPoint PresentationSlide 4Slide 5Slide 6Slide 7Slide 8Boxplots and highlightingSlide 10Slide 11Slide 12Slide 13NormalizationSlide 15Slide 16Slide 17Slide 18Slide 19Slide 20Normalization: methodsSlide 22Which spots to use for normalization?Use of control spotsSlide 25MSP titration series (Microarray Sample Pool)Slide 27Composite normalizationComparison of Normalization Schemes (courtesy of Jason Goncalves)Slide 30Slide 31Slide 32Slide 33Slide 34Slide 35Scale normalization: another data setOne way of taking scale into accountA slightly harder normalization problemPrint-tip-group normalization helpsBut not completelyEffects of previous normalisationWithin print-tip-group box plots of M after print-tip-group normalizationTaking scale into account, cont.Effect of location & scale normalizationSlide 45The same idea on another data setSlide 47Paired-slides: dye-swapChecking the assumptionResult of self-normalizationSummary of normalizationWhat is missing?AcknowledgmentsSlide 54Preprocessing of cDNA microarray dataPreprocessing of cDNA microarray dataLecture 19, Statistics 246, April 1, 2004Was the experiment a success? What analysis tools should be used? Are there any specific problems?Begin by looking at the dataBegin by looking at the dataRed/Green overlay imagesGood: low bg, lots of d.e. Bad: high bg, ghost spots, little d.e.Co-registration and overlay offers a quick visualization,revealing information on color balance, uniformity ofhybridization, spot uniformity, background, and artifiactssuch as dust or scratchesAlways log, always rotatelog2R vs log2G M=log2R/G vs A=log2√RGSignal/Noise = log2(spot intensity/background intensity)HistogramsBoxplots of log2R/G Liver samples from 16 mice: 8 WT, 8 ApoAI KO.Spatial plots: background from the two slidesHighlighting extreme log ratiosTop (black) and bottom (green) 5% of log ratiosBoxplots and highlightingBoxplots and highlighting Clear example of spatial bias (here high is red, low green)Print-tip groupsLog-ratiospin group #Pin group (sub-array) effectsBoxplots of log ratios by pin groupLowess lines through points from pin groupsPlate effectsKO #8Probes: ~6,000 cDNAs, including 200 related to lipid metabolism. Arranged in a 4x4 array of 19x21 sub-arrays.Time of printing effectsGreen channel intensities (log2G). Printing over 4.5 days.The previous slide depicts a slide from this print run.spot numberNormalizationNormalization Why? To correct for systematic differences between samples on the same slide, or between slides, which do not represent true biological variation between samples.How do we know it is necessary? By examining self-self hybridizations, where no true differential expression is occurring.We find dye biases which vary with overall spot intensity, location on the array, plate origin, pins, scanning parameters,….Self-self hybridizationsFalse color overlay Boxplots within pin-groups Scatter (MA-)plotsFrom the NCI60 data set (Stanford web site)A series of non self-self hybridizationsEarly Ngai lab, UC BerkeleyEarly Goodman lab, UC BerkeleyFrom the Ernest Gallo Clinic & Research CenterEarly PMCRI, Melbourne AustraliaNormalization: methodsNormalization: methods a) Normalization based on a global adjustment log2 R/G -> log2 R/G - c = log2 R/(kG) Choices for k or c = log2k are c = median or mean of log ratios for a particular gene set (e.g. housekeeping genes). Or, total intensity normalization, where k = ∑Ri/ ∑Gi. b) Intensity-dependent normalization. Here we run a line through the middle of the MA plot, shifting the M value of the pair (A,M) by c=c(A), i.e. log2 R/G -> log2 R/G - c (A) = log2 R/(k(A)G). One estimate of c(A) is made using the LOWESS function of Cleveland (1979): LOcally WEighted Scatterplot Smoothing.Normalization: methodsNormalization: methodsc) Within print-tip group normalization. In addition to intensity-dependent variation in log ratios, spatial bias can also be a significant source of systematic error. Most normalization methods do not correct for spatial effects produced by hybridization artifacts or print-tip or plate effects during the construction of the microarrays. It is possible to correct for both print-tip and intensity-dependent bias by performing LOWESS fits to the data within print-tip groups, i.e. log2 R/G -> log2 R/G - ci(A) = log2 R/(ki(A)G), where ci(A) is the LOWESS fit to the MA-plot for the ith grid only.Which spots to use for normalization?Which spots to use for normalization? The LOWESS lines can be run through many different sets of points, and each strategy has its own implicit set of assumptions justifying its applicability. For example, we can justify the use of a global LOWESS approach by supposing that, when stratified by mRNA abundance, a) only a minority of genes are expected to be differentially expressed, or b) any differential expression is as likely to be up-regulation as down-regulation. Pin-group LOWESS requires stronger assumptions: that one of the above applies within each pin-group. The use of other sets of genes, e.g. control or housekeeping genes, involve similar assumptions.Use of control spotsUse of control spotsM = log R/G = logR - logG A = ( logR + logG) /2Positive controls(spotted in varying concentrations) Negative controlsblanksLowess curveGlobal scale, global lowess, pin-group lowess; spatial plot after, smooth histograms of M afterMSP titration seriesMSP titration series((Microarray Sample PoolMicroarray Sample Pool))Control set to aid intensity- dependent normalizationDifferent concentrationsSpotted evenly spread across the slidePool the whole libraryYellow: GAPDH, tubulin Light blue: MSP pool / titrationOrange: Schadt-Wong rank invariant set Red line: lowess smooth MSP normalization compared to other methodsComposite normalizationComposite normalizationBefore and after composite normalization-MSP lowess curve-Global lowess curve-Composite lowess curve(Other colours control spots)ci(A)=Ag(A)+(1-A)fi(A)Comparison of Normalization SchemesComparison of Normalization Schemes(courtesy of Jason Goncalves)(courtesy of Jason Goncalves) No consensus on best segmentation or normalization method Scheme was applied to assess the common normalization methods Based on reciprocal labeling experiment data for a series of 140 replicate experiments on two different arrays each with 19,200 spotsDESIGN OF RECIPROCALDESIGN OF
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