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Microarray Data Analysis

Microarray Data Analysis . Data quality assessment and normalization for affymetrix chips. Outline. Visualization and diagnostic plots Normalization Filtering. Microarray studies life cycle. Here we are. Looking at microarray data Diagnostic Plots. Was the experiment a success? .

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Microarray Data Analysis

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  1. Microarray Data Analysis Data quality assessmentand normalization for affymetrix chips

  2. Outline • Visualization and diagnostic plots • Normalization • Filtering

  3. Microarray studies life cycle Here we are

  4. Looking at microarray dataDiagnostic Plots Was the experiment a success?

  5. Diagnostic plots for cDNA chips • Plots can be used to check microarray quality • Some plots useful for both cDNA or Affy. • Scatterplot / MA plot • Histograms • Spatial plots • Box plots • Other are more technology specific • Degradation plots

  6. Image plots for affymetrix chips

  7. MA-plot for GeneChip arrays (1 color) MT intensity for each probe set aRNA RMA M Log2(MT)-Log2(WT) MT WT intensity for each probe set aRNA RMA WT A Log2(MT*WT) / 2 (signal strength)

  8. Box plots

  9. Density plots

  10. Digestion plots

  11. Preprocessing affy chips

  12. Preprocessing steps • Computing expression values for each probe set requires 3-steps • Background correction • Normalization • Probe set summaries

  13. Most popular approaches • Affymetrix’s own MAS 5 or GCOS 1.0 algorithms • RMA (Robust Multichip Analysis) • Irizarry, Bolstad, Collin, Cope, Hobbs, Speed (2003), Summaries of Affymetrix GeneChip probe level data. NAR 31(4):e15 • dChip http://www.dchip.org: Li and Wong (2001). Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection. PNAS 98, 31-

  14. MAS • Background correction • Ej = PMj - MMj* where MMj* is chosen so that Ej is non-negative • Normalization • Scale so that mean Ej is same for each chip • Probe Set Summary • log(Signal Intensity) = TukeyBiweight(log Ej)

  15. RMA- Background correction • Ignore MM, fit model to PM • PM = Background (N(0,s2) + Signal (Exp(a))

  16. RMA-Normalization • Force the empirical distribution of probe intensities to be the same for every chip in an experiment • The common distribution is obtained by averaging each quantile across chips: Quantile Normalization

  17. One distribution for all arrays: the black curve

  18. RMA: Probe set summary • Robustly fit a two-way model yielding an estimate of log2(signal) for each probe set • Fit may be by • median polish (quick) or by • Mestimation (slower but yields standard errors and good quality • RMA reduces variability without loosing the ability to detect differential expression

  19. MA plots before and after RMA

  20. Summary • Microarray experiments have many “hot spots” where errors or systematic biases can apper • Visual and numerical quality control should be performed • Usually intensities will require normalisation • At least global or intensity dependent normalisation should be performed • More sophisticated procedures rely on stronger assumptions Must look for a balance

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