Filtering and Normalizationof Microarray Gene Expression DataWaclaw KusnierczykNorwegian University of Science and Technology Trondheim, Norway

OutlineFiltering: spotsremoval of spots based on quality measuresNormalizationcompensation for measurement errorsExamples of common problems

Useful plotsChannel - channel plot (CC)

Intensity - ratio plot (AM or IR)

Filtering: SpotsCriteria used to remove spotsspot area [pixels]signal/noise ratio (spot intensity vs. background intensity)other quality measures (e.g. based on quality scores from image analysis software)morphological criteriapixel-level variability

Filtering: SpotsSpot area

Filtering: SpotsSpot area based filteringkeep spots with area > threshold in both channelsproblem: setting the appropriate thresholddependent on the definition of the spot (image analysis software), and the distribution of the spot areatypical value: 10 pixels

Filtering: SpotsSignal and background

Filtering: SpotsSignal/noise based filteringkeep spots with signal / background > threshold in both channelsproblem: setting the appropriate thresholddependent on the spot and background definition (image analysis software)typical value: sgn/bkg > 2 (or, equivalent, sgn - bkg > bkg)

Filtering: SpotsSignal/noise based filtering (alternative)flag spots if Sij< Bij+Bij, where: Sij: ith spot intensity in jth channel (not corrected) Bij: ith spot background in jth channel Bij: ith spot background deviation in jth channel : user defined threshold

Filtering: Spots (example)

Filtering: SpotsOther criteriaIntensity threshold on background corrected intensity (for each channel separately)Spot quality measures (pixelwise distributional properties of spot and background intensities, manual morphology-based spot flagging etc.)Replicate-based spot filtering (adaptive threshold selection based on a repeatability coefficient, coefficient of variation etc.)

Filtering: SpotsTotal intensity (log2) threshold

Filtering: SpotsMorphology based filtering

NormalizationAnalysis of systematic errorsadjustment for bias coming from variation in the technology rather than from biologyDifferent sources of non-linearityPrint-tip differencesEfficiency of dye incorporation (labelling)Non-uniformity in hybridisationScanningBetween slide variation (print quality, ambient conditions)

NormalizationSelection of elements Housekeeping genes, spike controls, tip-dependence, raw data, between array normalizationMethodConstant subtraction (shift) (mean/median log2 ratio, iterative c estimation, ANOVA)Locally weighted mean (intensity or location dependent)Other recently proposed methods

Normalization (example 1)Intensity independent normalization with median ratio subtraction

Normalization (example 1)Intensity independent normalization with median ratio subtraction

Normalization (example 1)Intensity dependent normalization with locally weighted mean, global

Normalization (example 1)Intensity dependent normalization with locally weighted mean, print-tip dependent

Normalization (example 1)Intensity dependent normalization with locally weighted mean, global vs. print-tip dependent

Normalization (example 2)Intensity dependent normalization with locally weighted mean, print-tip dependent

NormalizationLocation dependent normalization with locally weighted mean(from SNOMAD web page)

Common problems: examples

Common problems: examples

Common problems: examples

Common problems: examples

Common problems: examples

Common problems: examples

Common problems: examples

AcknowledgmentsMette LangaasDepartment of Mathematical Sciences, Norwegian Institute of Science and TechnologyAstrid Lgreid, Kristin NrsettDepartment of Physiology and Biomedical Engineering, Norwegian Institute of Science and TechnologyPer Kristian LehreDepartment of Computer and Information Science, Norwegian Institute of Science and Technology