14vReview%203_11_08

14vReview%203_11_08 - Microarray Experiments Review Peng...

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1 1 Review Peng Liu 3/11/2008 2 Microarray Experiments ± Designing the experiment ± Perform experiments and get the image ± Data analysis (image data processing, normalization, fitting linear models, statistical inference) ± Biological validation 3 Experimental Design ± Designs covered in class: Completed Randomized Design Randomized Complete Block Design Incomplete Block Design (including BIBD) Split-plot Design Latin Square Design 4 Designing Affy microarray ± Sample from one observational unit onto one GeneChip ± Possible technical replication or pooling of samples 5 Designing 2-color microarray (3 layers) From Churchill, 2002, nature genetics 6 Pre-normalization analysis ± Image processing obtain the intensity measurement of the signal ± Background correction get rid of local background that might due to non- specific binding and obtain the target sample intensity ± Filtration remove unreliable spots and reduce the dimension of data ± Transformation convert data into a format that makes data analysis valid or easier
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2 7 Normalization ± Normalization describes the process of removing (or minimizing) non-biological variation in measured signal intensity levels so that biological differences in gene expression can be appropriately detected. ± Aim : remove sources of systematic variation ± Example of non-biological variation: dye difference 8 Normalization ± 2-color microarray Within slide normalization Location-scale normalization Quantile normalization ± Affymetrix normalization MAS5.0 RMA 9 Figure 2 from Dudoit et al, 2002, Statistica Sinica 10 LOWESS Fit ( Log Green + Log Red )/2 Log Red - Log Green 11 After normalization A Normalized M 12 Location-scale normalization ± Location adjustment: subtracting some function c(.) so that the “ center ”( mean or median ) of different channels are comparable. The LOWESS normalization is one kind of location adjustment. The function c(.) depends on the average intensity In LOWESS. ± Scale adjustment: multiplying some function a(.) so that the “ spread variance ) of different channels are comparable.
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3 13 Affy: MAS 5.0 ± A ‘local’ background estimate with weighted average of background
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14vReview%203_11_08 - Microarray Experiments Review Peng...

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