07%20Normalization%20cDNA%201_31_08

07%20Normalization%20cDNA%201_31_08 - In last lecture...

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1 1 Normalization Methods for Two-Color Microarray Data (continued) 1/31/2008 Peng Liu 2 In last lecture ± Within-slide normalization Intensity-dependent dye effect LOWESS normalization ± LOWESS can be applied to each sub-grid (sector / print-tip group) when there is sub- grid specific pattern of dye effect 3 Log Green Log Red Data from 3 Sectors on a Single Slide 4 Result from LOWESS: ± Suppose the fitted value from LOWESS for spot j is M j -hat. ± Normalized M values: ± Average log intensity: A j stays the same ± You can transform to the normalized logR and logG.
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2 5 Location-scale normalization ± Location adjustment: subtracting some function c(.) so that the “ center ”( )o f 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 ” ( ) of different channels are comparable. 6 Location-scale normalization ± This can be applied to the data from two dyes for the same slide, or all channels ( Channel is used to refer to a combination of a dye and a slide), or different sub-grids for the same channel 7 Side-by-side boxplots show examples of variation across channels. 8 Slide 2 Cy3 Cy5 Slide 1 Cy3 Cy5 median Q3=75 th percentile Q1=25 th percentile minimum
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3 9 Interquartile range (IQR) is Q3-Q1. Points more than 1.5*IQR above Q3 or more than 1.5*IQR below Q1 are displayed individually. median Q3=75 th percentile Q1=25 th percentile minimum maximum 10 R commands to generate the side-by-side boxplots boxplot(as.data.frame(X), xlab="Channel",ylab="Log Signal", axes=F) axis(1,labels=1:ncol(X),at=1:ncol(X)) axis(2) box() X is a matrix with one column for each channel. Element i,j of the matrix is the background corrected log-transformed value for the i th gene on the j th channel. If the matrix X has other columns that you don’t want to deal with, you may pick out the columns that you want or delete those you don’t want. For example, X[,c(1,2,3,6)] (only work with columns 1,2,3 and 6) or X[,-1] (all columns except the first column). 11 Location normalization ± One of the simplest strategies is to align the log signals so that all channels have the same median. ± The value of the common median is not important for
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07%20Normalization%20cDNA%201_31_08 - In last lecture...

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