One channel microarray antibody analysis
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Last seen 3.5 years ago
Dear BioC users, I am trying to analyze 4 antibody arrays (hypromatrix) with 400 spots, which are stained with the HRP system (images in black and white with an important background in my case) [for more information about the arrays ]. The images were obtained with an standard scanner with high resolution. I have analyzed the TIFF images with GenePix, as it was a Cy3 image, to obtain the signal and the background which corresponds to each spot. All the 4 arrays are four different conditions, 1 control and 3 conditions (neither technical nor biological replicates) and there aren?t control spots. Nevertheless, it?s expected that many of the spots don?t change its expression (I am very conscious that the design is very weak). I have considered the 4 different arrays as 3 arrays of 2 colors: Array---Cy3---------Cy5 1--------Reference---Condition1 2--------Reference---Condition2 3--------Reference---Condition3 I have normalized within and between the arrays and I have calculated the fold change. Is my analysis correct or should I proceed in a different way? Many thanks in advance for your advices. Yours faithfully, Jordi Altirriba PhD student Hospital Clinic, Barcelona, Spain This is the code that I have used: >library(limma) >targets <- readTargets() >targets SlideNumber FileName Cy3 Cy5 Date standard_cond1 1 standard_cond1.txt Ref cond1 7/6/2006 standard_cond2 2 standard_cond2.txt Ref cond2 7/6/2006 standard_cond3 3 standard_cond3.txt Ref cond3 7/6/2006 >RG <- read.maimages(targets$FileName, columns=list(R="F635 Mean",G="F532 >Mean",Rb="B635 Median",Gb="B532 >Median"),annotation=c("Block","Row","Column","ID","Name")) >RGb <- backgroundCorrect(RG, method="subtract") >MA <- normalizeWithinArrays(RGb, method="loess") >MA.q <- normalizeBetweenArrays(MA, method="quantile") >sink(?results.txt?) >MA.q$M >sink() >MA.q An object of class "MAList" $targets FileName standard_ cond1 standard_ cond1.txt standard_ cond2 standard_ cond2.txt standard_ cond3 standard_ cond3.txt $genes Block Row Column ID Name 1 1 1 1 14,3,3 14,3,3 2 1 2 1 c-Abl c-Abl 398 more rows ... $source [1] "generic" $M standard_cond1 standard_cond2 standard_cond3 [1,] -0.5339283 -0.31473618 -0.8712970 [2,] 0.5333891 0.17447885 0.1897565 398 more rows ... $A standard_cond1 standard_cond2 standard_cond3 [1,] 12.92027 12.79477 13.06500 [2,] 12.82701 13.04232 13.04360 398 more rows ... This is my R session: >sessionInfo() Version 2.3.0 (2006-04-24) i386-pc-mingw32 attached base packages: [1] "methods" "stats" "graphics" "grDevices" "utils" "datasets" "base" other attached packages: limma "2.7.2"

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