Agilent time course with technical replicates, differential expression using Limma
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Matt Huska ▴ 10
@matt-huska-2998
Last seen 11.2 years ago
Hi all, I have been given the microarray data to analyze for a 4 point time course experiment with two technical replicates (dye swaps) and no biological replicates. One channel is has RNA from a control mouse and the other has RNA from a mouse infected with a treatment of interest. The platform is the 4x44k Agilent whole mouse oligoarray. I am trying to use Limma to determine significantly differentially expressed probes over the whole time series. First, is it even possible to do this with the given experimental design? I have only worked with Affymetrix chips with biological replicates, so this type of experimental design is new to me. If it is, then I could still use some help. I read the section of the Limma User's Guide (2 Jan 2008) on Dye Swaps, Technical Replication and Time Course Experiments but am having trouble putting the ideas together. Here is my targets table: filename timepoint cy3 cy5 1_1 h1 c t 1_2 h4 c t 1_3 h8 c t 1_4 h16 c t 2_1 h1 t c 2_2 h4 t c 2_3 h8 t c 2_4 h16 t c I believe this is correct as far as normalization is concerned: targets <- readTargets(file="Targets-invitro.txt"); RG <- read.maimages(targets$filename, source="agilent", path="../input/MAGEML+txt/Annabell", ext="txt") MA <- normalizeWithinArrays(RG, method="loess") MA <- normalizeBetweenArrays(MA, method="scale") but I am unsure what to do for a design and contrasts matrix. This is my best guess: TS <- factor(targets$timepoint) design <- model.matrix(~0+TS) # Multiple 2nd half of matrix by -1 because of dye swap? design[5:8,] <- design[5:8,] * -1 colnames(design) <- levels(TS) # so design is now: # h1 h16 h4 h8 # 1 1 0 0 0 # 2 0 0 1 0 # 3 0 0 0 1 # 4 0 1 0 0 # 5 -1 0 0 0 # 6 0 0 -1 0 # 7 0 0 0 -1 # 8 0 -1 0 0 cont.matrix <- makeContrasts( h1vsh4=h4-h1, h4vsh8=h8-h4, h8vsh16=h16-h8, levels=TS) fit2 <- contrasts.fit(fit, cont.matrix) fit2 <- eBayes(fit2) tt <- topTable(fit2, adjust.method="BH") Many thanks, Matt -- Matthew Huska Bioinformatics Core Facility Computational Biology and Data Mining Group Max-Delbrueck-Centrum fuer Molekulare Medizin Robert-Roessle-Str. 10 D-13125 Berlin tel: +49 30 9406 4221 fax: +49 30 9406 4240 email: matthew.huska at mdc-berlin.de web: http://cbdm.mdc-berlin.de
Microarray Normalization limma Microarray Normalization limma • 1.3k views
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