[BioC] Simple affymetrix question (treated vs non-treated)
James W. MacDonald
jmacdon at med.umich.edu
Fri Oct 13 20:10:08 CEST 2006
Wonjong Moon wrote:
> Thank you for your reply.
> Here, I have two different BioC codes A and B. I am comparaing
> Affymetrix data for 'CSA' and 'Non'. I used two different matrix design.
>
> Target file: 141PD.txt
>
> Name FileName Target
> CSA1 1A-1_SA1_141PD.CEL CSA
> CSA2 2A-1_SA2_141PD.CEL CSA
> CSA3 3A-1_SA4_141PD.CEL CSA
> CSA4 4A-1_SA5_141PD.CEL CSA
> Non5 5A-1_Non1_141PD.CEL Non
> Non6 6Ar-1_Non2_141PD.CEL Non
> Non7 7A-1_Non4_141PD.CEL Non
> Non8 8A-1_Non5_141PD.CEL Non
>
>
> Matrix design1 and design2 gave me the opposite sign with same B value
> (absolute value of M is exactly same), which means up-regulated genes in
> design1 became down-regulated in design2.
> I would like to know which one is correct for my purpose. My purpose is
> to know which matrix design gives me the up-regulated genes in 'CSA'
> with reasonable B or p values.
> Questions.
> 1. Positive M values in design1 mean up-regulated in CSA?
You set up the design matrix specifically using CSA - Non, so that is
the comparison you are making. Therefore, a positive value means up in
CSA, and a negative means the opposite.
> 2. Positive M values in design2 mean up-regulated in CSA?
No. Coefficient 2 in that design is Non - CSA.
HTH,
Jim
>
> A. matrix design1
> library(affy)
>
> library(limma) # Loads limma library.
>
> targets <- readTargets("141PD.txt") # Reads targets information from
> file
> data <- ReadAffy(filenames=targets$FileName) # Reads CEL files
> (specified in 'targets') into AffyBatch object
> eset <- rma(data) # Normalizes data with 'rma'
> design <- model.matrix(~ -1+factor(c(1,1,1,1,2,2,2,2)))
>
> design
> colnames(design) <- c("CSA", "Non")
> fit <- lmFit(eset, design)
>
> contrast.matrix <- makeContrasts(CSA-Non,levels=design)
>
> fit2 <- contrasts.fit(fit, contrast.matrix)
>
> fit2 <- eBayes(fit2)
>
> topTable(fit2, coef=1, adjust="fdr", sort.by="B", number=10)
>
>
> ID M A t P.Value
> adj.P.Val B
> Pf.4.224.0_CDS_x_at -4.493635 3.989016 -37.73719 2.591029e-10
> 5.899515e-06 10.266739
> Pf.4.223.0_CDS_x_at -4.321856 4.101203 -30.76139 1.319405e-09
> 1.502077e-05 9.771946
> X03144.1_at -3.570154 4.388052 -23.35914 1.171606e-08
> 5.742442e-05 8.855552
> Pf.7.64.0_CDS_a_at -5.031334 4.643776 -23.24412 1.218247e-08
> 5.742442e-05 8.836283
> AF306408.1_RC_at -3.512501 3.516498 -22.64498 1.497590e-08
> 5.742442e-05 8.732638
> Pf.13_1.84.0_CDS_a_at -5.032685 4.542793 -22.61523 1.513226e-08
> 5.742442e-05 8.727346
> Pf.2.36.0_CDS_at -2.584731 4.651177 -20.17259 3.728248e-08
> 1.212693e-04 8.239339
> Pf.9.267.0_CDS_at -4.158351 4.053352 -18.52982 7.270084e-08
> 2.041490e-04 7.841473
> Pf.5.119.0_CDS_x_at -4.550460 5.321148 -18.28511 8.069483e-08
> 2.041490e-04 7.776541
> Pf.13_1.99.0_CDS_x_at -2.364882 6.501028 -17.90327 9.521651e-08
> 2.167985e-04 7.672015
>
>
>
>
>>design
>
> CSA Non
> 1 1 0
> 2 1 0
> 3 1 0
> 4 1 0
> 5 0 1
> 6 0 1
> 7 0 1
> 8 0 1
> attr(,"assign")
> [1] 1 1
> attr(,"contrasts")
> attr(,"contrasts")$`factor(c(1, 1, 1, 1, 2, 2, 2, 2))`
> [1] "contr.treatment"
>
> B. matrix design2
> library(affy)
>
> library(limma) # Loads limma library.
>
> targets <- readTargets("141PD.txt") # Reads targets information from
> file
> data <- ReadAffy(filenames=targets$FileName) # Reads CEL files
> (specified in 'targets') into AffyBatch object
> eset <- rma(data) # Normalizes data with 'rma'
> pData(eset)
> chips <- c("CSA", "CSA", "CSA", "CSA", "Non", "Non", "Non", "Non")
> design <-model.matrix(~factor(chips))
> colnames(design) <- c("CSA", "CSA vs Non")
> design
> fit <- lmFit(eset, design)
> fit <- eBayes(fit)
> options(digits=2)
> topTable(fit, coef=2, n=10, adjust="BH")
> ID M A t P.Value adj.P.Val B
> 21231 Pf.4.224.0_CDS_x_at 4.5 4.0 38 2.6e-10 5.9e-06 10.3
> 21230 Pf.4.223.0_CDS_x_at 4.3 4.1 31 1.3e-09 1.5e-05 9.8
> 22728 X03144.1_at 3.6 4.4 23 1.2e-08 5.7e-05 8.9
> 22101 Pf.7.64.0_CDS_a_at 5.0 4.6 23 1.2e-08 5.7e-05 8.8
> 612 AF306408.1_RC_at 3.5 3.5 23 1.5e-08 5.7e-05 8.7
> 20063 Pf.13_1.84.0_CDS_a_at 5.0 4.5 23 1.5e-08 5.7e-05 8.7
> 20855 Pf.2.36.0_CDS_at 2.6 4.7 20 3.7e-08 1.2e-04 8.2
> 22524 Pf.9.267.0_CDS_at 4.2 4.1 19 7.3e-08 2.0e-04 7.8
> 21350 Pf.5.119.0_CDS_x_at 4.6 5.3 18 8.1e-08 2.0e-04 7.8
> 20078 Pf.13_1.99.0_CDS_x_at 2.4 6.5 18 9.5e-08 2.2e-04 7.7
>
>
>
>>design
>
> CSA CSA vs Non
> 1 1 0
> 2 1 0
> 3 1 0
> 4 1 0
> 5 1 1
> 6 1 1
> 7 1 1
> 8 1 1
> attr(,"assign")
> [1] 0 1
> attr(,"contrasts")
> attr(,"contrasts")$`factor(chips)`
> [1] "contr.treatment"
>
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--
James W. MacDonald, M.S.
Biostatistician
Affymetrix and cDNA Microarray Core
University of Michigan Cancer Center
1500 E. Medical Center Drive
7410 CCGC
Ann Arbor MI 48109
734-647-5623
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