[BioC] Simple affymetrix question (treated vs non-treated)

[Wonjong Moon]

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? 
2. Positive M values in design2 mean up-regulated in CSA?

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"