Hello everybody,

I'm have a set of CEL files of six time points and I would like to use LIMMA
to analyse to differences in time between a treatment and the control
arrays.

I'm reading the data from an excel sheet and create an object for each time
point and treatment level.
> data_1.0    <- read.delim(file="stress_1.0.txt", row.names=1)
> dim(data_1.0)
22810   51

I than combined the various time points and treatments into one data frame
with
> xxx <- cbind(log_control_1.0_1h, log_control_1.0_3h, log_control_1.0_6h,
log_control_1.0_9h,
log_control_1.0_24h, log_control_1.0_48h, log_xxx_1.0_1h, log_xxx_1.0_3h,
log_xxx_1.0_6h,
log_xxx_1.0_9h, log_xxx_1.0_24h, log_xxx_1.0_48h)
> row.names(xxx) <- row.names(data_1.0)

I have a different number of array for the various time points.
1. Can that cause a problem in the analysis?

I'm having difficulties in creating the correct design and contrast
matrices.
I tried :
>d <-
factor(c("c1","c1","c1","c1","c3","c3","c3","c3","c3","c3","c6","c6","c6","c6","c6","c6",
"c9","c9","c9","c24","c24","c24","c24","c48","c48","t1","t1","t1","t1",
"t3","t3","t3","t3","t3","t3","t3","t6","t6","t6","t6","t6","t9","t9","t9","t9",
"t24","t24","t24","t24","t48","t48"),
levels=c("c1","c3","c6","c9","c24","c48","t1","t3","t6","t9","t24","t48"))
#Design matrix for stress
> design <- model.matrix(~0+d)
# line in the table with NaN values (NA) were taken out - 4 lines of house
keeoping genes
> fit_limma<- lmFit(xxx, design)
#which genes responds either at 1h, 3h,6h,9h,24h or 48h to treatment?
> contrastoftreatment <-
makeContrasts("dt1-dc1","dt3-dc3","dt6-dc6","dt9-dc9","dt24-dc24","dt48-dc48"
,levels=design)

But after a lot of thoughts I found out that I'm basically only searching
for here for differentially regulated genes between treated and control
experiments in a specific time point.

so I created a terget files:
Name    fileName    Target    Time
xxx_0_1h_1    xxx_0_1h_1.CEL    wt.1h1    1
xxx_0_1h_2    xxx_0_1h_2.CEL    wt.1h2    1
xxx_0_1h_3    xxx_0_1h_3.CEL    wt.1h3    1
xxx_0_1h_4    xxx_0_1h_4.CEL    wt.1h4    1
xxx_0_24h_1    xxx_0_24h_1.CEL    wt.24h1    24
xxx_0_24h_2    xxx_0_24h_2.CEL    wt.24h2    24
xxx_0_24h_3    xxx_0_24h_3.CEL    wt.24h3    24
xxx_0_24h_4    xxx_0_24h_4.CEL    wt.24h4    24
xxx_0_3h_1    xxx_0_3h_1.CEL    wt.3h1    3
xxx_0_3h_2    xxx_0_3h_2.CEL    wt.3h2    3
xxx_0_3h_3    xxx_0_3h_3.CEL    wt.3h3    3
xxx_0_3h_5    xxx_0_3h_5.CEL    wt.3h4    3
xxx_0_3h_6    xxx_0_3h_6.CEL    wt.3h5    3
xxx_0_3h_7    xxx_0_3h_7.CEL    wt.3h6    3
xxx_0_48h_1    xxx_0_48h_1.CEL    wt.48h1    48
xxx_0_48h_2    xxx_0_48h_2.CEL    wt.48h2    48
xxx_0_6h_1    xxx_0_6h_1.CEL    wt.6h1    6
xxx_0_6h_2    xxx_0_6h_2.CEL    wt.6h2    6
xxx_0_6h_3    xxx_0_6h_3.CEL    wt.6h3    6
xxx_0_6h_4    xxx_0_6h_4.CEL    wt.6h4    6
xxx ... (you get the idea)

and used it to analyse the data according to the differences between the
treated and the control experiments.
> targets <-readTargets("targets.txt",row.names="Name",quote="")
> lev <- c("wt.1h", "wt.24h", "wt.3h", "wt.48h", "wt.6h", "wt.9h",
"mu.1h", "mu.24h", "mu.3h", "mu.48h", "mu.6h", "mu.9h")
> f<-factor(targets$Target, levels=lev)
> design_1 <- model.matrix(~0+f)
> colnames(design_1) <- lev
> fit_1 <- lmFit(xxx, design_1)
# searching for the genes with time effects.
> cont.wt <- makeContrasts("wt.3h-wt.1h","wt.6h-wt.3h","wt.9h-wt.6h",
"wt.24h-wt.9h","wt.48h-wt.24h", "mu.3h-mu.1h", "mu.6h-mu.3h", "mu.9h-mu.6h",
"mu.24h-mu.9h","mu.48h-mu.24h", levels=design_1)

> fit_2 <-contrasts.fit(fit_1, cont.wt)
> fit_3 <- eBayes(fit_2)
> topTable(fit_3, adjust="BH")
> sel.wt <- p.adjust(fit_3$F.p.value, method="fdr")<0.01

> cont.diff <- makeContrasts(diff3h=(mu.3h-mu.1h) - (wt.3h-wt.1h),
diff6h=(mu.6h-mu.3h) - (wt.6h-wt.3h),
diff9h=(mu.9h-mu.6h) - (wt.9h-wt.6h),
diff24h=(mu.24h-mu.9h) - (wt.24h-wt.9h),
diff48h=(mu.48h-mu.24h) - (wt.48h-wt.24h),
levels=design_1)
> fit_diff <-contrasts.fit(fit_1, cont.diff)
> fit_diff1 <- eBayes(fit_diff)

with this command I'm extracting the genes which are differentially
regulated between the 3h and the 1h time points with thresholds p<0.01 and a
2 fold change.
> topGenes <- topTable(fit_diff1, coef=1, number=3000, adjust="BH",
p.value=0.01, lfc=2, sort.by="logFC")

I'd have a look at the resulting genes, but somehow I didn't get the feeling
that it now does what I wanted it to do, so my main question is -  Is the
experimental design I made here on the second part correct for finding
significantly differentially regulated genes in a time course analysis
between different time points?

Is there a way of extrapolating this kind of contrast design so that I'm
able to find only the genes which are differentially regulated in all time
points with this experimetal design?

Does such an analysis make any sense?

THX for any kind of help.


Assa Yeroslaviz

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