Microarray

Time Course

• http://www.biostat.jhsph.edu/~ririzarr/688/

Limma package

• http://master.bioconductor.org/help/course-materials/2005/BioC2005/labs/lab01/drosEmbryo/ or http://bioinf.wehi.edu.au/marray/jsm2005/lab5/lab5.html. Note the package drosEmbryo is not available on Bioc although it can be downloaded from bioinf.wehi.edu.au. It still cannot be used.

> data(drosEmbryoRMA)
Warning message:
'drosEmbryoRMA' looks like a pre-2.4.0 S4 object: please recreate it 

• Limma Guide Section 9.6 (no .Rd file nor R code!).

# Step 1 - Read the design and create eset.
targets <- read.table(file='stdin', header=T)
FileName	Target
File1	wt.0hr
File2	wt.0hr
File3	wt.6hr
File4	wt.24hr
File5	mu.0hr
File6	mu.0hr
File7	mu.6hr
File8	mu.24hr
# Hit Ctrl+D twice.
# eset = rma(ReadAffy())
exprs <- matrix(rnorm(1000*8), nr=1000)
eset <- ExpressionSet(assayData = exprs)
colnames(exprs) <- targets[,1]

lev <- c("wt.0hr","wt.6hr","wt.24hr","mu.0hr","mu.6hr","mu.24hr")
f <- factor(targets$Target, levels=lev)
design <- model.matrix(~0+f)
colnames(design) <- lev
fit <- lmFit(eset, design)

# Step 2 - Which genes respond at either the 6 hour or 24 hour times in the wild-type?
cont.wt <- makeContrasts(
"wt.6hr-wt.0hr",
"wt.24hr-wt.6hr",
levels=design)
fit2 <- contrasts.fit(fit, cont.wt)
fit2 <- eBayes(fit2)
topTableF(fit2, adjust="BH")
# Output:
    wt.6hr.wt.0hr wt.24hr.wt.6hr     AveExpr        F     P.Value adj.P.Val
112     3.2749023     -4.5401508  0.23644620 7.192590 0.006274150 0.9079656
142     0.2028617      3.5727319  0.31292924 6.425034 0.009419387 0.9079656
151    -2.4795481      4.5473025  0.28953869 6.138177 0.011026934 0.9079656
830     0.9768946      2.7571045  0.55529723 5.982599 0.012027262 0.9079656
371    -2.5848778      4.4752189 -0.68087827 5.813166 0.013235184 0.9079656
861    -3.6229467      1.2348225 -0.19658086 5.810336 0.013256488 0.9079656
645     2.6729536     -4.3124373  0.09475226 5.789848 0.013411914 0.9079656
926     3.5387477     -0.9448989  0.15060787 5.484057 0.015994787 0.9079656
113     2.5057968     -4.0472781  0.12254199 5.372589 0.017072749 0.9079656
802     0.5210958      2.9178136 -0.54632441 5.140624 0.019589928 0.9079656
> cont.wt
         Contrasts
Levels    wt.6hr-wt.0hr wt.24hr-wt.6hr
  wt.0hr             -1              0
  wt.6hr              1             -1
  wt.24hr             0              1
  mu.0hr              0              0
  mu.6hr              0              0
  mu.24hr             0              0

# Step 3- Which genes respond (i.e., change over time) in the mutant?
cont.mu <- makeContrasts(
"mu.6hr-mu.0hr",
"mu.24hr-mu.6hr",
levels=design)
fit2 <- contrasts.fit(fit, cont.mu)
fit2 <- eBayes(fit2)
topTableF(fit2, adjust="BH")
# Output:
    mu.6hr.mu.0hr mu.24hr.mu.6hr     AveExpr        F     P.Value adj.P.Val
203   -4.70500261      4.3795233 -0.52285214 8.753341 0.002919147 0.8967851
797   -3.39270257     -0.5862948 -0.02746123 8.328550 0.003568277 0.8967851
828   -4.25071499      1.8429580  0.34257837 7.590078 0.005125338 0.8967851
764    0.02937019      3.7623514  0.32860517 6.991332 0.006965102 0.8967851
570   -2.14961101     -2.1415450  0.52207117 6.558165 0.008764670 0.8967851
457    2.28175860     -4.5011533 -0.25060779 6.522692 0.008933944 0.8967851
593    2.57911585      1.3339224  0.18262853 6.460369 0.009240381 0.8967851
693   -0.43909594      3.8805006 -0.30368945 6.117644 0.011153404 0.8967851
414    1.76240994     -4.3652464  0.12864332 5.887795 0.012686997 0.8967851
309   -3.57816645      3.4458003 -0.09221823 5.847096 0.012982697 0.8967851

# Step 4- Which genes respond differently over time in the mutant relative to the wild-type?
cont.dif <- makeContrasts(
Dif6hr =(mu.6hr-mu.0hr)-(wt.6hr-wt.0hr),
Dif24hr=(mu.24hr-mu.6hr)-(wt.24hr-wt.6hr),
levels=design)
fit2 <- contrasts.fit(fit, cont.dif)
fit2 <- eBayes(fit2)
topTableF(fit2, adjust="BH")
# Output:
        Dif6hr   Dif24hr     AveExpr        F     P.Value adj.P.Val
797 -6.0630607  1.468086 -0.02746123 8.922239 0.002699040 0.8685775
18  -1.7460726  7.130525  0.12631684 8.707761 0.002981974 0.8685775
764 -0.3370846  5.759971  0.32860517 7.512472 0.005329540 0.8685775
564  5.9827916 -4.071109  0.11052980 7.237032 0.006132233 0.8685775
24  -5.3124510  2.462141 -0.07153017 6.291352 0.010133010 0.8685775
113 -4.1366307  5.990927  0.12254199 6.138815 0.011023028 0.8685775
313 -2.0419712  6.157636 -0.12618538 5.979547 0.012047886 0.8685775
913 -4.4262959  6.195062  0.46998972 5.562351 0.015283627 0.8685775
926 -4.9695447  1.222076  0.15060787 5.496894 0.015875656 0.8685775
674 -0.9636603 -3.808384  0.13392844 5.020330 0.021058895 0.8685775

A case study using Limma package

http://www.biomedcentral.com/1756-0500/3/81