Update:
1. Here is a better illustration. I assumed that the gene expression was
the same for the three replicates for each gene, in the respective Time and
Treatment groups, so that I could follow (verify) what was going on.
2. To better-view the following, copy and paste into a plain-text editor
with a monospace font (e.g. gedit on Ubuntu with the Monospace font).
Sincerely, Victoria :-)
------------------------------------
5 Genes x 3 Replicates x 2 Times x 2 Treatments
> x<-matrix(scan("/home/victoria/R/5.3.2.2.csv",n=5*12),5,12,byrow=TRUE)
Read 60 items
> x
[,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10] [,11] [,12]
[1,] 1 1 1 2 2 2 3 3 3 4 4 4 #
Gene1 avg. = row avg. = 2.5
[2,] 1 1 1 2 2 2 3 3 3 4 4 4 #
Gene2 avg. = row avg. = 2.5
[3,] 1 1 1 2 2 2 3 3 3 4 4 4 #
Gene3 avg. = row avg. = 2.5
[4,] 1 1 1 2 2 2 3 3 3 4 4 4 #
Gene4 avg. = row avg. = 2.5
[5,] 1 1 1 2 2 2 3 3 3 4 4 4 #
Gene5 avg. = row avg. = 2.5
# |------- Treatment1 -------| |-------- Treatment2 ---------| Tr1
avg. = (1+2)/2; Tr2 avg. = (3+4)/2
# |-- Time1 --| |-- Time2 --| |-- Time1 --| |-- Time2 --| Ti1
avg. = (1+3)/2; Ti2 avg. = (2+4)/2
# (x3) (x3) (x3) (x3)
# This, above,is for illustration only; the actual data will have different
values for eah of the above
# [ 5x3x2x2 = 60 data individual, independent data points ]
> dim(x)
[1] 5 12
> n<-dim(x)[1]
> n
[1] 5
> dim(x)<-c(5,3,2,2)
> dim(x)
[1] 5 3 2 2
> genemeans <- apply(x, 1, mean, na.rm=TRUE)
> genemeans
[1] 2.5 2.5 2.5 2.5 2.5 # = row averages (5 Genes)
> timemeans <- apply(x, 3, mean, na.rm=TRUE)
> timemeans
[1] 2 3 # = Time1 avg Time2 avg (1+1+1+3+3+3)/6; (2+2+2+4+4+4)/6
> treatmeans <- apply(x, 4, mean, na.rm=TRUE)
> treatmeans
[1] 1.5 3.5 # Treatment1 avg Treatment2 avg (1+1+1+2+2+2)/6;
(3+3+3+4+4+4)/6
------------------------------------
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