[BioC] 4 replicates, of 3 tissues- one good hyb (2xreps), one bad hyb (2xreps). Best way forward?

[k. brand]

Hi James, BioCers,

I wanted to follow up your suggestions for dealing with technical 
variation across 2 hybs by giving you all available information for your 
consideration.

Aim:

To characterise transcriptomes of three different, neighbouring neuron 
populations within mouse brain, relative to each other.

Methods:
To do this, the 3 populations were captured from four biological 
replicates. Thus 12 arrays. Two complete replicates were hybed on day 1- 
thus Hyb A, and the second two rep.s a month later- Hyb B.

*Hyb A used old microarrays ~18 months past expiration. Hyb B used 
unexpired arrays. All other (known) factors are equal.

Results:
Hyb A shows significantly lower intensities than Hyb B. See summary of 
"#Unnormalised data:" below. QPCR shows good concordance between Hyb A 
and B  ~8/10 genes analsyed show the same relative changes.

Discussion:

I was thinking (with Douglas Altman in hand, but no statistician to 
interrogate!):

An ANOVA on the paired data of the 3 tissues per animal. Because my 3 
tissues come from the same animal, can i increase statistical power by 
employing a paired test per animal per hyb. I could normalise all 
samples from Hyb A. and analyse them with a duplicate, paired sample 
ANOVA approach to identify differential expression bwtn the 3 tissues. 
This could be repeated for Hyb B.

My Qs:

-a valid approach?
-how to best combine Hyb A and B. to gain a greater statistical power?
-exactly what test , ANOVA variation is employed?
-R implementation (or any other?)?
-controlling for false discovery?

2. Employ a non-parametric version of the above?

3. RMA all of it, hope for the best, and then employ ANOVA or some other 
test? RMA normalized data is not so bad (see summary "#RMA normalized 
data:" below), and maybe not so good...

Any and all guidance very much appreciated.

thanks in advance,

Karl



#Unnormalized data:
 > dat <- ReadAffy()
 > dat.rma <- rma(dat, normalize=FALSE)
Background correcting
Calculating Expression
 > apply(exprs(dat.rma),2,summary)
         Tco1A.CEL Tco2A.CEL Tco3B.CEL Tco4B.CEL Tmi1A.CEL Tmi2A.CEL
Min.        1.633     1.713     1.869     2.431     2.027     1.736
1st Qu.     2.627     2.751     3.234     3.554     2.831     2.882
Median      3.329     3.320     4.933     4.952     3.433     3.588
Mean        4.153     3.902     5.572     5.741     4.330     4.261
3rd Qu.     5.147     4.541     7.534     7.556     5.254     5.151
Max.       14.180    13.640    14.370    14.280    14.240    13.880

         Tmi3B.CEL Tmi4B.CEL Tsh1A.CEL Tsh2A.CEL Tsh3B.CEL Tsh4B.CEL
Min.        1.771     2.575     1.607     1.902     1.771     2.360
1st Qu.     3.280     3.716     2.541     2.850     3.197     3.940
Median      4.986     4.978     3.183     3.357     4.833     5.488
Mean        5.629     5.762     3.983     4.032     5.493     6.092
3rd Qu.     7.606     7.449     4.882     4.629     7.426     7.892
Max.       14.300    14.180    14.070    13.990    14.230    14.340

#RMA normalized data:
 > eset <- justRMA(filenames=list.celfiles())
Background correcting
Normalizing
Calculating Expression
 > apply(exprs(eset),2,summary)
         Tco1A.CEL Tco2A.CEL Tco3B.CEL Tco4B.CEL Tmi1A.CEL Tmi2A.CEL
Min.        1.945     2.077     2.014     2.070     1.970     2.050
1st Qu.     3.394     3.574     3.114     3.135     3.381     3.483
Median      4.469     4.533     4.552     4.435     4.474     4.472
Mean        5.191     5.110     5.221     5.192     5.199     5.133
3rd Qu.     6.539     6.114     6.947     6.866     6.589     6.260
Max.       14.180    14.170    14.120    14.170    14.170    14.180

         Tmi3B.CEL Tmi4B.CEL Tsh1A.CEL Tsh2A.CEL Tsh3B.CEL Tsh4B.CEL
Min.        1.930     2.056     2.015     2.028     1.815     1.919
1st Qu.     3.106     3.190     3.436     3.524     3.132     3.134
Median      4.520     4.426     4.497     4.484     4.548     4.450
Mean        5.219     5.194     5.203     5.140     5.224     5.198
3rd Qu.     6.949     6.824     6.537     6.241     6.938     6.887
Max.       14.130    14.070    14.180    14.130    14.110    14.150

 > sessionInfo()
Version 2.3.0 (2006-04-24)
i386-pc-mingw32

attached base packages:
[1] "tools"     "methods"   "stats"     "graphics"  "grDevices" "utils" 
     "datasets"  "base"

other attached packages:
      affyPLM        gcrma  matchprobes     affydata mouse4302cdf 
    vsn        limma         affy       affyio      Biobase
      "1.8.0"      "2.4.1"      "1.4.0"      "1.8.0"     "1.12.0" 
"1.10.0"      "2.7.3"     "1.10.0"      "1.0.0"     "1.10.0"

-- 
Karl Brand <k.brand at erasmusmc.nl>
Department of Cell Biology and Genetics
Erasmus MC
Dr Molewaterplein 50
3015 GE Rotterdam
lab +31 (0)10 408 7409 fax +31 (0)10 408 9468