Hi,
I'm using limma to analyze an Operon Oligo data set.
I would try to use in down stream part of the analysis a nestedF approach.
The problem is that using
> results<-decideTest(fit2,method="nestedF",adjust.method="fdr")
I had this error message
Errore: nestedF method can't handle NA p-values

Can I use instead classifyTestsF? What is the difference?
I will have with this function a way to obtain adjusted p.values
Or otherwise what is the best solution to avoid NA P.values?


Below, the R code of my analysis:what is wrong???

>Targets<-readTargets()
>Targets
file.name Cy5 Cy3
1 013.gpr nsM linf B
2 015.gpr UM linf B
3 018.gpr UM linf B
4 021.gpr UM linf B
5 022.gpr UM linf B
6 032.gpr nsM linf B
7 039.gpr UM linf B
8 047.gpr UM linf B
9 049.gpr nsM linf B
10 067.gpr sM linf B
11 068.gpr nsM linf B
12 079.gpr sM linf B
13 080.gpr nsM linf B
14 089.gpr linf B
15 098.gpr sM linf B
16 107.gpr sM linf B
17 119.gpr UM linf B
18 127.gpr sM linf B
19 128.gpr UM linf B
20 129.gpr nsM linf B
21 149.gpr UM linf B
22 164.gpr nsM linf B
23 181.gpr sM linf B
24 185.gpr sM linf B
25 186.gpr UM linf B
26 188.gpr nsM linf B
27 191.gpr UM linf B
28 195.gpr UM linf B
29 245.gpr UM linf B
30 257.gpr sM linf B
31 258.gpr nsM linf B
32 286.gpr nsM linf B
33 287.gpr sM linf B
34 288.gpr nsM linf B
35 304.gpr nsM linf B
36 305.gpr sM linf B
37 313.gpr nsM linf B
38 316.gpr sM linf B
39 318.gpr sM linf B
40 320.gpr sM linf B
41 323.gpr nsM linf B
42 325.gpr sM linf B
43 326.gpr nsM linf B
44 328.gpr UM linf B
45 329.gpr sM linf B
46 331.gpr UM linf B
47 332.gpr sM linf B
48 334.gpr sM linf B
49 337.gpr sM linf B
50 338.gpr sM linf B
51 340.gpr sM linf B
52 344.gpr UM linf B
53 345.gpr UM linf B
54 346.gpr nsM linf B
55 354.gpr UM linf B
56 369.gpr nsM linf B
57 378.gpr nsM linf B
58 382.gpr nsM linf B

>RG<-read.maimages(Targets$file.name,source="genepix",wt.fun=wtflags(0.1))
>MAmov<-normalizeWithinArrays(RG,bc.method="movingmin")

>MAmovQ<-normalizeBetweenArrays(MA,method="quantile")

>group<-factor(c("nsM",rep("UM",7),"nsM","sM","nsM","sM","nsM","NC",rep("sM",2),"UM","sM","UM","nsM","UM","nsM",
rep("sM",2),"UM","nsM",rep("UM",3),"sM",rep("nsM",2),"sM",rep("nsM",2),"sM","nsM",rep("sM",3),"nsM","sM","nsM",
"UM","sM","UM",rep("sM",5),rep("UM",2),"nsM","UM",rep("nsM",3)),levels=c("nsM","UM","sM","NC"))

>design<-model.matrix(~0+group)
>colnames(design)<-c("nsM","UM","sM","NC")
>fit<-lmFit(MAmovQ,design,weights=MAmovQ$weights,ndups=1)

>cont.matrix<-makeContrasts(UM-(nsM+sM),UM-nsM,UM-sM,nsM-sM,levels=design)

> cont.matrix
UM - (nsM + sM) UM - nsM UM - sM nsM - sM
nsM -1 -1 0 1
UM 1 1 1 0
sM -1 0 -1 -1
NC 0 0 0 0

>fit2<-contrasts.fit(fit,cont.matrix)

>results<-decideTests(fit2,method="nestedF",adjust.method="fdr",p.value=0.05)

Any idea??
Thanks
Daniela

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