Entering edit mode
@danielamarconiliberoit-857
Last seen 9.6 years ago
Hi limma users!!!
Does anyone know why and how a different weight value for flagged spot
vastly influence significance levels(for both p-value and log odds)
and order in topTable() list?
I flagged spot with a self-made script for genePix(taking into
acconunt some quality parameter like Signal to noise ratio and so
on...)
After I've used different values as weight (in wtflags(): 1, 0, 0.1,
0.5, 0.01) obtaining really different results.
For example with 0 and 1 ...I obtained no significant adjusted
p-values (P>=0.05)
For Weight=0.01 I obtained very signhificant P-value: for more or less
300 genes in the topTable list I have P-value in the range of E-20 to
E-10
I'm wondering what is the correct choice for weights?
Thanks for any help and suggestion
Daniela
PS: (Below I attached my R script in wich the only change is in
read.maimages(...,wt.fun=wtflag(???))
Marconi Daniela
Phd Student
Bologna University
Physics Department
Viale Berti P. 6/2
40137 Bologna
tel: +39 0512095136
e-mail: daniela.marconi at bo.infn.it
########Reading data
>library(limma)
>memory.limit(4000)
>Targets<-readTargets()
>RG<-read.maimages(Targets$file.name,source="genepix",wt.fun=wtflags(0
.01))
>RG$printer<-getLayout(RG$genes)
>MA<-normalizeWithinArrays(RG,bc.method="minimum")
>MAlast<-normalizeBetweenArrays(MA,method="quantile")
>MAlast$targets<-Targets
>Targets
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 UM 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
14 098.gpr sM linf B
15 107.gpr sM linf B
16 119.gpr UM linf B
17 127.gpr sM linf B
18 128.gpr UM linf B
19 129.gpr nsM linf B
20 149.gpr UM linf B
21 164.gpr nsM linf B
22 181.gpr sM linf B
23 185.gpr sM linf B
24 186.gpr sM linf B
25 188.gpr nsM linf B
26 191.gpr UM linf B
27 195.gpr UM linf B
28 245.gpr UM linf B
29 257.gpr sM linf B
30 258.gpr nsM linf B
31 286.gpr nsM linf B
32 287.gpr sM linf B
33 288.gpr nsM linf B
34 304.gpr nsM linf B
35 305.gpr sM linf B
36 313.gpr nsM linf B
37 316.gpr sM linf B
38 318.gpr sM linf B
39 320.gpr sM linf B
40 323.gpr nsM linf B
41 325.gpr sM linf B
42 326.gpr nsM linf B
43 328.gpr UM linf B
44 329.gpr sM linf B
45 331.gpr UM linf B
46 332.gpr sM linf B
47 334.gpr sM linf B
48 337.gpr sM linf B
49 338.gpr sM linf B
50 340.gpr sM linf B
51 344.gpr UM linf B
52 345.gpr UM linf B
53 346.gpr nsM linf B
54 354.gpr UM linf B
55 369.gpr nsM linf B
56 378.gpr nsM linf B
57 382.gpr nsM linf B
#######################
####################### LIMMA
#######################
>group<-factor(c("M",rep("UM",7),"M","M","M","M","M",rep("M",2),"UM","
M","UM","M","UM","M",
rep("M",3),"M",rep("UM",3),"M",rep("M",2),"M",rep("M",2),"M","M",rep("
M",3),"M","M","M",
"UM","M","UM",rep("M",5),rep("UM",2),"M","UM",rep("M",3)),levels=c("UM
","M"))
>design<-model.matrix(~0+group)
>colnames(design)<-c("M","UM")
>dupcor <- duplicateCorrelation(MAlast,design=design)
>fitCOR <- lmFit(MAlast,ndup=2,correlation=dupcor$consensus.correlatio
n,design=design,weights=RG$weights)
>cont.matrix<-makeContrasts(UM.M=UM-M,levels=design)
>fit2COR<-contrasts.fit(fitCOR,cont.matrix)
>fit2COR<-eBayes(fit2COR)
>result<-topTable(fit2COR,n=300,sort.by="P",adjust.method="fdr")
>write.table(result,file="limmaUMvsMflag0.txt",quote = FALSE,
row.names = FALSE,sep="\t")
