Dear list members,
I am analysing my microarray data using limma package. Now I encounter
several problems. Looking forward to your suggestions!
Question 1:
During the process of background correction using method="normexp",
four
warning messages appeared as "NaNs produced in: log(x)" (as you can
see in
the program posted below). What does that mean? How will it effect the
final result? How could it be settled?
Question 2:
On my microarray, every probe has two replicates.During the process of
duplicateCorrelation, two warnings appear as "Too much damping -
convergence tolerance not achievable" (as you also can see in the program
posted below). What does it mean? Is there anything wrong with my data?
Question 3:
How to construct the design matrix is a puzzle to me. Here I constructed
the design matrix using the function modelMatrix and the object targets.
However, I am not sure whether it is constructed appropriately. Looking
forward to your suggestions.
(Additional info about my experimental design. Uppercase and lowercase
words in the R object targets (see below in the posted program) have
different meanings. The locusts on the plain [PLAIN] was treated [plain]
in a simulated plateau environment while the locusts on the plateau
[PLATEAU] was treated [plateau] in a simulated plain environment. They
experienced different treatments. I think it is not a complete factorial
design. Therefore I did not choose the design matrix for factorial
designs. However, I do not know whether what I chose is appropriate.)
Question 4:
All in all, I wonder whether the differentially expressed genes produced
via the posted program are convincing. Will the above-mentioned warnings
affect the reliability of the final result? Can I continue to the next
step?
Thanks!
Dejian Zhao
++++++++++++++++++ Program Starts +++++++++++++++++++++
> library(limma)
> library(statmod) #duplicateCorrelation requires this package.
> targets<-readTargets()
> targets
Cy3 Cy5 FileName Date
1 PLAIN PLATEAU Locust 186.gpr 2006-5-31
2 PLAIN PLATEAU Locust 187.gpr 2006-5-31
3 PLAIN PLATEAU Locust 188.gpr 2006-5-31
4 PLAIN PLATEAU Locust 189.gpr 2006-5-31
5 PLAIN PLATEAU Locust 190.gpr 2006-5-31
6 PLAIN PLATEAU Locust 191.gpr 2006-5-31
7 plain PLAIN Locust 192.gpr 2006-6-6
8 plain PLAIN Locust 193.gpr 2006-6-6
9 plain PLAIN Locust 194.gpr 2006-6-6
10 plain PLAIN Locust 195.gpr 2006-6-6
11 plain PLAIN Locust 196.gpr 2006-6-6
12 plain PLAIN Locust 197.gpr 2006-6-6
13 plateau PLATEAU Locust 198.gpr 2006-6-8
14 plateau PLATEAU Locust 199.gpr 2006-6-8
15 plateau PLATEAU Locust 200.gpr 2006-6-8
16 plateau PLATEAU Locust 201.gpr 2006-6-8
17 plateau PLATEAU Locust 202.gpr 2006-6-8
18 plateau PLATEAU Locust 203.gpr 2006-6-8>
RG<-read.maimages(targets,source="genepix",wt.fun=wtflags(0.1))
Read Locust 186.gpr
Read Locust 187.gpr
Read Locust 188.gpr
Read Locust 189.gpr
Read Locust 190.gpr
Read Locust 191.gpr
Read Locust 192.gpr
Read Locust 193.gpr
Read Locust 194.gpr
Read Locust 195.gpr
Read Locust 196.gpr
Read Locust 197.gpr
Read Locust 198.gpr
Read Locust 199.gpr
Read Locust 200.gpr
Read Locust 201.gpr
Read Locust 202.gpr
Read Locust 203.gpr> RG$genes<-readGAL()
> spottypes<-readSpotTypes()
> spottypes
SpotType ID Name Color
1 gene * * black
2 blank Blank * brown
3 buffer *sc * blue
4 rice Os026* * green
5 beta-actin Beta* * red
6 18S 18S* * yellow
7 GAPDH GAPDH* * purple> RG$genes$Status<-controlStatus(spottypes,RG)
Matching patterns for: ID Name
Found 19200 gene
Found 96 blank
Found 220 buffer
Found 192 rice
Found 192 beta-actin
Found 96 18S
Found 96 GAPDH
Setting attributes: values Color> RG.b<-backgroundCorrect(RG,method="normexp",offset=0)
Corrected array 1
Corrected array 2
Corrected array 3
Corrected array 4
Corrected array 5
Corrected array 6
Corrected array 7
Corrected array 8
Corrected array 9
Corrected array 10
Corrected array 11
Corrected array 12
Corrected array 13
Corrected array 14
Corrected array 15
Corrected array 16
Corrected array 17
Corrected array 18
Warning messages:
1: NaNs produced in: log(x)
2: NaNs produced in: log(x)
3: NaNs produced in: log(x)
4: NaNs produced in: log(x)>
w<-modifyWeights(RG$weights,RG$genes$Status,c("rice","beta-actin","18S","GAPDH"),c(0.1,2,2,2))
MA.p<-normalizeWithinArrays(RG.b,weights=w,iterations=6)> design<-modelMatrix(targets,ref="PLAIN")
Found unique target names:
plain PLAIN plateau PLATEAU> design
plain plateau PLATEAU
[1,] 0 0 1
[2,] 0 0 1
[3,] 0 0 1
[4,] 0 0 1
[5,] 0 0 1
[6,] 0 0 1
[7,] -1 0 0
[8,] -1 0 0
[9,] -1 0 0
[10,] -1 0 0
[11,] -1 0 0
[12,] -1 0 0
[13,] 0 -1 1
[14,] 0 -1 1
[15,] 0 -1 1
[16,] 0 -1 1
[17,] 0 -1 1
[18,] 0 -1 1> i<-MA.p$genes$Status=="gene"
> corMA.pi<-duplicateCorrelation(MA.p[i,],design,ndups=2)
Warning messages:
1: Too much damping - convergence tolerance not achievable in:
glmgam.fit(dx, dy, start = start, tol = tol, maxit = maxit, trace = trace)
2: Too much damping - convergence tolerance not achievable in:
glmgam.fit(dx, dy, start = start, tol = tol, maxit = maxit, trace = trace)
>
fitMA.pi<-lmFit(MA.p[i,],design,ndups=2,correlation=corMA.pi$consensus.correlation)
contrast.matrix<-makeContrasts(plain,plateau-PLATEAU,PLATEAU,levels=design)
contrast.matrix
Contrasts
Levels plain plateau - PLATEAU PLATEAU
plain 1 0 0
plateau 0 1 0
PLATEAU 0 -1 1>
colnames(contrast.matrix)<-cbind("plain-PLAIN","plateau-PLATEAU","PLATEAU-PLAIN")
contrast.matrix
Contrasts
Levels plain-PLAIN plateau-PLATEAU PLATEAU-PLAIN
plain 1 0 0
plateau 0 1 0
PLATEAU 0 -1 1> fit2MA.pi<-contrasts.fit(fitMA.pi,contrast.matrix)
> fit2MA.pi<-eBayes(fit2MA.pi)
>
resultsi.001.fc2=decideTests(fit2MA.pi,method="nestedF",adjust.method="BH",p.value=0.001,lfc=log2(2))
write.fit(fit2MA.pi,
results=decideTests(fit2MA.pi,method="nestedF",adjust.method="BH",p.value=0.001,lfc=1),
"fit2MA.pi.nestedF.adj_BH.P_001.FC_2.csv", digits=4,
adjust="BH",
sep=",")> save.image("result.RData")
++++++++++++++++++ Program Ends +++++++++++++++++++++
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