Question

Single channel analysis of two-color agilent array using limma

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pm2015 • 0

@pm2015-8878

Last seen 8.5 years ago

United States

Hello

I fairly new to limma for analyzing two-color microarray data. I am trying to analyze a somewhat unusually designed experiment. Here's the design:

Filename Cy3 Cy5

File1 M_cont M_cont

File2 M_10hr M_10hr

File1 M_14hr M_14hr

File1 P_cont P_cont

File1 M_10hr M_10hr

File1 M_hr M_14hr

I am following the instructions on limma userguide chapter 12. However, I keep getting the following error after this step:

corfit <- intraspotCorrelation(MA, design)

Warning messages:
1: In statmod::remlscore(y, X, Z) : reml: Max iterations exceeded
2: In statmod::remlscore(y, X, Z) : reml: Max iterations exceeded

Please help!

agilent limma twochannel microarray • 2.3k views

ADD COMMENT • link updated 9.2 years ago by Gordon Smyth 52k • written 9.2 years ago by pm2015 • 0

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That's not an error, it's a warning. It's just telling you that for two genes, the remlscore function in the statmod package (that's called internally by intraspotCorrelation) wasn't able to converge to a solution. You should still be able to proceed with the rest of the analysis, using the corfit object.

ADD REPLY • link 9.2 years ago Aaron Lun ★ 28k

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Hi Aaron

Thanks for your reply. I thought so too but I get the following error in the next step:

>fit <- lmscFit(MA, design, correlation=corfit$consensus)

Error in if (abs(correlation) >= 1) stop("correlation must be strictly between -1 and 1") :
missing value where TRUE/FALSE needed

> corfit$consensus
[1] NaN

Any ideas why this might be?

ADD REPLY • link 9.2 years ago pm2015 • 0

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Well, for future reference, it would better to post code up to the error, otherwise you'll just confuse people.

Now, I don't do a lot of two-color microarray analyses, but it seems to me that your two-color data set doesn't really use the two colors at all. Each file uses the same condition for both dyes, which defeats the purpose of a two-color setup. I can't imagine the design you got out of modelMatrix would look particularly healthy.

ADD REPLY • link 9.2 years ago Aaron Lun ★ 28k

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Thanks for replying. I am just analyzing a dataset from a microarray designed by someone else in lab. I have worked with a lot of affymetrix datasets in the past. I was also very surprised by why this array was done this way. Doesn't make much sense to me either. Anyhow trying to see if I can still get something meaningful out of this.

ADD REPLY • link 9.2 years ago pm2015 • 0

Gordon Smyth · Answer 1 · 2015-09-26

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Gordon Smyth 52k

@gordon-smyth

Last seen 9 minutes ago

WEHI, Melbourne, Australia

First, can you confirm that you really do have RNA from the same treatment condition hybridized to both channels of each array? As Aaron has remarked, that defeats the purpose of two colour microarrays and drastically decreases the precision with which you can detect DE genes. Why would you design the hybridizations like that?

Anway, for intraspotCorrelation() to return an NA consensus correlation, there must be a serious problem with either the data or the design matrix. You haven't given us enough information to diagnose any problem. If you want more help could you please:

1. Show the correct targets frame (the one in your original post looks to have a typo or two).

2. Show the code you used to construct the design matrix.

3. Show the output from summary(corfit$atanh.correlation)

4. Show the output from summary(MA$M) and summary(MA$A).

Please give the extra information as a comment on this answer (rather than as a new answer).

ADD COMMENT • link 9.2 years ago Gordon Smyth 52k

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Hi Gordon

Thanks for your reply. From the name of the files it does seem like RNA from the same condition has been hybridized to both channels. I do not have any further info on the hybridizations besides the names of the files themselves. I really don't understand why they were done this way.

Sorry about the typos in the targets frame. Here's my correct targets frame:

SlideNumber
PANC1-0 1
PANC1-10 2
PANC1-14 3
MIAPAC2-0 4
MIAPAC2-10 5
MIAPAC2-14 6
FileName
PANC1-0 jhu_252665229289_S01_GE2_107_Sep09_1_3_POG-CY3_POR-CY5.txt
PANC1-10 jhu_252665229289_S01_GE2_107_Sep09_1_4_P10G-CY3_P10R-CY5.txt
PANC1-14 jhu_252665229290_S01_GE2_107_Sep09_1_1_P14G-CY3_P14R-CY5.txt
MIAPAC2-0 jhu_252665229290_S01_GE2_107_Sep09_1_2_MOG-CY3_MOR-CY5.txt
MIAPAC2-10 jhu_252665229290_S01_GE2_107_Sep09_1_3_M10G-CY3_M10R-CY5.txt
MIAPAC2-14 jhu_252665229290_S01_GE2_107_Sep09_1_4_M14G-CY3_M14R-CY5.txt
Name Cy3 Cy5
PANC1-0 PANC1-0 P0 P0
PANC1-10 PANC1-10 P10 P10
PANC1-14 PANC1-14 P14 P14
MIAPAC2-0 MIAPAC2-0 M0 M0
MIAPAC2-10 MIAPAC2-10 M10 M10
MIAPAC2-14 MIAPAC2-14 M14 M14

>RG <- backgroundCorrect(RG, method="normexp", offset=50)

>MA <- normalizeWithinArrays(RG, method="loess")

>MA <- normalizeBetweenArrays(MA, method="Aquantile")

>targets2 <- targetsA2C(targets)

>u <- unique(targets2$Target)

>f <- factor(targets2$Target, levels=u)

>design <- model.matrix(~0+f)

> colnames(design) <- u
> corfit <- intraspotCorrelation(MA, design)

Warning messages:
1: In statmod::remlscore(y, X, Z) : reml: Max iterations exceeded
2: In statmod::remlscore(y, X, Z) : reml: Max iterations exceeded
3: In statmod::remlscore(y, X, Z) : reml: Max iterations exceeded
4: In statmod::remlscore(y, X, Z) : reml: Max iterations exceeded
5: In statmod::remlscore(y, X, Z) : reml: Max iterations exceeded
6: In statmod::remlscore(y, X, Z) : reml: Max iterations exceeded
7: In statmod::remlscore(y, X, Z) : reml: Max iterations exceeded
8: In statmod::remlscore(y, X, Z) : reml: Max iterations exceeded
9: In statmod::remlscore(y, X, Z) : reml: Max iterations exceeded

> fit <- lmscFit(MA, design, correlation=corfit$consensus)
Error in if (abs(correlation) >= 1) stop("correlation must be strictly between -1 and 1") :
missing value where TRUE/FALSE needed

>summary(corfit$atanh.correlation)
Min. 1st Qu. Median Mean 3rd Qu. Max. NA's
NA NA NA NaN NA NA 44495

>summary(MA$M)
jhu_252665229289_S01_GE2_107_Sep09_1_3_POG-CY3_POR-CY5
Min. :-1.290555
1st Qu.:-0.077030
Median : 0.006482
Mean : 0.005537
3rd Qu.: 0.087279
Max. : 0.969519
jhu_252665229289_S01_GE2_107_Sep09_1_4_P10G-CY3_P10R-CY5
Min. :-1.672056
1st Qu.:-0.094154
Median : 0.006573
Mean : 0.009162
3rd Qu.: 0.108370
Max. : 2.211291
jhu_252665229290_S01_GE2_107_Sep09_1_1_P14G-CY3_P14R-CY5
Min. :-2.303812
1st Qu.:-0.124000
Median : 0.003292
Mean :-0.002939
3rd Qu.: 0.125831
Max. : 1.403780
jhu_252665229290_S01_GE2_107_Sep09_1_2_MOG-CY3_MOR-CY5
Min. :-1.566965
1st Qu.:-0.069998
Median : 0.003873
Mean : 0.006056
3rd Qu.: 0.079320
Max. : 1.666165
jhu_252665229290_S01_GE2_107_Sep09_1_3_M10G-CY3_M10R-CY5
Min. :-3.841923
1st Qu.:-0.108153
Median : 0.003021
Mean :-0.009084
3rd Qu.: 0.109940
Max. : 1.922013
jhu_252665229290_S01_GE2_107_Sep09_1_4_M14G-CY3_M14R-CY5
Min. :-6.689630
1st Qu.:-0.137185
Median : 0.005364
Mean : 0.014088
3rd Qu.: 0.154397
Max. : 6.796540
> summary(MA$A)
jhu_252665229289_S01_GE2_107_Sep09_1_3_POG-CY3_POR-CY5
Min. : 5.690
1st Qu.: 6.096
Median : 6.945
Mean : 7.732
3rd Qu.: 8.858
Max. :17.765
jhu_252665229289_S01_GE2_107_Sep09_1_4_P10G-CY3_P10R-CY5
Min. : 5.690
1st Qu.: 6.097
Median : 6.945
Mean : 7.732
3rd Qu.: 8.858
Max. :17.765
jhu_252665229290_S01_GE2_107_Sep09_1_1_P14G-CY3_P14R-CY5
Min. : 5.700
1st Qu.: 6.097
Median : 6.945
Mean : 7.732
3rd Qu.: 8.858
Max. :17.765
jhu_252665229290_S01_GE2_107_Sep09_1_2_MOG-CY3_MOR-CY5
Min. : 5.690
1st Qu.: 6.097
Median : 6.945
Mean : 7.732
3rd Qu.: 8.858
Max. :17.765
jhu_252665229290_S01_GE2_107_Sep09_1_3_M10G-CY3_M10R-CY5
Min. : 5.690
1st Qu.: 6.097
Median : 6.945
Mean : 7.732
3rd Qu.: 8.858
Max. :17.765
jhu_252665229290_S01_GE2_107_Sep09_1_4_M14G-CY3_M14R-CY5
Min. : 5.690
1st Qu.: 6.097
Median : 6.945
Mean : 7.732
3rd Qu.: 8.858
Max. :17.765

summary(MA$A)
jhu_252665229289_S01_GE2_107_Sep09_1_3_POG-CY3_POR-CY5 jhu_252665229289_S01_GE2_107_Sep09_1_4_P10G-CY3_P10R-CY5
Min. : 5.690 Min. : 5.690
1st Qu.: 6.096 1st Qu.: 6.097
Median : 6.945 Median : 6.945
Mean : 7.732 Mean : 7.732
3rd Qu.: 8.858 3rd Qu.: 8.858
Max. :17.765 Max. :17.765
jhu_252665229290_S01_GE2_107_Sep09_1_1_P14G-CY3_P14R-CY5 jhu_252665229290_S01_GE2_107_Sep09_1_2_MOG-CY3_MOR-CY5
Min. : 5.700 Min. : 5.690
1st Qu.: 6.097 1st Qu.: 6.097
Median : 6.945 Median : 6.945
Mean : 7.732 Mean : 7.732
3rd Qu.: 8.858 3rd Qu.: 8.858
Max. :17.765 Max. :17.765
jhu_252665229290_S01_GE2_107_Sep09_1_3_M10G-CY3_M10R-CY5
Min. : 5.690
1st Qu.: 6.097
Median : 6.945
Mean : 7.732
3rd Qu.: 8.858
Max. :17.765
jhu_252665229290_S01_GE2_107_Sep09_1_4_M14G-CY3_M14R-CY5
Min. : 5.690
1st Qu.: 6.097
Median : 6.945
Mean : 7.732
3rd Qu.: 8.858
Max. :17.765

Hope this helps in the diagnosis of the problem.

ADD REPLY • link updated 9.2 years ago by Gordon Smyth 52k • written 9.2 years ago by pm2015 • 0

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Thanks for the corrected targets information. I see the problem now.

The experiment has a different treatment on each array, and no two arrays share the same treatment. This makes it impossible to distinguish inter-array variation from inter-treatment variation, making it also impossible to estimate the intraspot correlation. Hence the function just returns NAs.

I have to tell you that there is no statistically rigorous way to analyze this experiment. With a different treatment on every array, there is essentially no replication. The two channels on each array are wasted as replicates without knowing the intraspot correlation.

The only way to try to rescue this experiment is to put in a preset value for the intraspot correlation. The following article

http://www.biomedcentral.com/1471-2105/14/165

shows that intraspot correlations tend to be between about 0.65 and 0.9 for two colour arrays. So I suggest you put in a value of about 0.8:

fit <- lmscFit(MA, design, correlation=0.8)

and proceed from there.

ADD REPLY • link 9.2 years ago Gordon Smyth 52k

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Thanks a lot. I will proceed as you suggested.

ADD REPLY • link 9.2 years ago pm2015 • 0

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Hello,

if I try to put a preset value for the intraspot correlation using your command, I get the following error in R:

Fehler in fit$effects[(fit$rank + 1):ny, , drop = FALSE] :
Indizierung außerhalb der Grenzen

It's in German, but 'Fehler' means 'error' and 'Indizierung außerhalb der Grenzen' could be translated into 'indexing outside borders'.

I'm not sure whether this indicates a R or limma error?

I would like to perform "Separate Channel Analysis of Two-Color Data" (Chap 12 in the limma manual) but don't have replicates in this experiment.

I would appreciate any suggestions.

Best regards

Alfons

ADD REPLY • link 8.3 years ago a.weig • 0