[BioC] limma warning: Coefficients not estimable
James W. MacDonald
jmacdon at med.umich.edu
Wed Feb 10 17:57:46 CET 2010
Hi Karl,
Karl Brand wrote:
> Dear BioC,
>
> Using limma, when fitting the model:
> model.matrix(~Tissue * Pperiod + Time + Animal)
>
> I get this warning:
> > fit <- lmFit(rma.pp, design)
> Coefficients not estimable: Animal32 Animal33 Animal34 Animal35 Animal36
> Animal37 Animal38 Animal39 Animal40 Animal41 Animal42 Animal43 Animal44
> Animal45 Animal46 Animal47 Animal48
> Warning message:
> Partial NA coefficients for 45101 probe(s)
>
> In addition, the reuslting number or DE genes for my contrasts of
> interest (which are different than the 'not estimable' ones listed in
> teh warning above) are mcuh lower than expected; & furthermore, the
> contrast-coefficents (log2FCs) and simply wrong.
>
> When fitting a similar model, merely lacking the 'pairing' factor
> ("Animal"):
> model.matrix(~Tissue * Pperiod + Time)
>
> I don't get this error. My question:
>
> Is it me? Or am i attempting the impossible, ie., by including a factor
> for pairing (Animal) trying to fit more factors than my measurements can
> support and this is limma's way of telling me? Raw script and targets
> file below.
You may be attempting the impossible, or you may just be doing something
incorrectly. You are certainly trying to estimate more parameters than
you have data with which to do so.
It looks like you have a fairly complex experimental design, so I would
recommend finding a local statistician who can help you with the analysis.
>
> I really hope an experienced limma user can enlighten me on this, or
> point me to a resource suitable for a biologists level of understanding.
Pretty much any basic linear modeling textbook would be helpful.
However, it looks like you might have a timecourse experiment with
perhaps repeated measures, which may require a non-trivial analysis
method. As a Biologist, you might have jumped into the deep end of the
pool, so finding somebody local to help is not a bad idea.
Best,
Jim
>
> Thanks in advance,
>
> Karl
>
>
> > targets <- read.delim("RNA_Targets.txt")
>
> > Tissue <- factor(targets$Tissue, levels = c("R", "C"))
>
> > Pperiod <- factor(targets$Pperiod, levels = c("E", "L", "S"))
>
> > Time <- factor(targets$Time, levels = c("1", "2", "3", "4",
> + "5", "6", "7", "8",
> + .... [TRUNCATED]
>
> > Animal <- factor(targets$Animal, levels = c("1", "2", "3", "4",
> + "5", "6", "7", "8",
> + .... [TRUNCATED]
>
> > design <- model.matrix(~Tissue * Pperiod + Time + Animal)
>
> > colnames(design)
> [1] "(Intercept)" "TissueC" "PperiodL" "PperiodS"
> "Time2" "Time3" "Time4" "Time5"
> "Time6" "Time7"
> [11] "Time8" "Time9" "Time10" "Time11"
> "Time12" "Time13" "Time14"
> "Time15" "Time16" "Animal2"
> [21] "Animal3" "Animal4" "Animal5" "Animal6"
> "Animal7" "Animal8" "Animal9"
> "Animal10" "Animal11" "Animal12"
> [31] "Animal13" "Animal14" "Animal15" "Animal16"
> "Animal17" "Animal18" "Animal19"
> "Animal20" "Animal21" "Animal22"
> [41] "Animal23" "Animal24" "Animal25" "Animal26"
> "Animal27" "Animal28" "Animal29"
> "Animal30" "Animal31" "Animal32"
> [51] "Animal33" "Animal34" "Animal35" "Animal36"
> "Animal37" "Animal38" "Animal39"
> "Animal40" "Animal41" "Animal42"
> [61] "Animal43" "Animal44" "Animal45" "Animal46"
> "Animal47" "Animal48" "TissueC:PperiodL"
> "TissueC:PperiodS"
> > source(.trPaths[5], echo=TRUE, max.deparse.length=150)
>
> > fit <- lmFit(rma.pp, design)
> Coefficients not estimable: Animal32 Animal33 Animal34 Animal35 Animal36
> Animal37 Animal38 Animal39 Animal40 Animal41 Animal42 Animal43 Animal44
> Animal45 Animal46 Animal47 Animal48
> Warning message:
> Partial NA coefficients for 45101 probe(s)
> >
>
>
> FileName Tissue Pperiod Time Animal
> 01-PPL3-sample02.CEL R S 1 1
> 02-PPL3-sample03.CEL C S 1 1
> 03-PPL5-sample02.CEL R S 2 2
> 04-PPL5-sample03.CEL C S 2 2
> 05-PPL3-sample04.CEL R S 3 3
> 06-PPL3-sample05.CEL C S 3 3
> 07-PPL5-sample04.CEL R S 4 4
> 08-PPL5-sample05.CEL C S 4 4
> 09-PPL3-sample06.CEL R S 5 5
> 10-PPL3-sample07.CEL C S 5 5
> 11-PPL5-sample06.CEL R S 6 6
> 12-PPL5-sample07.CEL C S 6 6
> 13-PPL3-sample08.CEL R S 7 7
> 14-PPL3-sample09.CEL C S 7 7
> 15-PPL5-sample08.CEL R S 8 8
> 16-PPL5-sample09.CEL C S 8 8
> 17-PPL3-sample10.CEL R S 9 9
> 18-PPL3-sample11.CEL C S 9 9
> 19-PPL5-sample10.CEL R S 10 10
> 20-PPL5-sample11.CEL C S 10 10
> 21-PPL3-sample12.CEL R S 11 11
> 22-PPL3-sample13.CEL C S 11 11
> 23-PPL5-sample12.CEL R S 12 12
> 24-PPL5-sample13.CEL C S 12 12
> 25-PPL3-sample14.CEL R S 13 13
> 26-PPL3-sample15.CEL C S 13 13
> 27-PPL5-sample14.CEL R S 14 14
> 28-PPL5-sample15.CEL C S 14 14
> 29-PPL3-sample16.CEL R S 15 15
> 30-PPL3-sample17.CEL C S 15 15
> 31-PPL5-sample16.CEL R S 16 16
> 32-PPL5-sample17.CEL C S 16 16
> 33-PPL1-sample02.CEL R E 1 17
> 34-PPL1-sample03.CEL C E 1 17
> 35-PPL6-sample02.CEL R E 2 18
> 36-PPL6-sample03.CEL C E 2 18
> 37-PPL1-sample04.CEL R E 3 19
> 38-PPL1-sample05.CEL C E 3 19
> 39-PPL6-sample04.CEL R E 4 20
> 40-PPL6-sample05.CEL C E 4 20
> 41-PPL1-sample06.CEL R E 5 21
> 42-PPL1-sample07.CEL C E 5 21
> 43-PPL6-sample06.CEL R E 6 22
> 44-PPL6-sample07.CEL C E 6 22
> 45-PPL1-sample08.CEL R E 7 23
> 46-PPL1-sample09.CEL C E 7 23
> 47-PPL6-sample08.CEL R E 8 24
> 48-PPL6-sample09.CEL C E 8 24
> 49-PPL1-sample10.CEL R E 9 25
> 50-PPL1-sample11.CEL C E 9 25
> 51-PPL6-sample10.CEL R E 10 26
> 52-PPL6-sample11.CEL C E 10 26
> 53-PPL1-sample12.CEL R E 11 27
> 54-PPL1-sample13.CEL C E 11 27
> 55-PPL6-sample12.CEL R E 12 28
> 56-PPL6-sample13.CEL C E 12 28
> 57-PPL1-sample14.CEL R E 13 29
> 58-PPL1-sample15.CEL C E 13 29
> 59-PPL6-sample14.CEL R E 14 30
> 60-PPL6-sample15.CEL C E 14 30
> 61-PPL1-sample16.CEL R E 15 31
> 62-PPL1-sample17.CEL C E 15 31
> 63-PPL6-sample16.CEL R E 16 32
> 64-PPL6-sample17.CEL C E 16 32
> 65-PPL2-sample02.CEL R L 1 33
> 66-PPL2-sample03.CEL C L 1 33
> 67-PPL4-sample02.CEL R L 2 34
> 68-PPL4-sample03.CEL C L 2 34
> 69-PPL2-sample04.CEL R L 3 35
> 70-PPL2-sample05.CEL C L 3 35
> 71-PPL4-sample04.CEL R L 4 36
> 72-PPL4-sample05.CEL C L 4 36
> 73-PPL2-sample06.CEL R L 5 37
> 74-PPL2-sample07.CEL C L 5 37
> 75-PPL4-sample06.CEL R L 6 38
> 76-PPL4-sample07.CEL C L 6 38
> 77-PPL2-sample08.CEL R L 7 39
> 78-PPL2-sample09.CEL C L 7 39
> 79-PPL4-sample08.CEL R L 8 40
> 80-PPL4-sample09.CEL C L 8 40
> 81-PPL2-sample10.CEL R L 9 41
> 82-PPL2-sample11.CEL C L 9 41
> 83-PPL4-sample10.CEL R L 10 42
> 84-PPL4-sample11.CEL C L 10 42
> 85-PPL2-sample12.CEL R L 11 43
> 86-PPL2-sample13.CEL C L 11 43
> 87-PPL4-sample12.CEL R L 12 44
> 88-PPL4-sample13.CEL C L 12 44
> 89-PPL2-sample14.CEL R L 13 45
> 90-PPL2-sample15.CEL C L 13 45
> 91-PPL4-sample14.CEL R L 14 46
> 92-PPL4-sample15.CEL C L 14 46
> 93-PPL2-sample16.CEL R L 15 47
> 94-PPL2-sample17.CEL C L 15 47
> 95-PPL4-sample16.CEL R L 16 48
> 96-PPL4-sample17.CEL C L 16 48
>
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