[BioC] limma warning: Coefficients not estimable

[Karl Brand]

Cheers Jim,

On 2/10/2010 5:57 PM, James W. MacDonald wrote:
> 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.

Right. This helps me alot, some confirmation of what i can and cant 
achieve with my data.
>
> 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.

Unfortunately learning to swim has been faster....along with your (and 
several other non-local statisticians) 'flotation aids'.

sincere thanks for your thoughts,

Karl

>
> 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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-- 
Karl Brand <k.brand at erasmusmc.nl>
Department of Genetics
Erasmus MC
Dr Molewaterplein 50
3015 GE Rotterdam
lab +31 (0)10 704 3409 fax +31 (0)10 704 4743 mob +31 (0)642 777 268