[BioC] combat error message

Fri May 16 12:48:40 CEST 2014

Dear Peter,

Thank you for your response.

Yes, I should have seen it being confounding!
Groups "a" and "b" are different, as I understand from my lab colleagues
since they are 2 separate cell lines.

Perhaps then I could compare gene lists to get an idea of possible 'a' and
'b' differences? Where I do the comparisons I want in group a, similarly
in group b and then subsequently compare the 'a' gene list to the 'b' gene
list.

Many Thanks,
Natasha

On 15/05/2014 17:49, "Peter Langfelder" <peter.langfelder at gmail.com> wrote:

>Your group and batch variables are dependent. All group "a" samples
>are in batches 1,2,3, and all group "b" samples are in batches 4,5. It
>is therefore impossible to tell whether a difference between groups
>a[anything] and b[anything] are due to group differences or batch
>differences, so ComBat cannot adjust for batch effect while preserving
>differences between groups.
>
>Mathematically, the linear model within ComBat does not have a unique
>solution, which is manifested by the fact that the relevant matrix
>cannot be inverted, and it produces the error you see ("system is
>computationally singular").
>
>Unfortunately, this is a problem of your experimental design and
>there's no computational way around it (that I'm aware of). If the
>groups "a" and "b" aren't truly different (i.e., a1 and b1 are
>comparable etc.), you may be able to get by by combining the
>corresponding a and b groups.
>
>HTH,
>
>Peter
>
>
>On Thu, May 15, 2014 at 8:11 AM, Natasha [guest] <guest at bioconductor.org>
>wrote:
>> Dear List,
>>
>> I have a microarray dataset with 30 samples which I have normalised
>>using the vsn function.
>>
>> >From this data, clustering and PCA plots show that I appear to have
>>chip and batch effects which I would like to account for using combat.
>>However, when I try to account  for the batch effect, I get an error
>>that I do not follow.
>>
>> The code I have used is below:
>> =========
>>> chip
>> # 1 2 2 3 1 3 3 2 3 2 3 1 2 2 1 2 3 1 3 1 3 1 2 3 3 1 1 1 3 2
>>> group
>> # a_Cont   a_Cont   a_Cont   a_1 a_1 a_1 a_2 a_2 a_2 a_3 a_3 a_3 a_4
>>a_4   a_4   b_Cont b_Cont  b_Cont   b_1  b_1  b_1  b_2  b_2  b_2  b_3
>>b_3  b_3  b_4    b_4    b_4
>> # Levels: a_Cont a_1 a_2 a_3 a_4 b_Cont b_1 b_2 b_3 b_4
>>> mod = model.matrix(~as.factor(group))
>>
>>> combat.c = ComBat(dat=d.norm, batch=chip, mod=mod, numCovs=NULL,
>>>par.prior=TRUE, prior.plots=FALSE)
>> # Found 3 batches
>> # Found 9  categorical covariate(s)
>> # Standardizing Data across genes
>> # Fitting L/S model and finding priors
>> # Finding parametric adjustments
>> # Adjusting the Data
>>
>> ### Combat to get rid of batch effect
>>> day2
>>  # 1 2 3 1 2 3 1 2 3 1 2 3 1 2 3 4 4 5 4 4 5 4 4 5 4 4 5 4 4 5
>>>mod #same as above
>>
>>> combat.b = ComBat(dat=combat.c, batch=day2, mod=mod, numCovs=NULL,
>>>par.prior=TRUE, prior.plots=FALSE)
>> # Found 5 batches
>> # Found 9  categorical covariate(s)
>> # Standardizing Data across genes
>> ####### Error in solve.default(t(design) %*% design) :
>> #######   system is computationally singular: reciprocal condition
>>number = 7.93016e-18
>> =========
>>
>> Any help much appreciated.
>> (I do know that the R /BioC version is not the latest, but hoping that
>>is not the case here!)
>>
>> Many Thanks,
>> Natasha
>>
>>  -- output of sessionInfo():
>>
>> sessionInfo()
>> # R version 3.0.2 (2013-09-25)
>> # Platform: x86_64-apple-darwin10.8.0 (64-bit)
>> #
>> # locale:
>> #   [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
>> #
>> # attached base packages:
>> #   [1] parallel  stats     graphics  grDevices utils     datasets
>>methods   base
>> #
>> # other attached packages:
>> #   [1] gplots_2.13.0         WriteXLS_3.5.0        limma_3.18.13
>>  genefilter_1.44.0     sva_3.8.0             mgcv_1.7-29
>>nlme_3.1-117          corpcor_1.6.6
>> # [9] ClassDiscovery_2.14.1 PreProcess_2.12.3     oompaBase_3.0.0
>>mclust_4.3            cluster_1.15.2        scatterplot3d_0.3-35
>>gdata_2.13.3          vsn_3.30.0
>> # [17] Biobase_2.22.0        BiocGenerics_0.8.0
>> #
>> # loaded via a namespace (and not attached):
>> #   [1] affy_1.40.0           affyio_1.30.0         annotate_1.40.1
>>  AnnotationDbi_1.24.0  BiocInstaller_1.12.1  bitops_1.0-6
>>caTools_1.17          DBI_0.2-7
>> # [9] grid_3.0.2            gtools_3.4.0          IRanges_1.20.7
>>KernSmooth_2.23-12    lattice_0.20-29       Matrix_1.1-3
>>preprocessCore_1.24.0 RSQLite_0.11.4
>> # [17] splines_3.0.2         stats4_3.0.2          survival_2.37-7
>> tools_3.0.2           XML_3.95-0.2          xtable_1.7-3
>>zlibbioc_1.8.0
>>
>>
>> --
>> Sent via the guest posting facility at bioconductor.org.
>>
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Natasha Sahgal | Postdoctoral Research Assistant
Centre for Molecular Oncology
Barts Cancer Institute - a Cancer Research UK Centre of Excellence
Queen Mary, University of London
John Vane Science Centre, Charterhouse Square, London EC1M 6BQ
Tel: +44 (0)20 7882 3560 | Fax: +44 (0)20 7882 3884 | www.bci.qmul.ac.uk/research/centre-profiles/molecular-oncology.html

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