[BioC] DESeq2: nested multi-factor design, singular matrix error

[Michael Love]

Ah that makes sense. Another question, are you interested in the
treatment effect for each different sample? Or are you interested in
the general effect of treatment, controlling for replicate and sample.


On Mon, Jul 28, 2014 at 4:18 PM, BJ Chen <bj.j.chen at gmail.com> wrote:
>
>
> Hi Mike,
>
> Sorry I was not being clear on the experiment design.  Replicate 1 in sample A are different from replicate 1 in sample B. The replicate number just means there are two replicates for each sample. So the experiment was done as the following
>
> Sample A -> take one replicate (label A1) --> no treatment
>                                                             \--> with treatment
> Sample A -> take the 2nd replicate (label A2) --> no treatment
>                                                                   \--> with treatment
> Sample B -> take one replicate (label B1) --> no treatment
>                                                             \--> with treatment
>
> The samples are "paired" in terms that each replicate of a sample is going through either no treatment or with treatment.
>
> Hopefully this explains it better. If not, please let me know.
>
> Thanks for your help,
> BJ
>
>
>
>
>
>
>
> On Mon, Jul 28, 2014 at 4:07 PM, Michael Love <michaelisaiahlove at gmail.com> wrote:
>>
>> hi BJ,
>>
>> ignoreRank is only for advanced use, so you don't want to use that.
>>
>> I don't fully understand what replicate 1 and 2 are, can you explain? You say replicate 1 and 2 are paired samples for treatment, but they are both also sample A? How is replicate 1 sample A related to replicate 1 sample B?
>>
>> Mike
>>
>>
>> On Mon, Jul 28, 2014 at 3:50 PM, BJ Chen <bj.j.chen at gmail.com> wrote:
>>>
>>> Hi,
>>>
>>> I am trying to run DESeq2 analysis on sample design like this:
>>>
>>> sample   replicate  treatment
>>> A        1          no
>>> A        2          no
>>> A        1          yes
>>> A        2          yes
>>> B        1          no
>>> B        2          no
>>> B        1          yes
>>> B        2          yes
>>> (repeated for 4 different samples. eg. A-D).
>>>
>>> The interests of DE effect include treatment on each sample and treatment
>>> over all.
>>>
>>> I have searched online and found previously suggested model as
>>> ~ treatment + sample:replicate + sample:treatment.
>>>
>>>
>>> However, when I called DESeqDataSetFromMatrix(readcount, sampleinfo,
>>> ~treatment+sample:replicate+sample:treatment), it first complained the
>>> matrix is not full rank.
>>>
>>> I tried ignoreRank option, but then I got error when I called DESeq()
>>> (default parameters):
>>>
>>> error: inv(): matrix appears to be singular
>>>                                               Error in eval(expr, envir,
>>> enclos) : inv(): matrix appears to be singular
>>>         In addition: Warning message:
>>>                                                       In
>>> fitNbinomGLMs(objectNZ[fitidx, , drop = FALSE], alpha_hat =
>>> alpha_hat[fitidx],  :                25rows had non-positive estimates of
>>> variance for coefficients, likely due to rank deficient model matrices
>>> without betaPrior
>>>
>>> If I exclude the replicate ( eg.
>>> design=~treatment+sample+sample:treatment), it run through without errors.
>>> However, I would like to take into account the replicates, as they are
>>> paired samples for the treatment.
>>>
>>> I will appreciate any help/suggestions.
>>>
>>> Thanks,
>>> BJ
>>>
>>>
>>>
>>>
>>> Session info is included in the bottom.
>>>
>>> R
>>> versio(2014-04-10)4-04-10)
>>>
>>> Platform: x86_64-unknown-linux-gnu (64-bit)
>>>
>>>
>>>                         attached base packages:
>>>
>>>
>>>
>>> [1] parallel  stats     graphics  grDevices utils     datasets  methods
>>>                                   [8] base
>>>
>>>
>>>
>>>
>>>
>>>
>>> other attached
>>> packages:
>>>
>>> [1] DESeq2_1.4.5            RcppArmadillo_0.4.200.0
>>> Rcpp_0.11.1
>>>
>>> [4] GenomicRanges_1.14.4    XVector_0.2.0
>>> IRanges_1.20.7
>>>
>>> [7] BiocGenerics_0.8.0
>>>
>>>
>>>                                   loaded via a namespace (and not
>>> attached):
>>>
>>>
>>>  [1] annotate_1.40.1      AnnotationDbi_1.24.0
>>> Biobase_2.22.0
>>>
>>>  [4] DBI_0.2-7            genefilter_1.44.0
>>> geneplotter_1.40.0
>>>
>>>  [7] grid_3.1.0           lattice_0.20-29      locfit_1.5-9.1
>>>                                              [10] RColorBrewer_1.0-5
>>> RSQLite_0.11.4       splines_3.1.0
>>>          [13] stats4_3.1.0         survival_2.37-7      XML_3.98-1.1
>>>                                                 [16] xtable_1.7-3
>>>
>>>         [[alternative HTML version deleted]]
>>>
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>>
>>
>