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

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