Thanks for help in advance!!
My experimental design involves using DESeqs to look at the impact of three diets (diet1, diet2, and diet3) on gene expression at three sites of animal's gut (colon, cecum and rectum) . Each diet was fed to three animals. All animals were sacrificed at the end of trial and three sections of the intestine (colon, cecum and rectum) was sampled and gene expression was analyzed.
diet animal site var1 var2
diet1 a1 site1
diet1 a2 site1
diet1 a3 site1
diet1 a1 site2
diet1 a2 site2
diet1 a3 site2
diet1 a1 site3
diet1 a2 site3
diet1 a3 site3
diet2 a4 site1
diet2 a5 site1
diet2 a6 site1
diet2 a4 site2
diet2 a5 site2
diet2 a6 site2
diet2 a4 site3
diet2 a5 site3
diet2 a6 site3
diet3 a7 site1
diet3 a8 site1
diet3 a9 site1
diet3 a7 site2
diet3 a8 site2
diet3 a9 site2
diet3 a7 site3
diet3 a8 site3
diet3 a9 site3
I tried " design(dds) <- ~ diet + site:animal + site"
I got the following information
______________
Error in DESeqDataSet(se, design = design, ignoreRank) :
the model matrix is not full rank, so the model cannot be fit as specified.
one or more variables or interaction terms in the design formula
are linear combinations of the others and must be removed
_________________
> sessionInfo()
R version 3.1.3 (2015-03-09)
Platform: x86_64-apple-darwin13.4.0 (64-bit)
Running under: OS X 10.9.5 (Mavericks)
locale:
[1] en_CA.UTF-8/en_CA.UTF-8/en_CA.UTF-8/C/en_CA.UTF-8/en_CA.UTF-8
attached base packages:
[1] stats4 parallel grid splines stats graphics grDevices utils datasets methods base
other attached packages:
[1] ecodist_1.2.9 Biostrings_2.34.1 doParallel_1.0.8 foreach_1.4.3
[5] iterators_1.0.7 metagenomeSeq_1.8.3 interactiveDisplay_1.4.1 limma_3.22.7
[9] Biobase_2.26.0 DESeq2_1.6.3 GenomicRanges_1.18.4 GenomeInfoDb_1.2.5
[13] RcppArmadillo_0.5.000.0 Rcpp_0.12.3 XVector_0.6.0 IRanges_2.0.1
[17] S4Vectors_0.4.0 BiocGenerics_0.12.1 locfit_1.5-9.1 phangorn_2.0.2
[21] genefilter_1.48.1 adephylo_1.1-6 scatterplot3d_0.3-35 analogue_0.16-0
[25] rgl_0.93.1098 princurve_1.1-12 labdsv_1.6-1 mgcv_1.8-6
[29] indicspecies_1.7.5 biom_0.4.0 ggplot2_1.0.1 reshape2_1.4.1
[33] plyr_1.8.3 phyloseq_1.10.0 pamr_1.55 cluster_2.0.1
[37] survival_2.38-1 vegan_2.2-1 lattice_0.20-31 permute_0.8-3
[41] RColorBrewer_1.1-2 matrixStats_0.14.0 MASS_7.3-40 ape_3.4
[45] ade4_1.7-2 nlme_3.1-120
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The formula in my answer above controls for the pairing of animals within diet, the diet effect, and the differences across site. You may want to speak to a local statistician or someone with a quantitative background if you have questions on the interpretation of the formula or extracting results.