I am trying to calculate the enrichment of control (IgG) of an RIP-seq experiment. Since it was performed in triplicates, one approach I am exploring is using DESeq2 to take advantage of that replicate goodness. The goals:

1. Determine enrichment of specific IPs over a background control.
2. Possibly determine enrichment difference between IPs (testing DESeq2 testing ratio of ratios (RIP-Seq, CLIP-Seq, ribosomal profiling))

Details

These samples are small RNA RIP-seqs, and his is what the sample table looks like:

From above we can see that there is only one control condition (N2, assay = IgG) for all four experimental conditions. This is ok to solve (1) using the the formula design= ~ condition and then extracting pairwise results.

Side note, the dispersion plot, default settings, seems to indicate a good fit of the data.

Question/issue

My main doubt is how to prepare the sample table to determine (2), and following DESeq2 testing ratio of ratios (RIP-Seq, CLIP-Seq, ribosomal profiling). I tried design= ~ assay + condition + assay:condition but of course got the error:

Model matrix not full rank

Likely because IgG and N2 are a linear combination. My question is:

Is it possible to change the sample table in a way to avoid this? How would I go about it? I basically want to use N2 as a (background) control for every condition, e.g. `(RFK679 / N2) / (Tyr101 / N2)`, before comparing them between each other (one to one comparisons).

I know this type of question is rather frequent, but even after reading the vignette and a number of threads on this, I am still at a loss.

This is a preliminary analysts, and the experimental design is already being changed to included input as background for each experimental condition. That said, I would still like to a brief feel of what is being enriched in this experimental setting.

sessionInfo()
R version 3.3.3 (2017-03-06)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Debian GNU/Linux 8 (jessie)

locale:
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 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] parallel  stats4    stats     graphics  grDevices utils     datasets 
[8] methods   base     

other attached packages:
 [1] stringr_1.2.0              org.Ce.eg.db_3.4.0        
 [3] AnnotationDbi_1.36.2       pheatmap_1.0.8            
 [5] scales_0.5.0               DESeq2_1.14.1             
 [7] SummarizedExperiment_1.4.0 Biobase_2.34.0            
 [9] GenomicRanges_1.26.4       GenomeInfoDb_1.10.3       
[11] IRanges_2.8.2              S4Vectors_0.12.2          
[13] BiocGenerics_0.20.0        ggplot2_2.2.1             
[15] RColorBrewer_1.1-2         data.table_1.10.4         
[17] fortunes_1.5-4            

loaded via a namespace (and not attached):
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 [4] lattice_0.20-35     colorspace_1.3-2    htmltools_0.3.6    
 [7] base64enc_0.1-3     blob_1.1.0          survival_2.41-3    
[10] XML_3.98-1.9        rlang_0.1.2         foreign_0.8-69     
[13] DBI_0.7             BiocParallel_1.8.2  bit64_0.9-7        
[16] plyr_1.8.4          zlibbioc_1.20.0     munsell_0.4.3      
[19] gtable_0.2.0        htmlwidgets_0.9     memoise_1.1.0      
[22] latticeExtra_0.6-28 knitr_1.17          geneplotter_1.52.0 
[25] highr_0.6           htmlTable_1.9       Rcpp_0.12.12       
[28] acepack_1.4.1       xtable_1.8-2        backports_1.1.0    
[31] checkmate_1.8.3     Hmisc_4.0-3         annotate_1.52.1    
[34] XVector_0.14.1      bit_1.1-12          gridExtra_2.2.1    
[37] digest_0.6.12       stringi_1.1.5       grid_3.3.3         
[40] bitops_1.0-6        tools_3.3.3         magrittr_1.5       
[43] lazyeval_0.2.0      RCurl_1.95-4.8      tibble_1.3.4       
[46] RSQLite_2.0         Formula_1.2-2       cluster_2.0.6      
[49] pkgconfig_2.0.1     Matrix_1.2-11       rpart_4.1-11       
[52] nnet_7.3-12<font face="sans-serif, Arial, Verdana, Trebuchet MS">
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hi Antonio,

"I basically want to use N2 and a control for all other conditions, before comparing them between each other (one to one comparisons)."

Given your experimental design and what you want to compare, you can do ~condition, and then:

results(dds, contrast=c("condition","RFK679","N2"))

etc.

The interaction term is when you have two different assay types, and you want to look for the ratio of ratios: e.g. the fold change in the ratio of two assay types for a condition. Here you only have a single set of measurements per condition, so the above design is the best you can do. So you are just looking at changes in counts, not changes in an enrichment (or ratio, or fold change).