Hello,

I am attempting to analyze expression differences between three genotypes of mice (1 wildtype, 2 mutated) at three different ages. I would like to compare expression at a particular age across the genotypes – for example, compare wildtype to d52 at age 6 months. I have attached my sample list for clarity:

However, because my model matrix is not full rank, I manually edited the matrix as per the vignette. I am still having trouble though because when I call resultsNames(dds), I am not getting the contrasts that I want.

ddsHTSeq <- DESeqDataSetFromHTSeqCount(sampleTable = sampletable,
                                       directory = directory,
                                       design = ~ 1)
ddsHTSeq_filtered <- ddsHTSeq[rowSums(counts(ddsHTSeq)) > 10, ]

custom.matrix <- model.matrix(~ Genotype + Age:Genotype, data = colData(ddsHTSeq_filtered))
all.zero <- apply(custom.matrix, 2, function(x) all(x==0))
idx <- which(all.zero)
custom.matrix <- custom.matrix[,-idx]

dds <- DESeq(ddsHTSeq_filtered, full = custom.matrix)
resultsNames(dds)

Output: "Intercept" "Genotypemdx" "GenotypeWT" "Genotyped52.Age6m" "Genotypemdx.Age6m" "GenotypeWT.Age6m" "Genotypemdx.Agep8" "GenotypeWT.Agep8"

I also attempted to extract contrasts using the following:

results(dds, contrast = c("Genotype", "WT", "mdx"))

But I receive the following error: only list- and numeric-type contrasts are supported for user-supplied model matrices

Any help would be greatly appreciated!!

sessionInfo( )
R version 3.6.2 (2019-12-12)
Platform: x86_64-apple-darwin15.6.0 (64-bit)
Running under: macOS Mojave 10.14

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib

locale:
[1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] forcats_0.5.1               stringr_1.4.0               dplyr_1.0.4                 purrr_0.3.4                
 [5] readr_1.4.0                 tidyr_1.1.2                 tibble_3.0.6                tidyverse_1.3.0            
 [9] ggplot2_3.3.3               pheatmap_1.0.12             apeglm_1.8.0                DESeq2_1.26.0              
[13] SummarizedExperiment_1.16.1 DelayedArray_0.12.3         BiocParallel_1.20.1         matrixStats_0.58.0         
[17] Biobase_2.46.0              GenomicRanges_1.38.0        GenomeInfoDb_1.22.1         IRanges_2.20.2             
[21] S4Vectors_0.24.4            BiocGenerics_0.32.0        

loaded via a namespace (and not attached):
 [1] colorspace_2.0-0       ellipsis_0.3.1         htmlTable_2.1.0        XVector_0.26.0         base64enc_0.1-3       
 [6] fs_1.5.0               rstudioapi_0.13        farver_2.0.3           bit64_4.0.5            AnnotationDbi_1.48.0  
[11] mvtnorm_1.1-1          lubridate_1.7.9.2      xml2_1.3.2             splines_3.6.2          cachem_1.0.4          
[16] geneplotter_1.64.0     knitr_1.31             Formula_1.2-4          jsonlite_1.7.2         broom_0.7.4           
[21] annotate_1.64.0        cluster_2.1.1          dbplyr_2.1.0           png_0.1-7              compiler_3.6.2        
[26] httr_1.4.2             backports_1.2.1        assertthat_0.2.1       Matrix_1.3-2           fastmap_1.1.0         
[31] cli_2.3.0              htmltools_0.5.1.1      tools_3.6.2            coda_0.19-4            gtable_0.3.0          
[36] glue_1.4.2             GenomeInfoDbData_1.2.2 Rcpp_1.0.6             bbmle_1.0.23.1         cellranger_1.1.0      
[41] vctrs_0.3.6            xfun_0.21              rvest_0.3.6            lifecycle_1.0.0        XML_3.99-0.3          
[46] zlibbioc_1.32.0        MASS_7.3-53.1          scales_1.1.1           hms_1.0.0              RColorBrewer_1.1-2    
[51] memoise_2.0.0          gridExtra_2.3          emdbook_1.3.12         bdsmatrix_1.3-4        rpart_4.1-15          
[56] latticeExtra_0.6-29    stringi_1.5.3          RSQLite_2.2.3          genefilter_1.68.0      checkmate_2.0.0       
[61] rlang_0.4.10           pkgconfig_2.0.3        bitops_1.0-6           lattice_0.20-41        labeling_0.4.2        
[66] htmlwidgets_1.5.3      bit_4.0.4              tidyselect_1.1.0       plyr_1.8.6             magrittr_2.0.1        
[71] R6_2.5.0               generics_0.1.0         Hmisc_4.4-2            DBI_1.1.1              pillar_1.4.7          
[76] haven_2.3.1            foreign_0.8-75         withr_2.4.1            survival_3.2-7         RCurl_1.98-1.2        
[81] nnet_7.3-15            modelr_0.1.8           crayon_1.4.1           jpeg_0.1-8.1           locfit_1.5-9.4        
[86] grid_3.6.2             readxl_1.3.1           data.table_1.13.6      blob_1.2.1             reprex_1.0.0          
[91] digest_0.6.27          xtable_1.8-4           numDeriv_2016.8-1.1    munsell_0.5.0

I'd recommend you work with a statistician to figure out how to map from contrasts of interest to the coefficients of the model matrix.

"only list- and numeric-type contrasts" this refers to how to provide information to contrast, and these are explained in ?results.

A list-type contrast looks like list("...", "...") where you can fill in the coefficient name for the numerator and for the denominator of the fold change, respectively.

If you are interested in a single coefficient, you should use name when calling results().