DESeq2: One or more designs for contrasts of interest
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Mirin1357 • 0
Last seen 5 days ago


despite reading numerous tutorials and workshops on DESeq2 package (which are of a tremendous help!), I cannot seem to be able to have the appropriate design and be able to get out the contrasts of interest.


Two groups of subjects at three time points (no missing data):

  • group G (5 subjects): T0 and T2 normal diet, T4 without gluten
  • group noG (7 subjects): T0 normal diet, T2 and T4 without gluten

If we put it in parallel, to make it clearer

To put it in parallel:

Time4: Ta__Tb__Tc__Td

G:____Ta__Tb__Tc__x : Ta,Tb

noG:__x__Tb__Tc__Td : Tb

where in bold are time points with normal diet for two groups (problem with visualisation so I had to note it aside).

Comparisons of interest:

  1. (G+noG Tb) vs (G+noG Tc) i.e. normal diet vs without gluten
  2. Ta vs Tb only for group G to have paired samples: see the effect of normal diet
  3. Tc vs Td only for group noG to have paired samples: see the long-term effect of diet w/o gluten

As recommended, I wanted to do the model on overall data, and account for between-subject variabilty.

Design proposition 1:

~ Gluten+ Gluten:Subject.n + Gluten:Time (Time: T0/T2/T4) As suggested by the tutorial, model matrix was filtered from 0 rows and provided to DESeq2 function.

The design should take into account that at time T2, two groups will be different and also the intersubject variability.

resultNames output:

 [1] "Intercept"            "GlutennoG"            "GlutenG.Subject.nb"   "GlutennoG.Subject.nb" ""  
 [6] "" "GlutenG.Subject.nd"   "GlutennoG.Subject.nd" ""   ""
[11] "" "" "GlutenG.TimeT2"       "GlutennoG.TimeT2"     "GlutenG.TimeT4"      
[16] "GlutennoG.TimeT4"

Here I can answer my questions b) and c), considering comparisons within each group and between specific time points by specifing contrast as follows:

# noG: T2vsT4
res_G.T0_T2.OTU = results (dds_G.T0_T2, cooksCutoff = FALSE, alpha = alpha, contrast = list("GlutennoG.TimeT2", "GlutennoG.TimeT4"))

However, I cannot take out the effect for the non-referent level (GlutennoG is reference level, and TimeT0), trying to do as suggested for the non-referent level. ( results function)

# G: T0vsT2
res_G.T0_T2.OTU = results (dds_G.T0_T2, cooksCutoff = FALSE, alpha = alpha, contrast = list("Time_T2_vs_T0", "GlutenG.TimeT2")) 

Error in checkContrast(contrast, resNames) : all elements of the contrast as a list of length 2 should be elements of 'resultsNames(object)'

I have seen the post where it was suggested to use betaPrior = TRUE, but in interactions it is by default off, so this does not seem to be a solution (?). Also, with this design it is impossible to do grouped analysis (G_T2+noG_T0) vs (G_T4+noG_T2) (eq. last time point normal diet vs first time point noGluten).

Design proposition 2:

I tried to use the same logic but with four time points. ~ Gluten+ Gluten:Subject.n + Gluten:Time4 (Time4: Ta/Tb/Tc/Td)

even after renaming subjects and removing 0 rows, an error 'Model matrix not full rank'

Design proposition 3: Finally, the simplest design without take into consideration paired samples.

~ Subject + Time4

simple to obtain grouped analysis (G+noG Tb) vs (G+noG Tc) (eq. last time point normal diet vs first time point noGluten), however no possibility to have within group analysis for two group-specific time points (i.e. Ta vs Tb for G, and Tc vs Td for noG).

My questions:

  1. Is it possible to have an unique model to answer all three of my questions through contrasts?
  2. In any case, what should be the most appropriate design?
  3. How should I specify contrasts since the suggested method does not seem to work (assuming I have used it properly)?

Thank you in advance for your time and help!

R version 4.0.5 (2021-03-31)
Platform: x86_64-w64-mingw32/x64 (64-bit)
Running under: Windows 10 x64 (build 19042)

Matrix products: default

[1] LC_COLLATE=French_France.1252  LC_CTYPE=French_France.1252    LC_MONETARY=French_France.1252 LC_NUMERIC=C                  
[5] LC_TIME=French_France.1252    

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

other attached packages:
 [1] vsn_3.58.0                  stromboli_0.1.0             randomcoloR_1.1.0.1         xlsx_0.6.5.9000            
 [5] EnhancedVolcano_1.9.13      ggrepel_0.9.1               microbiomeutilities_1.00.16 microbiomeSeq_0.1          
 [9] microbiome_1.12.0           pheatmap_1.0.12             DESeq2_1.30.1               SummarizedExperiment_1.20.0
[13] Biobase_2.50.0              MatrixGenerics_1.2.0        matrixStats_0.58.0          GenomicRanges_1.42.0       
[17] GenomeInfoDb_1.26.1         IRanges_2.24.0              S4Vectors_0.28.0            BiocGenerics_0.36.0        
[21] vegan_2.5-7                 lattice_0.20-44             permute_0.9-5               ggpubr_0.4.0               
[25] ggsci_2.9                   ggplot2_3.3.3               dplyr_1.0.6                 phyloseq_1.34.0            

loaded via a namespace (and not attached):
  [1] utf8_1.2.1             tidyselect_1.1.1       RSQLite_2.2.7          AnnotationDbi_1.52.0   grid_4.0.5            
  [6] BiocParallel_1.24.1    Rtsne_0.15             RNeXML_2.4.5           munsell_0.5.0          preprocessCore_1.52.1 
 [11] codetools_0.2-18       units_0.7-2            withr_2.4.2            colorspace_2.0-1       ggalt_0.4.0           
 [16] knitr_1.33             uuid_0.1-4             rstudioapi_0.13        ggsignif_0.6.1         rJava_1.0-4           
 [21] Rttf2pt1_1.3.8         labeling_0.4.2         GenomeInfoDbData_1.2.4 farver_2.1.0           bit64_4.0.5           
 [26] rhdf5_2.34.0           coda_0.19-4            LearnBayes_2.15.1      vctrs_0.3.8            generics_0.1.0        
 [31] xfun_0.23              adegenet_2.1.3         adephylo_1.1-11        R6_2.5.0               ggbeeswarm_0.6.0      
 [36] locfit_1.5-9.4         btools_0.0.1           bitops_1.0-7           rhdf5filters_1.2.0     cachem_1.0.5          
 [41] DelayedArray_0.16.0    assertthat_0.2.1       promises_1.2.0.1       scales_1.1.1           beeswarm_0.4.0        
 [46] gtable_0.3.0           phylobase_0.8.10       ash_1.0-15             affy_1.68.0            rlang_0.4.11          
 [51] genefilter_1.72.0      splines_4.0.5          extrafontdb_1.0        rstatix_0.7.0          lazyeval_0.2.2        
 [56] hexbin_1.28.2          broom_0.7.6            GUniFrac_1.2           BiocManager_1.30.15    yaml_2.2.1            
 [61] reshape2_1.4.4         abind_1.4-5            backports_1.2.1        httpuv_1.6.1           gghalves_0.1.1        
 [66] extrafont_0.17         tools_4.0.5            spData_0.3.8           affyio_1.60.0          ellipsis_0.3.2        
 [71] jquerylib_0.1.4        raster_3.4-10          biomformat_1.18.0      RColorBrewer_1.1-2     proxy_0.4-26          
 [76] Rcpp_1.0.6             plyr_1.8.6             progress_1.2.2         zlibbioc_1.36.0        classInt_0.4-3        
 [81] purrr_0.3.4            RCurl_1.98-1.3         prettyunits_1.1.1      deldir_0.2-10          cowplot_1.1.1         
 [86] haven_2.4.1            cluster_2.1.0          tinytex_0.32           magrittr_2.0.1         data.table_1.14.0     
 [91] openxlsx_4.2.3         gmodels_2.18.1         xlsxjars_0.6.1         hms_1.1.0              mime_0.10             
 [96] evaluate_0.14          xtable_1.8-4           XML_3.99-0.6           rio_0.5.26             jpeg_0.1-8.1          
[101] readxl_1.3.1           gridExtra_2.3          compiler_4.0.5         maps_3.3.0             tibble_3.1.2          
[106] V8_3.4.2               KernSmooth_2.23-20     crayon_1.4.1           htmltools_0.5.1.1      mgcv_1.8-35           
[111] later_1.2.0            spdep_1.1-8            tidyr_1.1.3            geneplotter_1.68.0     expm_0.999-6          
[116] DBI_1.1.1              proj4_1.0-10.1         MASS_7.3-53            sf_0.9-8               boot_1.3-28           
[121] Matrix_1.2-18          ade4_1.7-16            car_3.0-10             cli_2.5.0              adegraphics_1.0-15    
[126] gdata_2.18.0           igraph_1.2.6           forcats_0.5.1          pkgconfig_2.0.3        adespatial_0.3-14     
[131] rncl_0.8.4             foreign_0.8-80         sp_1.4-5               xml2_1.3.2             foreach_1.5.1         
[136] annotate_1.68.0        bslib_0.2.5.1          vipor_0.4.5            multtest_2.46.0        XVector_0.30.0        
[141] stringr_1.4.0          digest_0.6.27          Biostrings_2.58.0      rmarkdown_2.8          cellranger_1.1.0      
[146] curl_4.3.1             shiny_1.6.0            gtools_3.8.2           lifecycle_1.0.0        nlme_3.1-152          
[151] jsonlite_1.7.2         Rhdf5lib_1.12.0        carData_3.0-4          seqinr_4.2-8           limma_3.46.0          
[156] fansi_0.4.2            pillar_1.6.1           ggrastr_0.2.3          fastmap_1.1.0          httr_1.4.2            
[161] survival_3.2-11        glue_1.4.2             zip_2.2.0              png_0.1-7              iterators_1.0.13      
[166] bit_4.0.4              sass_0.4.0             class_7.3-19           stringi_1.5.3          blob_1.2.1            
[171] latticeExtra_0.6-29    memoise_2.0.0          e1071_1.7-6            ape_5.5-1
DESeq2 • 123 views
Entering edit mode
Last seen 35 minutes ago
United States

Sorry, I can only provide software support at this time. I don’t have time to provide statistical consultation on the support site. I’d recommend to partner with someone familiar with linear models in R.

Entering edit mode

Thank you for your answer Michael Love, as I understand that this inquiry might be out of scope of your package support assistance.

Unfortunately, as I am not in position to team up with somebody having more profound knowledge of linear models, I will try to find solution either through other posted examples or, with a bit of luck, if one of community members finds answer to this question.

All the best


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