Question

DESeq treatment over baseline versus placebo over baseline methodology

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Entering edit mode

walter.eckalbar • 0

@waltereckalbar-11470

Last seen 2.2 years ago

United States

Hi Everyone,

We have a rather complicated clinical trial design from which we're attempting to isolate a treatment effect (allergen challenge - AC) from a control (diluent - DIL) in comparison to a baseline visit (BL). One complicating factor is that the BL visits occur on a different visit, but all of these three treatments happen to each of the subjects (so paired design). These subjects vary in 3 diagnosis groups (Diagnosis) - allergic asthmatic (AA), allergic non-asthmatic (ANA) and non-allergic, non-asthmatic (NANA). Additionally, we have differing time points (24 hr vs 7 day) and allergens (Cat and dust mite).

Clearly each subject is nested within disease groups, but also in their time point (24 Hr or 7D), so I've set up dummy variables to repeat a categorical variable over a group variable (Interval_Diagnosis).

I've pasted a sample of the table below:

                   SampleID Diagnosis Treatment Subject Interval Allergen Diagnosis_Interval_Subject Subject.nested 
AA-002-2BL       AA-002-2BL        AA       2BL ACE-002      24H      Cat             AA_24H_ACE-002              A 
AA-002-3AC       AA-002-3AC        AA       3AC ACE-002      24H      Cat             AA_24H_ACE-002              A 
AA-002-3DIL     AA-002-3DIL        AA      3DIL ACE-002      24H      Cat             AA_24H_ACE-002              A 
AA-004-2BL       AA-004-2BL        AA       2BL ACE-004       7D      Cat              AA_7D_ACE-004              A 
AA-004-3DIL     AA-004-3DIL        AA      3DIL ACE-004       7D      Cat              AA_7D_ACE-004              A 
AA-013-2BL       AA-013-2BL        AA       2BL ACE-013      24H    HDMdp             AA_24H_ACE-013              D 
AA-013-3AC       AA-013-3AC        AA       3AC ACE-013      24H    HDMdp             AA_24H_ACE-013              D 
AA-013-3DIL     AA-013-3DIL        AA      3DIL ACE-013      24H    HDMdp             AA_24H_ACE-013              D 
AA-014-2BL       AA-014-2BL        AA       2BL ACE-014       7D    HDMdp              AA_7D_ACE-014              C 
AA-014-3AC       AA-014-3AC        AA       3AC ACE-014       7D    HDMdp              AA_7D_ACE-014              C 
AA-014-3DIL     AA-014-3DIL        AA      3DIL ACE-014       7D    HDMdp              AA_7D_ACE-014              C 
AA-019-2BL       AA-019-2BL        AA       2BL ACE-019       7D      Cat              AA_7D_ACE-019              B 
AA-019-3AC       AA-019-3AC        AA       3AC ACE-019       7D      Cat              AA_7D_ACE-019              B 
AA-019-3DIL     AA-019-3DIL        AA      3DIL ACE-019       7D      Cat              AA_7D_ACE-019              B

Then I am setting up the model as follows, ignoring Interval and Allergen variables. I also have to remove some zeros from the model matrix, because the groups have uneven subject representation.

m = model.matrix(~  Diagnosis*Treatment  + Interval*Subject.nested + Diagnosis*Subject.nested , conds)

colSums(m)
                  (Intercept)                  DiagnosisANA                 DiagnosisNANA                  Treatment3AC                 Treatment3DIL                    Interval7D               Subject.nestedB 
                           65                             6                             9                            21                            22                            17                             9 
              Subject.nestedC               Subject.nestedD               Subject.nestedE               Subject.nestedF               Subject.nestedG               Subject.nestedH               Subject.nestedI 
                            9                             6                             6                             3                             3                             3                             3 
              Subject.nestedJ               Subject.nestedK               Subject.nestedL     DiagnosisANA:Treatment3AC    DiagnosisNANA:Treatment3AC    DiagnosisANA:Treatment3DIL   DiagnosisNANA:Treatment3DIL 
                            3                             3                             3                             2                             3                             2                             3 
   Interval7D:Subject.nestedB    Interval7D:Subject.nestedC    Interval7D:Subject.nestedD    Interval7D:Subject.nestedE    Interval7D:Subject.nestedF    Interval7D:Subject.nestedG    Interval7D:Subject.nestedH 
                            3                             3                             3                             3                             0                             0                             0 
   Interval7D:Subject.nestedI    Interval7D:Subject.nestedJ    Interval7D:Subject.nestedK    Interval7D:Subject.nestedL  DiagnosisANA:Subject.nestedB DiagnosisNANA:Subject.nestedB  DiagnosisANA:Subject.nestedC 
                            0                             0                             0                             0                             0                             3                             0 
DiagnosisNANA:Subject.nestedC  DiagnosisANA:Subject.nestedD DiagnosisNANA:Subject.nestedD  DiagnosisANA:Subject.nestedE DiagnosisNANA:Subject.nestedE  DiagnosisANA:Subject.nestedF DiagnosisNANA:Subject.nestedF 
                            3                             0                             0                             0                             0                             0                             0 
 DiagnosisANA:Subject.nestedG DiagnosisNANA:Subject.nestedG  DiagnosisANA:Subject.nestedH DiagnosisNANA:Subject.nestedH  DiagnosisANA:Subject.nestedI DiagnosisNANA:Subject.nestedI  DiagnosisANA:Subject.nestedJ 
                            0                             0                             0                             0                             0                             0                             0 
DiagnosisNANA:Subject.nestedJ  DiagnosisANA:Subject.nestedK DiagnosisNANA:Subject.nestedK  DiagnosisANA:Subject.nestedL DiagnosisNANA:Subject.nestedL 
                            0                             0                             0                             0                             0 

m = m[,which(colSums(m)!=0)]

ddsModel <- DESeqDataSetFromMatrix(countData = data, 
                                   colData = conds,
                                   design = ~1)

ddsModel <- DESeq(ddsModel, minReplicatesForReplace = Inf, parallel = T,full = m)

Primarily what we are interested in could be expressed in plain math terms as (3DIL - 2BL) / (3DIL - 2BL) in the allergic asthmatic group. However, I'm unsure how to properly capture this.

If I look at resultsNames:

resultsNames(ddsModel)
 [1] "Intercept"                     "DiagnosisANA"                  "DiagnosisNANA"                 "Treatment3AC"                  "Treatment3DIL"                 "Interval7D"                   
 [7] "Subject.nestedB"               "Subject.nestedC"               "Subject.nestedD"               "Subject.nestedE"               "Subject.nestedF"               "Subject.nestedG"              
[13] "Subject.nestedH"               "Subject.nestedI"               "Subject.nestedJ"               "Subject.nestedK"               "Subject.nestedL"               "DiagnosisANA.Treatment3AC"    
[19] "DiagnosisNANA.Treatment3AC"    "DiagnosisANA.Treatment3DIL"    "DiagnosisNANA.Treatment3DIL"   "Interval7D.Subject.nestedB"    "Interval7D.Subject.nestedC"    "Interval7D.Subject.nestedD"   
[25] "Interval7D.Subject.nestedE"    "DiagnosisNANA.Subject.nestedB" "DiagnosisNANA.Subject.nestedC"

So, my first hunch is simply carry out

res = results(ddsModel,cdiontrast=list("Treatment3AC"))

But that ignores Treatment3DIL. And if I do a specific contrast on those treatments, I get an error because I used a user-supplied model matrix:

results(ddsModel, contrast=c("Treatment","3AC","3DIL"))
Error in results(ddsModel, contrast = c("Treatment", "3AC", "3DIL")) : 
  only list- and numeric-type contrasts are supported for user-supplied model matrices

So, I'd appreciate any ideas on how to properly create a model and/or contrasts given this situation we are in. Thanks for the help.

> sessionInfo()
R version 4.1.1 (2021-08-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 18.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.7.1
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.7.1

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C               LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8     LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8    LC_PAPER=en_US.UTF-8      
 [8] LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

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

other attached packages:
 [1] pathview_1.32.0             rJava_1.0-5                 calibrate_1.7.7             MASS_7.3-54                 gage_2.42.0                 Rtsne_0.15                  BiocParallel_1.26.2        
 [8] devtools_2.4.2              usethis_2.0.1               genefilter_1.74.0           dplyr_1.0.7                 stringr_1.4.0               viridis_0.6.1               viridisLite_0.4.0          
[15] reshape2_1.4.4              cowplot_1.1.1               gtools_3.9.2                cluster_2.1.2               gplots_3.1.1                limma_3.48.3                R.utils_2.11.0             
[22] R.oo_1.24.0                 R.methodsS3_1.8.1           biomaRt_2.48.3              ggrepel_0.9.1               RColorBrewer_1.1-2          ggplot2_3.3.5               DESeq2_1.32.0              
[29] SummarizedExperiment_1.22.0 Biobase_2.52.0              MatrixGenerics_1.4.3        matrixStats_0.61.0          GenomicRanges_1.44.0        GenomeInfoDb_1.28.4         IRanges_2.26.0             
[36] S4Vectors_0.30.2            BiocGenerics_0.38.0         gageData_2.30.0            

loaded via a namespace (and not attached):
  [1] circlize_0.4.13        BiocFileCache_2.0.0    plyr_1.8.6             splines_4.1.1          digest_0.6.28          foreach_1.5.1          htmltools_0.5.2        GO.db_3.13.0           fansi_0.5.0           
 [10] magrittr_2.0.1         memoise_2.0.0          doParallel_1.0.16      remotes_2.4.1          ComplexHeatmap_2.8.0   Biostrings_2.60.2      annotate_1.70.0        prettyunits_1.1.1      colorspace_2.0-2      
 [19] blob_1.2.2             rappdirs_0.3.3         xfun_0.26              callr_3.7.0            crayon_1.4.1           RCurl_1.98-1.5         graph_1.70.0           survival_3.2-13        iterators_1.0.13      
 [28] glue_1.4.2             gtable_0.3.0           zlibbioc_1.38.0        XVector_0.32.0         GetoptLong_1.0.5       DelayedArray_0.18.0    pkgbuild_1.2.0         Rgraphviz_2.36.0       shape_1.4.6           
 [37] scales_1.1.1           DBI_1.1.1              Rcpp_1.0.7             xtable_1.8-4           progress_1.2.2         clue_0.3-60            bit_4.0.4              httr_1.4.2             ellipsis_0.3.2        
 [46] pkgconfig_2.0.3        XML_3.99-0.8           dbplyr_2.1.1           locfit_1.5-9.4         utf8_1.2.2             tidyselect_1.1.1       rlang_0.4.11           AnnotationDbi_1.54.1   munsell_0.5.0         
 [55] tools_4.1.1            cachem_1.0.6           cli_3.0.1              generics_0.1.0         RSQLite_2.2.8          evaluate_0.14          fastmap_1.1.0          yaml_2.2.1             processx_3.5.2        
 [64] org.Hs.eg.db_3.13.0    knitr_1.36             bit64_4.0.5            fs_1.5.0               caTools_1.18.2         purrr_0.3.4            KEGGREST_1.32.0        KEGGgraph_1.52.0       xml2_1.3.2            
 [73] compiler_4.1.1         rstudioapi_0.13        filelock_1.0.2         curl_4.3.2             png_0.1-7              testthat_3.1.0         tibble_3.1.5           geneplotter_1.70.0     stringi_1.7.5         
 [82] ps_1.6.0               desc_1.4.0             lattice_0.20-45        Matrix_1.3-4           vctrs_0.3.8            pillar_1.6.3           lifecycle_1.0.1        GlobalOptions_0.1.2    bitops_1.0-7          
 [91] R6_2.5.1               KernSmooth_2.23-20     gridExtra_2.3          sessioninfo_1.1.1      codetools_0.2-18       assertthat_0.2.1       pkgload_1.2.2          rprojroot_2.0.2        rjson_0.2.20          
[100] withr_2.4.2            GenomeInfoDbData_1.2.6 hms_1.1.1              grid_4.1.1             rmarkdown_2.11         Cairo_1.5-12.2

DESeq2 • 721 views

ADD COMMENT • link 2.3 years ago walter.eckalbar • 0

score 0 · Answer 1 · 2022-01-13

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Entering edit mode

Michael Love 41k

@mikelove

Last seen 1 day ago

United States

Can you restate this part: "(3DIL - 2BL) / (3DIL - 2BL)", because that equals 1.

If you want the treatment effect in a particular group, that's the interaction of the grouping variable with treatment, as in the "individuals nested within groups" part of the vignette. If you have treatments X, Y, Z and X is the baseline, then the groupA.treatmentZ for example is the Z vs X effect in the A group. You can also compare Z and Y in group A with groupA.treatmentZ - groupA.treatmentY, etc. Then you could use the list-style of contrast as you have above.

Another idea, if you can state your contrasts of interest as comparisons of particular combinations of samples, you could potentially use the contrast CRAN package to figure out what numeric contrasts to use with DESeq2:

high_fat <- contrast(lm_fit_1, 
                     list(diet = "low fat", group = "vehicle"),
                     list(diet = "low fat", group = "treatment"))
print(high_fat, X = TRUE)
#> lm model parameter contrast
#> 
#>  Contrast   S.E.  Lower  Upper     t df Pr(>|t|)
#>    -0.287 0.0738 -0.441 -0.133 -3.89 20    9e-04
#> 
#> Contrast coefficients:
#>  (Intercept) groupvehicle dietlow fat groupvehicle:dietlow fat
#>            0            1           0                        1

See the last line provides a numeric contrast vector, you can provide such numeric vectors to contrast in results().

ADD COMMENT • link 2.3 years ago Michael Love 41k

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Entering edit mode

Hi Michael,

Thanks for looking at this. I miss stated that ratio. Rather it should be (3AC - 2BL) / (3DIL - 2BL).

I'll attempt the contrast option in a moment, but I can't seem to get the syntax right on the subtraction method you show. I've tried:

> res = results(ddsModel,contrast=list("Treatment3AC-Treatment3DIL"))
Error in checkContrast(contrast, resNames) : 
  all elements of the contrast as a list of length 2 should be elements of 'resultsNames(object)'
> res = results(ddsModel,contrast=list(c("Treatment3AC-Treatment3DIL")))
Error in checkContrast(contrast, resNames) : 
  all elements of the contrast as a list of length 2 should be elements of 'resultsNames(object)'
> res = results(ddsModel,contrast=list(c("Treatment3AC",-"Treatment3DIL")))
Error in -"Treatment3DIL" : invalid argument to unary operator
> res = results(ddsModel,contrast=list(c("Treatment3AC","-Treatment3DIL")))
Error in checkContrast(contrast, resNames) : 
  all elements of the contrast as a list of length 2 should be elements of 'resultsNames(object)'

ADD REPLY • link 2.3 years ago walter.eckalbar • 0

0

Entering edit mode

The second list element is in the denominator (e.g. when we write B - A, that's the log scale term for a fold change of B / A and can be extracted from DESeq2 with list("B","A"). If you use list(c("B","A")) this is B + A, which is not what you want.

From docs:

a list of 2 character vectors: the names of the fold changes for the numerator, and the names of the fold changes for the denominator. these names should be elements of resultsNames(object)...