Hi all,

I have control (C) and disease (D) groups, and the status of a mutation in each sample as absent (0), heterozygous (1), and homozygous (2) so that my design matrix looks like this:

                C.0     C.1     C.2     D.0       D.1       D.2
sample.1        1       0       0       0         0         0
sample.2        1       0       0       0         0         0
sample.3        1       0       0       0         0         0
sample.4        0       1       0       0         0         0
sample.5        0       0       1       0         0         0
sample.6        0       0       1       0         0         0
sample.7        0       0       0       1         0         0
sample.8        0       0       0       1         0         0
sample.9        0       0       0       0         1         0
sample.10       0       0       0       0         1         0
sample.11       0       0       0       0         0         1
sample.12       0       0       0       0         0         1

Is there a way to format my contrasts argument to allow a comparison between D.1 and all C groups? I have tried the following, which was wrong:

qlf <- glmQLFTest(fit,contrast=c(-1,-1,-1,0,1,0))

A more correct answer (but still not correct) is the following:

qlf <- glmQLFTest(fit,contrast=c(-0.33,-0.33,-0.33,0,1,0))

Is there a simple way of writing what I want or am I better off collapsing C.0, C.1, and C.2 into a single control group and comparing D.1 vs C? Thank you.

sessionInfo( )
R version 4.1.1 (2021-08-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Ubuntu 20.04.3 LTS

Matrix products: default
BLAS/LAPACK: /opt/conda/lib/libopenblasp-r0.3.17.so

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [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] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] EnhancedVolcano_1.10.0 ggrepel_0.9.1          edgeR_3.34.1          
 [4] limma_3.48.3           BiocManager_1.30.16    ggthemes_4.2.4        
 [7] gorr_1.4.4             forcats_0.5.1          stringr_1.4.0         
[10] dplyr_1.0.7            purrr_0.3.4            readr_2.0.1           
[13] tidyr_1.1.3            tibble_3.1.4           ggplot2_3.3.5         
[16] tidyverse_1.3.1       

loaded via a namespace (and not attached):
 [1] fs_1.5.0           lubridate_1.7.10   bit64_4.0.5        ash_1.0-15        
 [5] RColorBrewer_1.1-2 httr_1.4.2         repr_1.1.3         tools_4.1.1       
 [9] backports_1.2.1    utf8_1.2.2         R6_2.5.1           KernSmooth_2.23-20
[13] vipor_0.4.5        DBI_1.1.1          colorspace_2.0-2   withr_2.4.2       
[17] tidyselect_1.1.1   ggrastr_0.2.3      ggalt_0.4.0        bit_4.0.4         
[21] curl_4.3.2         compiler_4.1.1     extrafontdb_1.0    cli_3.0.1         
[25] rvest_1.0.1        Cairo_1.5-12.2     xml2_1.3.2         labeling_0.4.2    
[29] scales_1.1.1       proj4_1.0-10.1     askpass_1.1        pbdZMQ_0.3-5      
[33] digest_0.6.28      base64enc_0.1-3    pkgconfig_2.0.3    htmltools_0.5.2   
[37] extrafont_0.17     dbplyr_2.1.1       fastmap_1.1.0      maps_3.4.0        
[41] rlang_0.4.11       readxl_1.3.1       rstudioapi_0.13    farver_2.1.0      
[45] generics_0.1.0     jsonlite_1.7.2     vroom_1.5.5        magrittr_2.0.1    
[49] Rcpp_1.0.7         ggbeeswarm_0.6.0   IRkernel_1.2       munsell_0.5.0     
[53] fansi_0.4.2        lifecycle_1.0.1    stringi_1.7.4      MASS_7.3-54       
[57] grid_4.1.1         parallel_4.1.1     crayon_1.4.1       lattice_0.20-45   
[61] splines_4.1.1      IRdisplay_1.0      haven_2.4.3        hms_1.1.1         
[65] locfit_1.5-9.4     pillar_1.6.3       uuid_0.1-4         reprex_2.0.1      
[69] glue_1.4.2         evaluate_0.14      modelr_0.1.8       vctrs_0.3.8       
[73] tzdb_0.1.2         Rttf2pt1_1.3.9     cellranger_1.1.0   gtable_0.3.0      
[77] openssl_1.4.5      assertthat_0.2.1   broom_0.7.9        beeswarm_0.4.0    
[81] ellipsis_0.3.2

Why do you say that the second contrast is "still not correct"? It seems the obvious way to make the comparison you want.

You can collapse C.0, C.1 and C.2 into one group if mutation status has no effect in the controls, otherwise not. Collapsing into one group is not actually wrong but it will be more conservative than the contrast if there is genuine DE between C.0, C.1 and C.2.