I have an experiment where we have knocked-down a protein (genotype, as per DESeq2 workflow, comparing siC vs siprotein) and stimulated with a cytokine (condition, as per DESeq2 workflow, comparing interleukin vs unstimulated cells).

I have two questions please:

1) The effect of the cytokine in my siC control cells is the expected one. The effect of cytokine in my knockdown cells is the opposite (we know that it modulates the effects, but some genes should still go up, but should go up less following our previous data). I have ordered the condition and genotype to make the comparisons in the right order, but I still have issues...

2) The code to determine the effects of the knockdown without cytokine: is the below, generating the object res3, correct?

In addition, I have added 'donor' in the design to make paired comparisons (we have 5 biological replicates). I have the same issue if I do not include the pairing. Thank you very much in advance

dds <- DESeqDataSetFromTximport(txi = txi, 
                                colData = df_total, 
                                design = ~ donor + genotype + condition + genotype:condition)
dds$condition <- relevel(dds$condition, ref = "unstim") # to contrast unstimulated vs interleukin stimulated cells
dds$genotype <- relevel(dds$genotype, ref = "siC") # to contrast knockdown control vs protein knockdown


resultsNames(dds) 

"Intercept"                       "donor_2_vs_1"                   
[3] "donor_3_vs_1"                    "donor_4_vs_1"                   
[5] "donor_5_vs_1"                    "genotype_siprotein_vs_siC"      
[7] "condition_interleukin_vs_unstim"        "genotypesiprotein.conditioninterleukin"



results(dds, contrast=c("condition", "unstim", "interleukin")) # to determine the genes that are modulated by the cytokine in siC cells. Our positive controls appear, so great.
results(dds, contrast = list( c("condition_interleukin_vs_unstim","genotypesiprotein.conditioninterleukin"))) # to detect the effect of knockdown on interleukin stimulation. Here, genes that we expect induced (but less induced than in siC control cells)now decrease.
res3 <- results(dds, contrast = c('genotype', 'siC', 'siprotein')) # effect of protein knockdown without cytokine


sessionInfo( )

R version 4.4.1 (2024-06-14)
Platform: x86_64-apple-darwin20
Running under: macOS Ventura 13.6.6

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-x86_64/Resources/lib/libRlapack.dylib;  LAPACK version 3.12.0

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

time zone: Europe/London
tzcode source: internal

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

other attached packages:
 [1] pheatmap_1.0.12             txdbmaker_1.0.1             magrittr_2.0.3             
 [4] DESeq2_1.44.0               SummarizedExperiment_1.34.0 DelayedArray_0.30.1        
 [7] SparseArray_1.4.8           S4Arrays_1.4.1              abind_1.4-5                
[10] MatrixGenerics_1.16.0       matrixStats_1.3.0           Matrix_1.7-0               
[13] GenomicFeatures_1.56.0      AnnotationDbi_1.66.0        Biobase_2.64.0             
[16] GenomicRanges_1.56.1        GenomeInfoDb_1.40.1         IRanges_2.38.1             
[19] S4Vectors_0.42.1            BiocGenerics_0.50.0         lubridate_1.9.3            
[22] forcats_1.0.0               stringr_1.5.1               dplyr_1.1.4                
[25] purrr_1.0.2                 readr_2.1.5                 tidyr_1.3.1                
[28] tibble_3.2.1                ggplot2_3.5.1               tidyverse_2.0.0            
[31] tximport_1.32.0            

loaded via a namespace (and not attached):
 [1] DBI_1.2.3                bitops_1.0-7             httr2_1.0.1             
 [4] biomaRt_2.60.1           rlang_1.1.4              compiler_4.4.1          
 [7] RSQLite_2.3.7            png_0.1-8                vctrs_0.6.5             
[10] pkgconfig_2.0.3          crayon_1.5.3             fastmap_1.2.0           
[13] dbplyr_2.5.0             XVector_0.44.0           labeling_0.4.3          
[16] utf8_1.2.4               Rsamtools_2.20.0         tzdb_0.4.0              
[19] UCSC.utils_1.0.0         bit_4.0.5                zlibbioc_1.50.0         
[22] cachem_1.1.0             jsonlite_1.8.8           progress_1.2.3          
[25] blob_1.2.4               BiocParallel_1.38.0      parallel_4.4.1          
[28] prettyunits_1.2.0        R6_2.5.1                 RColorBrewer_1.1-3      
[31] stringi_1.8.4            rtracklayer_1.64.0       Rcpp_1.0.12             
[34] timechange_0.3.0         tidyselect_1.2.1         rstudioapi_0.16.0       
[37] yaml_2.3.9               codetools_0.2-20         curl_5.2.1              
[40] lattice_0.22-6           withr_3.0.0              KEGGREST_1.44.1         
[43] BiocFileCache_2.12.0     xml2_1.3.6               Biostrings_2.72.1       
[46] pillar_1.9.0             BiocManager_1.30.23      filelock_1.0.3          
[49] generics_0.1.3           vroom_1.6.5              RCurl_1.98-1.14         
[52] hms_1.1.3                munsell_0.5.1            scales_1.3.0            
[55] glue_1.7.0               tools_4.4.1              BiocIO_1.14.0           
[58] locfit_1.5-9.10          GenomicAlignments_1.40.0 XML_3.99-0.17           
[61] grid_4.4.1               colorspace_2.1-0         GenomeInfoDbData_1.2.12 
[64] restfulr_0.0.15          cli_3.6.3                rappdirs_0.3.3          
[67] fansi_1.0.6              gtable_0.3.5             digest_0.6.36           
[70] farver_2.1.2             rjson_0.2.21             memoise_2.0.1           
[73] lifecycle_1.0.4          httr_1.4.7               bit64_4.0.5

In you first contrast you put interleukin in denominator of the LFC, why? Previously you set unstim as reference. See the contrast argument description in ?results.