What is the difference between this two R codes, DESMatrix.dse being a DESeqDataSetFromMatrix with multiple 64 conditions including replicates:

    plotMA(results(DESMatrix.dse, contrast=c("condition","treatedC","untreated")), ylim=c(-2,2))

# it gives the following plot :

and creating a sub matrix with only my two conditions

deseq2NI18vsUn.dse <- DESMatrix.dse[ , DESMatrix.dse@colData@listData$condition %in% c("treatedC","untreated" ]
deseq2NI18vsUn.res$condition

gives as expected: 1 untreated treatedC untreated treatedC [5] treatedC treatedC untreated

#then extracting the results to do the plot
res <- results(deseq2NI18vsUn.dse)
plotMA(res, ylim=c(-2,2))

This strangly looks equivalent to a plot over all the data obtained by:

plotMA(DESMatrix.dse, ylim=c(-2,2))

Thus is my submatrix deseq2NI18vsUn.dse really a subset of DESMatrix.dse ? If one can explain me why I do not get the same thing as I expected. Hope, I have given all the elements to understand the situation. Thanks for taking time to look to this.

Here are my R conditions:

sessionInfo( )

R version 4.0.4 (2021-02-15)
Platform: x86_64-apple-darwin17.0 (64-bit)
Running under: macOS Mojave 10.14.6

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.0/Resources/lib/libRlapack.dylib

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

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

other attached packages:
 [1] apeglm_1.12.0               kableExtra_1.3.4            org.Dm.eg.db_3.12.0         GOstats_2.56.0             
 [5] graph_1.68.0                Category_2.56.0             Matrix_1.3-2                AnnotationDbi_1.52.0       
 [9] ReportingTools_2.30.2       knitr_1.32                  RColorBrewer_1.1-2          gplots_3.1.1               
[13] ggplot2_3.3.3               dplyr_1.0.5                 DESeq2_1.30.1               SummarizedExperiment_1.20.0
[17] MatrixGenerics_1.2.1        matrixStats_0.58.0          GenomicRanges_1.42.0        GenomeInfoDb_1.26.7        
[21] IRanges_2.24.1              S4Vectors_0.28.1            Biobase_2.50.0              BiocGenerics_0.36.0        

loaded via a namespace (and not attached):
  [1] backports_1.2.1          Hmisc_4.5-0              systemfonts_1.0.1        BiocFileCache_1.14.0    
  [5] plyr_1.8.6               lazyeval_0.2.2           GSEABase_1.52.1          splines_4.0.4           
  [9] BiocParallel_1.24.1      digest_0.6.27            ensembldb_2.14.0         htmltools_0.5.1.1       
 [13] GO.db_3.12.1             fansi_0.4.2              magrittr_2.0.1           checkmate_2.0.0         
 [17] memoise_2.0.0            BSgenome_1.58.0          cluster_2.1.1            limma_3.46.0            
 [21] Biostrings_2.58.0        annotate_1.68.0          R.utils_2.10.1           svglite_2.0.0           
 [25] ggbio_1.38.0             bdsmatrix_1.3-4          askpass_1.1              prettyunits_1.1.1       
 [29] jpeg_0.1-8.1             colorspace_2.0-0         rvest_1.0.0              blob_1.2.1              
 [33] rappdirs_0.3.3           xfun_0.22                jsonlite_1.7.2           crayon_1.4.1            
 [37] RCurl_1.98-1.3           genefilter_1.72.1        survival_3.2-10          VariantAnnotation_1.36.0
 [41] glue_1.4.2               gtable_0.3.0             zlibbioc_1.36.0          XVector_0.30.0          
 [45] webshot_0.5.2            DelayedArray_0.16.3      Rgraphviz_2.34.0         scales_1.1.1            
 [49] mvtnorm_1.1-1            DBI_1.1.1                GGally_2.1.1             edgeR_3.32.1            
 [53] Rcpp_1.0.6               emdbook_1.3.12           viridisLite_0.4.0        xtable_1.8-4            
 [57] progress_1.2.2           htmlTable_2.1.0          foreign_0.8-81           bit_4.0.4               
 [61] OrganismDbi_1.32.0       Formula_1.2-4            AnnotationForge_1.32.0   htmlwidgets_1.5.3       
 [65] httr_1.4.2               ellipsis_0.3.1           pkgconfig_2.0.3          reshape_0.8.8           
 [69] XML_3.99-0.6             R.methodsS3_1.8.1        sass_0.3.1               nnet_7.3-15             
 [73] dbplyr_2.1.1             locfit_1.5-9.4           utf8_1.2.1               tidyselect_1.1.0        
 [77] rlang_0.4.10             reshape2_1.4.4           munsell_0.5.0            tools_4.0.4             
 [81] cachem_1.0.4             generics_0.1.0           RSQLite_2.2.6            evaluate_0.14           
 [85] stringr_1.4.0            fastmap_1.1.0            yaml_2.2.1               bit64_4.0.5             
 [89] caTools_1.18.2           purrr_0.3.4              AnnotationFilter_1.14.0  RBGL_1.66.0             
 [93] R.oo_1.24.0              xml2_1.3.2               biomaRt_2.46.3           compiler_4.0.4          
 [97] rstudioapi_0.13          curl_4.3                 png_0.1-7                PFAM.db_3.12.0          
[101] tibble_3.1.0             geneplotter_1.68.0       bslib_0.2.4              stringi_1.5.3           
[105] highr_0.8                GenomicFeatures_1.42.3   lattice_0.20-41          ProtGenerics_1.22.0     
[109] vctrs_0.3.7              pillar_1.6.0             lifecycle_1.0.0          BiocManager_1.30.12     
[113] jquerylib_0.1.3          data.table_1.14.0        bitops_1.0-6             rtracklayer_1.50.0      
[117] hwriter_1.3.2            R6_2.5.0                 latticeExtra_0.6-29      KernSmooth_2.23-18      
[121] gridExtra_2.3            dichromat_2.0-0          MASS_7.3-53.1            gtools_3.8.2            
[125] assertthat_0.2.1         openssl_1.4.3            withr_2.4.1              GenomicAlignments_1.26.0
[129] Rsamtools_2.6.0          GenomeInfoDbData_1.2.4   hms_1.0.0                grid_4.0.4              
[133] rpart_4.1-15             coda_0.19-4              rmarkdown_2.7            biovizBase_1.38.0       
[137] bbmle_1.0.23.1           numDeriv_2016.8-1.1      base64enc_0.1-3

The difference is that you are specifying contrast in one vs not specifying in the other. See the vignette or ?results on what happens when you don't specify which contrast to build a results table for.

Also note that dds$condition is an easy way to pull a column from colData of dds.