Hello everyone, I have a simple question; I am doing a DiffBind analysis and using dba.contrast function what's the difference if we do in diffBind contrast ie: WT vs Control and Control vs WT? normally there should not be any differences but when i did it, i saw a differences in Fold and even in the number of sites. Thank u!

sessionInfo( )

sessionInfo() R version 4.1.1 (2021-08-10) Platform: x86_64-conda-linux-gnu (64-bit) Running under: Ubuntu 20.04.6 LTS

Matrix products: default BLAS/LAPACK: /shared/ifbstor1/software/miniconda/envs/r-4.1.1/lib/libopenblasp-r0.3.18.so

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

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

other attached packages: [1] rtracklayer_1.54.0 TxDb.Hsapiens.UCSC.hg38.knownGene_3.14.0 [3] GenomicFeatures_1.46.1 AnnotationDbi_1.56.1
[5] DiffBind_3.4.11 BiocParallel_1.28.3
[7] BiocManager_1.30.23 openxlsx_4.2.5.2
[9] readxl_1.4.3 EnhancedVolcano_1.13.2
[11] ggrepel_0.9.1 magrittr_2.0.3
[13] lubridate_1.9.3 forcats_1.0.0
[15] stringr_1.5.1 dplyr_1.1.4
[17] purrr_1.0.2 readr_2.1.5
[19] tidyr_1.3.1 tibble_3.2.1
[21] tidyverse_2.0.0 DESeq2_1.34.0
[23] SummarizedExperiment_1.24.0 MatrixGenerics_1.6.0
[25] matrixStats_0.62.0 GenomicRanges_1.46.1
[27] GenomeInfoDb_1.30.1 IRanges_2.28.0
[29] S4Vectors_0.32.4 Biobase_2.54.0
[31] BiocGenerics_0.40.0 limma_3.50.3
[33] ggplot2_3.5.1

loaded via a namespace (and not attached): [1] amap_0.8-19 colorspace_2.0-3 rjson_0.2.20 hwriter_1.3.2
[5] XVector_0.34.0 rstudioapi_0.16.0 bit64_4.0.5 fansi_1.0.3
[9] mvtnorm_1.1-3 apeglm_1.16.0 xml2_1.3.3 splines_4.1.1
[13] cachem_1.0.6 geneplotter_1.72.0 Rsamtools_2.10.0 annotate_1.72.0
[17] dbplyr_2.1.1 ashr_2.2-47 png_0.1-7 GreyListChIP_1.26.0
[21] compiler_4.1.1 httr_1.4.4 assertthat_0.2.1 Matrix_1.3-4
[25] fastmap_1.1.0 cli_3.6.2 prettyunits_1.1.1 htmltools_0.5.2
[29] tools_4.1.1 coda_0.19-4 gtable_0.3.1 glue_1.6.2
[33] GenomeInfoDbData_1.2.7 systemPipeR_2.0.4 rappdirs_0.3.3 ShortRead_1.52.0
[37] Rcpp_1.0.9 bbmle_1.0.24 cellranger_1.1.0 vctrs_0.6.5
[41] Biostrings_2.62.0 irlba_2.3.5.1 timechange_0.3.0 lifecycle_1.0.3
[45] restfulr_0.0.13 gtools_3.9.3 XML_3.99-0.9 edgeR_3.36.0
[49] zlibbioc_1.40.0 MASS_7.3-54 scales_1.3.0 BSgenome_1.62.0
[53] hms_1.1.3 parallel_4.1.1 RColorBrewer_1.1-3 curl_4.3.3
[57] yaml_2.3.6 memoise_2.0.1 emdbook_1.3.12 biomaRt_2.50.0
[61] bdsmatrix_1.3-7 latticeExtra_0.6-29 stringi_1.7.8 RSQLite_2.2.8
[65] SQUAREM_2021.1 genefilter_1.76.0 BiocIO_1.4.0 filelock_1.0.2
[69] caTools_1.18.2 zip_2.2.0 truncnorm_1.0-8 rlang_1.1.3
[73] pkgconfig_2.0.3 bitops_1.0-7 lattice_0.20-45 invgamma_1.1
[77] GenomicAlignments_1.30.0 htmlwidgets_1.5.4 bit_4.0.4 tidyselect_1.2.1
[81] plyr_1.8.7 R6_2.5.1 gplots_3.1.3.1 generics_0.1.3
[85] DelayedArray_0.20.0 DBI_1.1.2 pillar_1.9.0 withr_2.5.0
[89] mixsqp_0.3-43 survival_3.2-13 KEGGREST_1.34.0 RCurl_1.98-1.9
[93] crayon_1.5.2 KernSmooth_2.23-20 utf8_1.2.2 BiocFileCache_2.2.0
[97] tzdb_0.2.0 progress_1.2.2 jpeg_0.1-9 locfit_1.5-9.4
[101] grid_4.1.1 blob_1.2.2 digest_0.6.30 xtable_1.8-4
[105] numDeriv_2016.8-1.1 munsell_0.5.0
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