Hi,

I am having some trouble in retrieving the results of a Differential Analysis using Diffbind. In particular, when I use the function

dba,report()

with the option bflip=TRUE or FALSE I do not see any change in the output in the field of the fold. I see a change of the column order regarding group 1 or 2 however not the fold in sense of direction of change. I can understand the values of Concentration Group 1 or 2, but not the fold, because I cannot reproduce using Group1 - Group2

as an example I write down one line of the table

Conc_a_POS Conc_NEG Fold 1,649371384 9,22688822 6,20142525

from those values: 0 5,76 3,65 # group 1

Thanks a lot if you can help me to solve

Michela

report.analysis.deseq2.df <- 
dba.report(lista.contrasti.conts.contrasts.analysis$a,method = DBA_DESEQ2,bUsePval = TRUE,th=1,fold = 0,bCounts = TRUE,bAll = TRUE,bFlip = TRUE,bNormalized = TRUE,DataType = DBA_DATA_FRAME)

sessionInfo()
R version 4.0.3 (2020-10-10)
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] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8

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

other attached packages:
 [1] openxlsx_4.2.4              DiffBind_3.0.15            
 [3] SummarizedExperiment_1.20.0 Biobase_2.50.0             
 [5] MatrixGenerics_1.2.1        matrixStats_0.58.0         
 [7] GenomicRanges_1.42.0        GenomeInfoDb_1.26.7        
 [9] IRanges_2.24.1              S4Vectors_0.28.1           
[11] BiocGenerics_0.36.0        

loaded via a namespace (and not attached):
  [1] backports_1.2.1          GOstats_2.56.0          
  [3] BiocFileCache_1.14.0     plyr_1.8.6              
  [5] GSEABase_1.52.1          splines_4.0.3           
  [7] BiocParallel_1.24.1      ggplot2_3.3.4           
  [9] amap_0.8-18              digest_0.6.27           
 [11] invgamma_1.1             GO.db_3.12.1            
 [13] SQUAREM_2021.1           fansi_0.4.2             
 [15] magrittr_2.0.1           checkmate_2.0.0         
 [17] memoise_2.0.0            BSgenome_1.58.0         
 [19] base64url_1.4            limma_3.46.0            
 [21] Biostrings_2.58.0        annotate_1.68.0         
 [23] systemPipeR_1.24.3       askpass_1.1             
 [25] bdsmatrix_1.3-4          prettyunits_1.1.1       
 [27] jpeg_0.1-8.1             colorspace_2.0-0        
 [29] blob_1.2.1               rappdirs_0.3.3          
 [31] apeglm_1.12.0            ggrepel_0.9.1           
 [33] xfun_0.22                dplyr_1.0.5             
 [35] crayon_1.4.1             RCurl_1.98-1.3          
 [37] jsonlite_1.7.2           graph_1.68.0            
 [39] genefilter_1.72.1        brew_1.0-6              
 [41] survival_3.2-10          VariantAnnotation_1.36.0
 [43] glue_1.4.2               gtable_0.3.0            
 [45] zlibbioc_1.36.0          XVector_0.30.0          
 [47] DelayedArray_0.16.3      V8_3.4.0                
 [49] Rgraphviz_2.34.0         scales_1.1.1            
 [51] pheatmap_1.0.12          mvtnorm_1.1-1           
 [53] DBI_1.1.1                edgeR_3.32.1            
 [55] Rcpp_1.0.6               xtable_1.8-4            
 [57] progress_1.2.2           emdbook_1.3.12          
 [59] bit_4.0.4                rsvg_2.1                
 [61] AnnotationForge_1.32.0   truncnorm_1.0-8         
 [63] httr_1.4.2               gplots_3.1.1            
 [65] RColorBrewer_1.1-2       ellipsis_0.3.1          
 [67] pkgconfig_2.0.3          XML_3.99-0.6            
 [69] dbplyr_2.1.1             locfit_1.5-9.4          
 [71] utf8_1.2.1               tidyselect_1.1.0        
 [73] rlang_0.4.10             AnnotationDbi_1.52.0    
 [75] munsell_0.5.0            tools_4.0.3             
 [77] cachem_1.0.4             generics_0.1.0          
 [79] RSQLite_2.2.6            stringr_1.4.0           
 [81] fastmap_1.1.0            yaml_2.2.1              
 [83] knitr_1.33               bit64_4.0.5             
 [85] zip_2.1.1                caTools_1.18.2          
 [87] purrr_0.3.4              RBGL_1.66.0             
 [89] xml2_1.3.2               biomaRt_2.46.3          
 [91] compiler_4.0.3           rstudioapi_0.13         
 [93] curl_4.3                 png_0.1-8               
 [95] geneplotter_1.68.0       tibble_3.1.0            
 [97] stringi_1.5.3            GenomicFeatures_1.42.3  
 [99] lattice_0.20-41          Matrix_1.3-2            
[101] vctrs_0.3.7              pillar_1.6.0            
[103] lifecycle_1.0.0          irlba_2.3.3             
[105] data.table_1.14.0        bitops_1.0-6            
[107] rtracklayer_1.50.0       R6_2.5.0                
[109] latticeExtra_0.6-30      hwriter_1.3.2           
[111] ShortRead_1.48.0         KernSmooth_2.23-18      
[113] MASS_7.3-53.1            gtools_3.8.2            
[115] assertthat_0.2.1         DESeq2_1.30.1           
[117] openssl_1.4.3            Category_2.56.0         
[119] rjson_0.2.20             withr_2.4.1             
[121] GenomicAlignments_1.26.0 batchtools_0.9.15       
[123] Rsamtools_2.6.0          GenomeInfoDbData_1.2.4  
[125] hms_1.0.0                grid_4.0.3              
[127] DOT_0.1                  coda_0.19-4             
[129] GreyListChIP_1.22.0      ashr_2.2-47             
[131] mixsqp_0.3-43            bbmle_1.0.23.1          
[133] numDeriv_2020.2-1

This was previously reported as a bug and fixed in version 3.2.4, so it works in the latest version of Bioconductor.

There is a workaround for version 3.0.x, which is to set the baseline for the contrast using the reorderMeta parameter of dba.contrast(). This is actually better practice -- bFlip is somewhat obsolete since version 3.0 enabled arbitrary design formulae and control of the ordering of factor values.