Hi!

I am trying to run Diffbind with DESeq2 on my Cut&Run dataset. I have tried multiple normalization methods in dba.count() and dba.normalize() and am unsure which is the most "fitting" for my data. No matter which method I use the Volcano plot looks something like this (see image). At the bottom is a clustering of hits and the extended arms of the plot seem to show a bin-ing effect. Is there any way to get rid of this? When plotted against p-values and not FDR, the results are the same.

Thank you all in advance,

Here is my code and session:

dba <- dba(sampleSheet=sample_sheet)
dba <- dba.count(dba, score = DBA_SCORE_READS_FOLD, summits = TRUE)
dba <- dba.normalize(dba, method = DBA_DESEQ2, normalize = DBA_NORM_RLE, background=TRUE)
dba <- dba.contrast(dba, reorderMeta=list(Condition="crtl"))
dba <- dba.analyze(dba, method=DBA_DESEQ2, bBlacklist = DBA_BLACKLIST_MM10, bGreylist = "BSgenome.Mmusculus.UCSC.mm10")

# volcano plot
dba.plotVolcano(dba, contrast=1)

sessionInfo( )

R version 4.3.2 (2023-10-31)
Platform: aarch64-apple-darwin20 (64-bit)
Running under: macOS Monterey 12.4

Matrix products: default
BLAS:   /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib 
LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib;  LAPACK version 3.11.0

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/Copenhagen
tzcode source: internal

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

other attached packages:
 [1] BSgenome.Mmusculus.UCSC.mm10_1.4.3       
 [2] BSgenome_1.70.2                          
 [3] rtracklayer_1.62.0                       
 [4] BiocIO_1.12.0                            
 [5] Biostrings_2.70.2                        
 [6] XVector_0.42.0                           
 [7] ggalt_0.4.0                              
 [8] magick_2.8.3                             
 [9] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
[10] org.Mm.eg.db_3.18.0                      
[11] rsq_2.6                                  
[12] EnhancedVolcano_1.20.0                   
[13] ggrepel_0.9.5                            
[14] edgeR_4.0.15                             
[15] limma_3.58.1                             
[16] csaw_1.36.1                              
[17] org.Hs.eg.db_3.18.0                      
[18] TxDb.Mmusculus.UCSC.mm10.ensGene_3.4.0   
[19] GenomicFeatures_1.54.3                   
[20] AnnotationDbi_1.64.1                     
[21] readxl_1.4.3                             
[22] lubridate_1.9.3                          
[23] forcats_1.0.0                            
[24] stringr_1.5.1                            
[25] purrr_1.0.2                              
[26] readr_2.1.5                              
[27] tidyr_1.3.1                              
[28] tibble_3.2.1                             
[29] ggplot2_3.4.4                            
[30] tidyverse_2.0.0                          
[31] ChIPseeker_1.38.0                        
[32] DiffBind_3.12.0                          
[33] dplyr_1.1.4                              
[34] profileplyr_1.18.0                       
[35] SummarizedExperiment_1.32.0              
[36] Biobase_2.62.0                           
[37] GenomicRanges_1.54.1                     
[38] GenomeInfoDb_1.38.6                      
[39] IRanges_2.36.0                           
[40] S4Vectors_0.40.2                         
[41] MatrixGenerics_1.14.0                    
[42] matrixStats_1.2.0                        
[43] BiocGenerics_0.48.1