I want to run an analysis with minOverlap=1 (all peaks to be included in the consensus peakset). However, dba.blacklist changes the consensus peakset to be based on minOverlap=2. How to run dba.blacklist without changing minOverlap?

dbObj_blacklist <- dba.blacklist(dbObj_initial, blacklist = blacklist_granges,  greylist = FALSE)
dbObj_initial$minOverlap
dbObj_blacklist$minOverlap

sessionInfo()

Output :

Applying blacklist...
Removed: 34 of 5210 intervals.
Removed: 28 merged (of 4205) and 3444 (of 4205) consensus.
[1] 1
[1] 2




R version 4.3.0 (2023-04-21)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.6 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_IL.UTF-8       LC_NUMERIC=C               LC_TIME=en_IL.UTF-8       
 [4] LC_COLLATE=en_IL.UTF-8     LC_MONETARY=en_IL.UTF-8    LC_MESSAGES=en_IL.UTF-8   
 [7] LC_PAPER=en_IL.UTF-8       LC_NAME=C                  LC_ADDRESS=C              
[10] LC_TELEPHONE=C             LC_MEASUREMENT=en_IL.UTF-8 LC_IDENTIFICATION=C       

time zone: Asia/Jerusalem
tzcode source: system (glibc)

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

other attached packages:
 [1] DiffBind_3.10.1             rgl_1.2.1                   limma_3.56.2               
 [4] DESeq2_1.40.1               SummarizedExperiment_1.30.2 Biobase_2.60.0             
 [7] MatrixGenerics_1.12.2       matrixStats_1.0.0           GenomicRanges_1.52.0       
[10] GenomeInfoDb_1.36.0         IRanges_2.34.0              S4Vectors_0.38.1           
[13] BiocGenerics_0.46.0        

loaded via a namespace (and not attached):
 [1] bitops_1.0-7             deldir_1.0-9             rlang_1.1.1             
 [4] magrittr_2.0.3           compiler_4.3.0           png_0.1-8               
 [7] vctrs_0.6.3              stringr_1.5.0            pkgconfig_2.0.3         
[10] crayon_1.5.2             fastmap_1.1.1            XVector_0.40.0          
[13] caTools_1.18.2           utf8_1.2.3               Rsamtools_2.16.0        
[16] rmarkdown_2.22           xfun_0.39                cachem_1.0.8            
[19] zlibbioc_1.46.0          jsonlite_1.8.5           DelayedArray_0.26.3     
[22] BiocParallel_1.34.2      jpeg_0.1-10              irlba_2.3.5.1           
[25] parallel_4.3.0           R6_2.5.1                 bslib_0.5.0             
[28] stringi_1.7.12           RColorBrewer_1.1-3       SQUAREM_2021.1          
[31] rtracklayer_1.60.0       jquerylib_0.1.4          numDeriv_2016.8-1.1     
[34] Rcpp_1.0.10              knitr_1.43               base64enc_0.1-3         
[37] Matrix_1.5-4.1           tidyselect_1.2.0         rstudioapi_0.14         
[40] yaml_2.3.7               gplots_3.1.3             codetools_0.2-19        
[43] hwriter_1.3.2.1          lattice_0.21-8           tibble_3.2.1            
[46] plyr_1.8.8               ShortRead_1.58.0         coda_0.19-4             
[49] evaluate_0.21            Biostrings_2.68.1        pillar_1.9.0            
[52] KernSmooth_2.23-21       generics_0.1.3           invgamma_1.1            
[55] RCurl_1.98-1.12          truncnorm_1.0-9          emdbook_1.3.12          
[58] ggplot2_3.4.2            munsell_0.5.0            scales_1.2.1            
[61] ashr_2.2-54              gtools_3.9.4             glue_1.6.2              
[64] pheatmap_1.0.12          tools_4.3.0              apeglm_1.22.1           
[67] interp_1.1-4             BiocIO_1.10.0            BSgenome_1.68.0         
[70] locfit_1.5-9.8           GenomicAlignments_1.36.0 systemPipeR_2.6.1       
[73] XML_3.99-0.14            mvtnorm_1.2-2            grid_4.3.0              
[76] bbmle_1.0.25             amap_0.8-19              bdsmatrix_1.3-6         
[79] latticeExtra_0.6-30      colorspace_2.1-0         GenomeInfoDbData_1.2.10 
[82] restfulr_0.0.15          cli_3.6.1                GreyListChIP_1.32.0     
[85] fansi_1.0.4              mixsqp_0.3-48            S4Arrays_1.0.4          
[88] dplyr_1.1.2              gtable_0.3.3             sass_0.4.6              
[91] digest_0.6.31            ggrepel_0.9.3            rjson_0.2.21            
[94] htmlwidgets_1.6.2        htmltools_0.5.5          lifecycle_1.0.3         
[97] MASS_7.3-60

I have found a workaround : running dba.blacklist after running dba.count (while running dba.count with minOverlap=1).

dbObj_count <- dba.count(dbObj_initial, minOverlap = 1)
dbObj_blacklist <- dba.blacklist(dbObj_count, blacklist = blacklist_granges,  greylist = FALSE)

Applying blacklist...
Removed: 28 of 4189 intervals.
Removed 28 (of 4189) consensus peaks.

Technical note : for some reason dba.blacklist still changes the minOverlap parameter to two, even when running it like this, but the consensus peaks remain to be calculated on the basis of minOverlap=1. Which means that the end result is satisfying.