Hello, I am trying to compare the peaks between 2 conditions to see what peaks belongs to which conditions. I have 15791 peaks where around 521 or 521 peaks are common between 2 conditions(S1 and S3) and 15450 are only for condition1. So I want to see which peaks are for condition S1 only and which are common, but the approach im using is not giving 100% correct results, for some peaks its correct for others it's not. I am working in R studio. Anyone have any idea of other approach or how can I adjust my script

 # Create GRanges objects for S1 and S3 peaks
 gr_S1 <- GRanges(seqnames = S1$seqnames, ranges = IRanges(start = S1$start, end = S1$end))
 gr_S3 <- GRanges(seqnames = S3$seqnames, ranges = IRanges(start = S3$start, end = S3$end))

 # Find overlaps between S1 and S3
 overlap_hits <- findOverlaps(gr_S1, gr_S3)

 # Add a status column to Chip_seq_data (assuming it has the S1 peaks)
 Chip_seq_finale <- Chip_seq_finale %>%
   mutate(status = ifelse(row_number() %in% queryHits(overlap_hits), "common", "S1 only"))

I tried another approach that didn't workout too

# Combine the peak information into a single string for easy comparison
 S3$peak_id <- paste(S3$seqnames, S3$start, S3$end, sep = ":")
 S1$peak_id <- paste(S1$seqnames, S1$start, S1$end, sep = ":")

 # Extract the unique peak_ids from both data frames
 all_peaks <- unique(c(S3$peak_id, S1$peak_id))

 # Identify common peaks
 common_peaks <- intersect(S3$peak_id, S1$peak_id)

 # Create a new data frame
 peak_status <- data.frame(
   peak_id = all_peaks,
   status = ifelse(all_peaks %in% common_peaks, "common", "S1 only")
 )
 # Split the peak_id back into seqnames, start, and end
 peak_status <- cbind(
   peak_status,
   do.call(rbind, strsplit(as.character(peak_status$peak_id), ":"))
 )

 # Rename the new columns appropriately
 colnames(peak_status)[3:5] <- c("seqnames", "start", "end")

 # Convert start and end to numeric
 peak_status$start <- as.numeric(peak_status$start)
 peak_status$end <- as.numeric(peak_status$end)

 # Merge additional information from S3 and S1
 peak_status <- merge(peak_status, S3_chromhmm, by.x = "peak_id", by.y = "peak_id", all.x = TRUE, suffixes = c("", "_S3"))
 peak_status <- merge(peak_status, S1_chromhmm, by.x = "peak_id", by.y = "peak_id", all.x = TRUE, suffixes = c("", "_S1"))

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] grid      stats4    stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] data.table_1.14.2                        reshape2_1.4.4                           TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2 
 [4] ChIPseeker_1.30.3                        ChIPpeakAnno_3.28.0                      msigdbr_7.4.1                           
 [7] ggupset_0.3.0                            DOSE_3.20.1                              RColorBrewer_1.1-3                      
[10] fgsea_1.20.0                             org.Hs.eg.db_3.14.0                      enrichplot_1.14.2                       
[13] clusterProfiler_4.2.2                    pheatmap_1.0.12                          VennDiagram_1.7.3                       
[16] futile.logger_1.4.3                      enrichR_3.2                              rtracklayer_1.54.0                      
[19] TxDb.Hsapiens.UCSC.hg38.knownGene_3.14.0 GenomicFeatures_1.46.1                   AnnotationDbi_1.56.1                    
[22] DiffBind_3.4.11                          BiocParallel_1.28.3                      BiocManager_1.30.25                     
[25] openxlsx_4.2.7.1                         readxl_1.4.3                             EnhancedVolcano_1.13.2                  
[28] ggrepel_0.9.6                            magrittr_2.0.3                           lubridate_1.9.3                         
[31] forcats_1.0.0                            stringr_1.5.1                            dplyr_1.1.4                             
[34] purrr_1.0.2                              readr_2.1.5                              tidyr_1.3.1                             
[37] tibble_3.2.1                             tidyverse_2.0.0                          DESeq2_1.34.0                           
[40] SummarizedExperiment_1.24.0              MatrixGenerics_1.6.0                     matrixStats_0.62.0                      
[43] GenomicRanges_1.46.1                     GenomeInfoDb_1.30.1                      IRanges_2.28.0                          
[46] S4Vectors_0.32.4                         Biobase_2.54.0                           BiocGenerics_0.40.0                     
[49] limma_3.50.3                             ggplot2_3.5.1                           

loaded via a namespace (and not attached):
  [1] rappdirs_0.3.3           coda_0.19-4              bit64_4.0.5              knitr_1.48               irlba_2.3.5.1           
  [6] DelayedArray_0.20.0      hwriter_1.3.2            KEGGREST_1.34.0          RCurl_1.98-1.9           AnnotationFilter_1.18.0 
 [11] generics_0.1.3           lambda.r_1.2.4           RSQLite_2.2.8            shadowtext_0.0.9         bit_4.0.4               
 [16] tzdb_0.2.0               xml2_1.3.3               viridis_0.6.2            amap_0.8-20              apeglm_1.16.0           
 [21] xfun_0.48                hms_1.1.3                babelgene_21.4           evaluate_0.17            fansi_1.0.3             
 [26] restfulr_0.0.13          progress_1.2.2           caTools_1.18.2           dbplyr_2.5.0             igraph_2.0.3            
 [31] DBI_1.2.3                geneplotter_1.72.0       htmlwidgets_1.5.4        annotate_1.72.0          biomaRt_2.50.0          
 [36] vctrs_0.6.5              ensembldb_2.18.2         cachem_1.0.6             withr_2.5.0              ggforce_0.3.3           
 [41] BSgenome_1.62.0          bdsmatrix_1.3-7          GenomicAlignments_1.30.0 treeio_1.18.1            prettyunits_1.1.1       
 [46] ape_5.5                  lazyeval_0.2.2           crayon_1.5.2             genefilter_1.76.0        pkgconfig_2.0.3         
 [51] tweenr_1.0.2             nlme_3.1-153             ProtGenerics_1.26.0      rlang_1.1.4              lifecycle_1.0.3         
 [56] downloader_0.4           filelock_1.0.2           BiocFileCache_2.2.0      invgamma_1.1             cellranger_1.1.0        
 [61] polyclip_1.10-0          graph_1.72.0             Matrix_1.3-4             aplot_0.1.1              ashr_2.2-47             
 [66] boot_1.3-28              png_0.1-7                viridisLite_0.4.1        rjson_0.2.20             bitops_1.0-7            
 [71] KernSmooth_2.23-20       Biostrings_2.62.0        blob_1.2.2               mixsqp_0.3-43            SQUAREM_2021.1          
 [76] qvalue_2.26.0            regioneR_1.26.0          ShortRead_1.52.0         jpeg_0.1-9               gridGraphics_0.5-1      
 [81] scales_1.3.0             memoise_2.0.1            plyr_1.8.7               gplots_3.2.0             zlibbioc_1.40.0         
 [86] compiler_4.1.1           scatterpie_0.1.7         BiocIO_1.4.0             bbmle_1.0.24             plotrix_3.8-2           
 [91] Rsamtools_2.10.0         cli_3.6.3                systemPipeR_2.0.4        XVector_0.34.0           patchwork_1.1.1         
 [96] formatR_1.11             MASS_7.3-54              tidyselect_1.2.1         stringi_1.7.8            emdbook_1.3.12          
[101] yaml_2.3.6               GOSemSim_2.20.0          locfit_1.5-9.4           latticeExtra_0.6-29      fastmatch_1.1-3         
[106] tools_4.1.1              timechange_0.3.0         parallel_4.1.1           rstudioapi_0.17.0        gridExtra_2.3           
[111] farver_2.1.1             ggraph_2.0.5             digest_0.6.30            Rcpp_1.0.9               WriteXLS_6.7.0          
[116] httr_1.4.7               colorspace_2.0-3         XML_3.99-0.9             truncnorm_1.0-8          splines_4.1.1           
[121] yulab.utils_0.0.4        RBGL_1.70.0              tidytree_0.3.6           graphlayouts_0.8.0       multtest_2.50.0         
[126] ggplotify_0.1.0          xtable_1.8-4             jsonlite_1.8.9           ggtree_3.2.1             futile.options_1.0.1    
[131] tidygraph_1.2.0          ggfun_0.0.4              R6_2.5.1                 pillar_1.9.0             htmltools_0.5.2         
[136] glue_1.6.2               fastmap_1.1.0            GreyListChIP_1.26.0      mvtnorm_1.1-3            utf8_1.2.2              
[141] lattice_0.20-45          numDeriv_2016.8-1.1      curl_4.3.3               gtools_3.9.3             zip_2.2.0               
[146] GO.db_3.14.0             survival_3.2-13          rmarkdown_2.27           InteractionSet_1.22.0    munsell_0.5.0           
[151] DO.db_2.9                GenomeInfoDbData_1.2.7   gtable_0.3.1    
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