Issue with CoveragePlot: shows opposite trend from ViolinPlot for a specific peak

[Daliya-K]

Thank you for developing this great package!
I am trying to visualize with CoveragePlot deferentially accessible peaks between condition A and B in scATACseq data. I used Wilcoxon rank sum test with FindMarkers in Seurat to calculate the DA peaks. Strangely, the DA peak upregulated in condition "A" with the lowest p-value has much lower signal in "A" in the Coverage Plot:

DEpeaks<-FindMarkers(seur, ident.1 ="A", ident.2="B",min.cells.group=2,pseudocount.use = 0.01, max.cells.per.ident = 1000)
DEpeaks$peak=row.names(DEpeaks)
closest_genes <- ClosestFeature(seur_full.noart, regions = rownames(DEpeaks))
DEpeaks=merge(DEpeaks, closest_genes, by.x="peak", by.y="query_region",sort=F, all=T)
features.use= head(DEpeaks[DEpeaks$avg_log2FC>6,]$peak,30)
i=1
DefaultAssay(seur)="peaks"
p=CoveragePlot(
object =seur,
region =features.use[i],
region.highlight = StringToGRanges(features.use[i]),
extend.upstream = 1000,
extend.downstream = 1000
)
p

If I plot the counts for this peak, it is clearly more accessible in "A"

Normalized counts:

VlnPlot(seur, features =features.use[i] )

Raw counts:

VlnPlot(seur, features =features.use[i], slot="counts" )

The remaining top 30 up and down DA peaks look more logical on the CoveragePlot. I will be very grateful in any help to understand why is this happening

SessionInfo:

R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS

Matrix products: default
BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.12.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0

locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=de_BE.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=de_BE.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=de_BE.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=de_BE.UTF-8 LC_IDENTIFICATION=C

time zone: Europe/Brussels
tzcode source: system (glibc)

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

other attached packages:
[1] ggrepel_0.9.6 karyoploteR_1.32.0 regioneR_1.38.0 shiny_1.9.1 EnsDb.Mmusculus.v79_2.99.0
[6] ensembldb_2.30.0 AnnotationFilter_1.30.0 GenomicFeatures_1.58.0 AnnotationDbi_1.68.0 Biobase_2.66.0
[11] GenomicRanges_1.58.0 GenomeInfoDb_1.42.1 IRanges_2.40.0 S4Vectors_0.44.0 BiocGenerics_0.52.0
[16] presto_1.0.0 data.table_1.16.2 Rcpp_1.0.13-1 plotly_4.10.4 clustree_0.5.1
[21] ggraph_2.2.1 cowplot_1.1.3 ggplot2_3.5.1 SeuratObject_5.0.2 Seurat_4.3.0
[26] Signac_1.14.0 dplyr_1.1.4

loaded via a namespace (and not attached):
[1] ProtGenerics_1.38.0 matrixStats_1.4.1 spatstat.sparse_3.1-0 bitops_1.0-9 httr_1.4.7
[6] RColorBrewer_1.1-3 tools_4.4.2 sctransform_0.4.1 backports_1.5.0 utf8_1.2.4
[11] R6_2.5.1 lazyeval_0.2.2 uwot_0.2.2 withr_3.0.2 sp_2.1-4
[16] gridExtra_2.3 progressr_0.15.1 cli_3.6.3 textshaping_0.4.0 spatstat.explore_3.3-3
[21] labeling_0.4.3 sass_0.4.9 spatstat.data_3.1-4 ggridges_0.5.6 pbapply_1.7-2
[26] Rsamtools_2.22.0 systemfonts_1.1.0 foreign_0.8-87 dichromat_2.0-0.1 parallelly_1.40.0
[31] BSgenome_1.74.0 limma_3.62.1 rstudioapi_0.17.1 RSQLite_2.3.8 generics_0.1.3
[36] BiocIO_1.16.0 ica_1.0-3 spatstat.random_3.3-2 Matrix_1.7-1 ggbeeswarm_0.7.2
[41] fansi_1.0.6 abind_1.4-8 lifecycle_1.0.4 yaml_2.3.10 SummarizedExperiment_1.36.0
[46] SparseArray_1.6.0 Rtsne_0.17 grid_4.4.2 blob_1.2.4 promises_1.3.2
[51] crayon_1.5.3 miniUI_0.1.1.1 lattice_0.22-5 KEGGREST_1.46.0 pillar_1.9.0
[56] knitr_1.49 rjson_0.2.23 future.apply_1.11.3 codetools_0.2-20 fastmatch_1.1-4
[61] leiden_0.4.3.1 glue_1.8.0 spatstat.univar_3.1-1 vctrs_0.6.5 png_0.1-8
[66] spam_2.11-0 gtable_0.3.6 cachem_1.1.0 xfun_0.49 S4Arrays_1.6.0
[71] mime_0.12 tidygraph_1.3.1 survival_3.7-0 RcppRoll_0.3.1 statmod_1.5.0
[76] fitdistrplus_1.2-1 ROCR_1.0-11 nlme_3.1-166 bit64_4.5.2 RcppAnnoy_0.0.22
[81] bslib_0.8.0 irlba_2.3.5.1 vipor_0.4.7 rpart_4.1.23 KernSmooth_2.23-24
[86] colorspace_2.1-1 DBI_1.2.3 Hmisc_5.2-1 nnet_7.3-19 ggrastr_1.0.2
[91] tidyselect_1.2.1 bit_4.5.0.1 compiler_4.4.2 curl_6.0.1 htmlTable_2.4.3
[96] bezier_1.1.2 DelayedArray_0.32.0 rtracklayer_1.66.0 checkmate_2.3.2 scales_1.3.0
[101] lmtest_0.9-40 stringr_1.5.1 digest_0.6.37 goftest_1.2-3 spatstat.utils_3.1-1
[106] rmarkdown_2.29 XVector_0.46.0 htmltools_0.5.8.1 pkgconfig_2.0.3 base64enc_0.1-3
[111] MatrixGenerics_1.18.0 fastmap_1.2.0 rlang_1.1.4 htmlwidgets_1.6.4 UCSC.utils_1.2.0
[116] farver_2.1.2 jquerylib_0.1.4 zoo_1.8-12 jsonlite_1.8.9 BiocParallel_1.40.0
[121] VariantAnnotation_1.52.0 RCurl_1.98-1.16 magrittr_2.0.3 Formula_1.2-5 GenomeInfoDbData_1.2.13
[126] dotCall64_1.2 patchwork_1.3.0 munsell_0.5.1 bamsignals_1.38.0 viridis_0.6.5
[131] reticulate_1.40.0 stringi_1.8.4 zlibbioc_1.52.0 MASS_7.3-61 plyr_1.8.9
[136] parallel_4.4.2 listenv_0.9.1 deldir_2.0-4 Biostrings_2.74.0 graphlayouts_1.2.1
[141] splines_4.4.2 tensor_1.5 igraph_2.1.2 spatstat.geom_3.3-4 reshape2_1.4.4
[146] pkgload_1.4.0 XML_3.99-0.17 evaluate_1.0.1 biovizBase_1.54.0 BiocManager_1.30.25
[151] tweenr_2.0.3 httpuv_1.6.15 RANN_2.6.2 tidyr_1.3.1 purrr_1.0.2
[156] polyclip_1.10-7 future_1.34.0 scattermore_1.2 ggforce_0.4.2 xtable_1.8-4
[161] restfulr_0.0.15 later_1.4.1 viridisLite_0.4.2 ragg_1.3.3 tibble_3.2.1
[166] beeswarm_0.4.0 memoise_2.0.1 GenomicAlignments_1.42.0 writexl_1.5.1 cluster_2.1.8
[171] globals_0.16.3
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