Hi, thanks for a great tool! I have encountered a strange issue where the CoveragePlot is completely blank for a certain region (in this case all of chromosome 9), despite there being fragment counts in the range I am trying to plot. Since this issue doesn't occur with every dataset, it's difficult to provide a reproducible example, but I have tried to describe the problem as well as I can:
If I try to plot coverage over the peak with the highest number of total counts on chromosome 9, I get an error:
c9_peaks <- sobj[['peaks']]@counts[grep('chr9', rownames(sobj[['peaks']])),]
rsums_9 <- apply(X = c9_peaks, 1, sum)
CoveragePlot(sobj, names(rsums_9[which.max(rsums_9)]), extend.upstream = 10000, extend.downstream = 10000)
Error in `colnames<-`(`*tmp*`, value = start(x = region):end(x = region)) :
attempt to set 'colnames' on an object with less than two dimensions
The region in question does contain fragments:
And doing the same thing for another chromosome works fine:
c18_peaks <- sobj[['peaks']]@counts[grep('chr18', rownames(sobj[['peaks']])),]
rsums_18 <- apply(X = c18_peaks, 1, sum)
CoveragePlot(sobj, names(rsums_18[which.max(rsums_18)]), extend.upstream = 10000, extend.downstream = 10000)
If I merge cells from this object with another dataset I have, the fragments from the other samples plot without issue, but the aforementioned object is still blank, despite the fact that there clearly are fragments present in the region.
CoveragePlot(merge, names(rsums_9[which.max(rsums_9)]), extend.upstream = 10000, extend.downstream = 10000)
What could be causing this? Would really appreciate any insight.
> sessionInfo()
R version 4.4.0 (2024-04-24)
Platform: aarch64-apple-darwin20
Running under: macOS 15.7.3
Matrix products: default
BLAS: /System/Library/Frameworks/Accelerate.framework/Versions/A/Frameworks/vecLib.framework/Versions/A/libBLAS.dylib
LAPACK: /Library/Frameworks/R.framework/Versions/4.4-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.12.0
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/Stockholm
tzcode source: internal
attached base packages:
[1] stats graphics grDevices utils datasets methods base
other attached packages:
[1] Signac_1.16.9004 Seurat_5.3.0 SeuratObject_5.1.0 sp_2.1-4
loaded via a namespace (and not attached):
[1] RColorBrewer_1.1-3 rstudioapi_0.16.0 jsonlite_1.8.8 magrittr_2.0.3 spatstat.utils_3.1-3
[6] farver_2.1.2 zlibbioc_1.50.0 vctrs_0.6.5 ROCR_1.0-11 spatstat.explore_3.2-7
[11] Rsamtools_2.20.0 RcppRoll_0.3.1 htmltools_0.5.8.1 sctransform_0.4.1 parallelly_1.37.1
[16] KernSmooth_2.23-24 htmlwidgets_1.6.4 ica_1.0-3 plyr_1.8.9 plotly_4.10.4
[21] zoo_1.8-12 igraph_2.0.3 mime_0.12 lifecycle_1.0.4 pkgconfig_2.0.3
[26] Matrix_1.7-0 R6_2.5.1 fastmap_1.2.0 MatrixGenerics_1.16.0 GenomeInfoDbData_1.2.12
[31] fitdistrplus_1.1-11 future_1.33.2 shiny_1.8.1.1 digest_0.6.35 colorspace_2.1-0
[36] patchwork_1.2.0 S4Vectors_0.42.0 tensor_1.5 RSpectra_0.16-1 irlba_2.3.5.1
[41] GenomicRanges_1.56.0 labeling_0.4.3 progressr_0.14.0 fansi_1.0.6 spatstat.sparse_3.0-3
[46] httr_1.4.7 polyclip_1.10-6 abind_1.4-5 compiler_4.4.0 withr_3.0.0
[51] BiocParallel_1.38.0 fastDummies_1.7.3 MASS_7.3-61 tools_4.4.0 lmtest_0.9-40
[56] httpuv_1.6.15 future.apply_1.11.2 goftest_1.2-3 glue_1.7.0 nlme_3.1-165
[61] promises_1.3.0 grid_4.4.0 Rtsne_0.17 cluster_2.1.6 reshape2_1.4.4
[66] generics_0.1.3 gtable_0.3.5 spatstat.data_3.0-4 tidyr_1.3.1 data.table_1.15.4
[71] utf8_1.2.4 XVector_0.44.0 BiocGenerics_0.50.0 spatstat.geom_3.2-9 RcppAnnoy_0.0.22
[76] ggrepel_0.9.5 RANN_2.6.1 pillar_1.9.0 stringr_1.5.1 spam_2.10-0
[81] RcppHNSW_0.6.0 later_1.3.2 splines_4.4.0 dplyr_1.1.4 lattice_0.22-6
[86] survival_3.7-0 deldir_2.0-4 tidyselect_1.2.1 Biostrings_2.72.1 miniUI_0.1.1.1
[91] pbapply_1.7-2 gridExtra_2.3 IRanges_2.38.0 scattermore_1.2 stats4_4.4.0
[96] matrixStats_1.3.0 stringi_1.8.4 UCSC.utils_1.0.0 lazyeval_0.2.2 codetools_0.2-20
[101] tibble_3.2.1 cli_3.6.2 uwot_0.2.2 xtable_1.8-4 reticulate_1.37.0
[106] munsell_0.5.1 Rcpp_1.0.12 GenomeInfoDb_1.40.1 globals_0.16.3 spatstat.random_3.2-3
[111] png_0.1-8 parallel_4.4.0 ggplot2_3.5.1 dotCall64_1.1-1 sparseMatrixStats_1.16.0
[116] bitops_1.0-7 listenv_0.9.1 viridisLite_0.4.2 scales_1.3.0 ggridges_0.5.6
[121] purrr_1.0.2 crayon_1.5.2 rlang_1.1.4 cowplot_1.1.3 fastmatch_1.1-6
Hi, thanks for a great tool! I have encountered a strange issue where the CoveragePlot is completely blank for a certain region (in this case all of chromosome 9), despite there being fragment counts in the range I am trying to plot. Since this issue doesn't occur with every dataset, it's difficult to provide a reproducible example, but I have tried to describe the problem as well as I can:
If I try to plot coverage over the peak with the highest number of total counts on chromosome 9, I get an error:
The region in question does contain fragments:
And doing the same thing for another chromosome works fine:
If I merge cells from this object with another dataset I have, the fragments from the other samples plot without issue, but the aforementioned object is still blank, despite the fact that there clearly are fragments present in the region.
CoveragePlot(merge, names(rsums_9[which.max(rsums_9)]), extend.upstream = 10000, extend.downstream = 10000)What could be causing this? Would really appreciate any insight.