Hi, big thanks for all your work on this software. I was using CoveragePlot and PeakPlot to plot both called peaks along with a granges object that I created with additional features (previously reported enhancers) when I realized that it seems like PeakPlot does not have the same behavior for the region input as CoveragePlot. Specifically this works:
PeakPlot( object = pbmc, region = "chr11-27654893-27720779", )
but the code below does not work:
PeakPlot( object = pbmc, region = "BDNF" )
The error is:
Error in .get_data_frame_col_as_numeric(df, granges_cols[["start"]]) :
some values in the "start" column cannot be turned into numeric values
In addition: Warning message:
Expected 3 pieces. Missing pieces filled with NA in 1 rows [1].
I believe the error is related to converting between granges and strings with the "-" separator. Gene inputs to CoveragePlot are working fine for me. I was able to use the workaround of explicitly defining regions, but it would be nice to have the gene name input working for combining multiple tracks.
Thank you!
sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)
Matrix products: default
BLAS: /wynton/home/cbi/shared/software/CBI/R-4.1.2-gcc8/lib64/R/lib/libRblas.so
LAPACK: /wynton/home/cbi/shared/software/CBI/R-4.1.2-gcc8/lib64/R/lib/libRlapack.so
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8 LC_MONETARY=en_US.UTF-8
[6] LC_MESSAGES=en_US.UTF-8 LC_PAPER=en_US.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] stats4 stats graphics grDevices utils datasets methods base
other attached packages:
[1] GenomicRanges_1.46.1 GenomeInfoDb_1.30.0 IRanges_2.28.0 S4Vectors_0.32.3 BiocGenerics_0.40.0 dplyr_1.0.7 SeuratObject_4.0.4
[8] Seurat_4.0.5 Signac_1.5.0
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_2.0-2 deldir_1.0-6 ellipsis_0.3.2 ggridges_0.5.3 lsa_0.73.2
[7] XVector_0.34.0 rstudioapi_0.13 spatstat.data_2.1-0 farver_2.1.0 leiden_0.3.9 listenv_0.8.0
[13] SnowballC_0.7.0 ggrepel_0.9.1 fansi_0.5.0 codetools_0.2-18 splines_4.1.2 docopt_0.7.1
[19] RcppRoll_0.3.0 polyclip_1.10-0 jsonlite_1.7.2 Rsamtools_2.10.0 ica_1.0-2 cluster_2.1.2
[25] png_0.1-7 uwot_0.1.11 ggforce_0.3.3 shiny_1.7.1 sctransform_0.3.2 spatstat.sparse_2.0-0
[31] compiler_4.1.2 httr_1.4.2 assertthat_0.2.1 Matrix_1.4-0 fastmap_1.1.0 lazyeval_0.2.2
[37] tweenr_1.0.2 later_1.3.0 htmltools_0.5.2 tools_4.1.2 igraph_1.2.9 gtable_0.3.0
[43] glue_1.5.1 GenomeInfoDbData_1.2.7 RANN_2.6.1 reshape2_1.4.4 fastmatch_1.1-3 Rcpp_1.0.7
[49] slam_0.1-49 scattermore_0.7 vctrs_0.3.8 Biostrings_2.62.0 nlme_3.1-153 ggseqlogo_0.1
[55] lmtest_0.9-39 stringr_1.4.0 globals_0.14.0 mime_0.12 miniUI_0.1.1.1 lifecycle_1.0.1
[61] irlba_2.3.5 goftest_1.2-3 future_1.23.0 zlibbioc_1.40.0 MASS_7.3-54 zoo_1.8-9
[67] scales_1.1.1 spatstat.core_2.3-2 promises_1.2.0.1 spatstat.utils_2.3-0 parallel_4.1.2 RColorBrewer_1.1-2
[73] reticulate_1.22 pbapply_1.5-0 gridExtra_2.3 ggplot2_3.3.5 rpart_4.1-15 stringi_1.7.6
[79] BiocParallel_1.28.2 rlang_0.4.12 pkgconfig_2.0.3 matrixStats_0.61.0 bitops_1.0-7 qlcMatrix_0.9.7
[85] lattice_0.20-45 ROCR_1.0-11 purrr_0.3.4 tensor_1.5 labeling_0.4.2 patchwork_1.1.1
[91] htmlwidgets_1.5.4 cowplot_1.1.1 tidyselect_1.1.1 parallelly_1.29.0 RcppAnnoy_0.0.19 plyr_1.8.6
[97] magrittr_2.0.1 R6_2.5.1 generics_0.1.1 DBI_1.1.1 mgcv_1.8-38 pillar_1.6.4
[103] fitdistrplus_1.1-6 survival_3.2-13 abind_1.4-5 RCurl_1.98-1.5 tibble_3.1.6 future.apply_1.8.1
[109] crayon_1.4.2 KernSmooth_2.23-20 utf8_1.2.2 spatstat.geom_2.3-1 plotly_4.10.0 grid_4.1.2
[115] data.table_1.14.2 sparsesvd_0.2 digest_0.6.29 xtable_1.8-4 tidyr_1.1.4 httpuv_1.6.3
[121] munsell_0.5.0 viridisLite_0.4.0
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