split.by feature in vizualization is not working well

[jgarces02]

Hi. I'm having some problems with the recently added feature to split the visualization according to a second variable in the metadata. I tried it in CoveragePlot and PlotFootprint, and it crashed in both cases... I think they could be related.

Thanks in advance.

> CoveragePlot(myobject, region = "chr3-183515949-183516718", features = "KLHL6",
    extend.upstream = 1000, extend.downstream = 1000,
    split.by = "BMI_change",
    group.by = "predicted.celltype.l2_curated", idents = "CD16 Monos")
Error in `group_by()`:
! Must group by variables found in `.data`.
✖ Column `group` is not found.
Run `rlang::last_trace()` to see where the error occurred.

> PlotFootprint(myobject, label = F, features = "PAX7", 
    group.by = "predicted.celltype.l2_curated", idents = "CD16 Monos", 
    split.by = "BMI_change") + patchwork::plot_layout(ncol = 1)
Error in vector(mode = "numeric", length = npos * ngroup) :
  invalid 'length' argument

(metadata)

> head(myobject@meta.data)
                                      orig.ident nCount_RNA nFeature_RNA
1_AAACCAACAAGTTATC-1 SD-2520_1003_BL_ES_multiome       1050          700
1_AAACGCGCAGCCAGTT-1 SD-2520_1003_BL_ES_multiome       1150          732
1_AAAGCCGCAGCACGTT-1 SD-2520_1003_BL_ES_multiome       1310          940
1_AAAGCCGCAGCTACGT-1 SD-2520_1003_BL_ES_multiome       2230         1140
1_AAAGGCTCATGAGCAG-1 SD-2520_1003_BL_ES_multiome       1376          868
1_AACAAGCCAAATGCCC-1 SD-2520_1003_BL_ES_multiome       1447          940
                     nCount_ATAC nFeature_ATAC nCount_HTO nFeature_HTO
1_AAACCAACAAGTTATC-1        1594          1436       4328            8
1_AAACGCGCAGCCAGTT-1        2323          2073       2132            8
1_AAAGCCGCAGCACGTT-1        4305          3877       1870            8
1_AAAGCCGCAGCTACGT-1         629           610      12644            8
1_AAAGGCTCATGAGCAG-1        4796          4323       3675            8
1_AACAAGCCAAATGCCC-1        8913          7647       5384            8
                     percent.mt nucleosome_signal nucleosome_percentile
1_AAACCAACAAGTTATC-1  12.857143         1.4131737                  0.76
1_AAACGCGCAGCCAGTT-1   2.695652         0.7011494                  0.21
1_AAAGCCGCAGCACGTT-1   4.503817         1.5840336                  0.84
1_AAAGCCGCAGCTACGT-1  17.085202         0.9302326                  0.40
1_AAAGGCTCATGAGCAG-1  19.404070         1.7010309                  0.89
1_AACAAGCCAAATGCCC-1  21.561852         1.0244141                  0.48
                     TSS.enrichment TSS.percentile
1_AAACCAACAAGTTATC-1       4.506263           0.04
1_AAACGCGCAGCCAGTT-1       5.408545           0.20
1_AAAGCCGCAGCACGTT-1       3.785757           0.01
1_AAAGCCGCAGCTACGT-1       4.220779           0.02
1_AAAGGCTCATGAGCAG-1       4.090388           0.02
1_AACAAGCCAAATGCCC-1       4.654981           0.05
                     predicted.celltype.l2_curated  BMI_change
1_AAACCAACAAGTTATC-1                   NKs (early) weight loss
1_AAACGCGCAGCCAGTT-1                          preB weight loss
1_AAAGCCGCAGCACGTT-1                          preB weight loss
1_AAAGCCGCAGCTACGT-1                    CD14 monos weight loss
1_AAAGGCTCATGAGCAG-1                     CD8 naive weight loss
1_AACAAGCCAAATGCCC-1                          HSCs weight loss

(sessionInfo)

R version 4.3.2 (2023-10-31)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: CentOS Linux 7 (Core)

Matrix products: default
BLAS/LAPACK: /rtsess01/juno/home/garcesj/software/miniconda3/envs/scNUTRI/lib/libopenblasp-r0.3.25.so;  LAPACK version 3.11.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
 [3] 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
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
 [9] LC_ADDRESS=C               LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C

time zone: America/New_York
tzcode source: system (glibc)

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

other attached packages:
 [1] BSgenome.Hsapiens.UCSC.hg38_1.4.5 BSgenome_1.70.1
 [3] rtracklayer_1.62.0                BiocIO_1.12.0
 [5] Biostrings_2.70.1                 XVector_0.42.0
 [7] GenomicRanges_1.54.1              GenomeInfoDb_1.38.5
 [9] IRanges_2.36.0                    S4Vectors_0.40.2
[11] motifmatchr_1.24.0                TFBSTools_1.40.0
[13] JASPAR2020_0.99.10                reshape2_1.4.4
[15] dplyr_1.1.4                       future_1.33.1
[17] ggside_0.2.3                      patchwork_1.2.0
[19] ggplot2_3.4.4                     AnnotationHub_3.10.0
[21] BiocFileCache_2.10.1              dbplyr_2.4.0
[23] BiocGenerics_0.48.1               Signac_1.12.9004
[25] Seurat_5.0.1                      SeuratObject_5.0.1
[27] sp_2.1-2

loaded via a namespace (and not attached):
  [1] RcppAnnoy_0.0.22              splines_4.3.2
  [3] later_1.3.2                   bitops_1.0-7
  [5] filelock_1.0.3                R.oo_1.26.0
  [7] tibble_3.2.1                  polyclip_1.10-6
  [9] XML_3.99-0.16.1               DirichletMultinomial_1.44.0
 [11] fastDummies_1.7.3             lifecycle_1.0.4
 [13] globals_0.16.2                lattice_0.22-5
 [15] MASS_7.3-60.0.1               magrittr_2.0.3
 [17] plotly_4.10.4                 yaml_2.3.8
 [19] httpuv_1.6.13                 sctransform_0.4.1
 [21] spam_2.10-0                   spatstat.sparse_3.0-3
 [23] reticulate_1.34.0             CNEr_1.38.0
 [25] cowplot_1.1.3                 pbapply_1.7-2
 [27] DBI_1.2.1                     RColorBrewer_1.1-3
 [29] abind_1.4-5                   zlibbioc_1.48.0
 [31] Rtsne_0.17                    R.utils_2.12.3
 [33] purrr_1.0.2                   RCurl_1.98-1.14
 [35] pracma_2.4.4                  tweenr_2.0.2
 [37] rappdirs_0.3.3                GenomeInfoDbData_1.2.11
 [39] ggrepel_0.9.5                 irlba_2.3.5.1
 [41] listenv_0.9.0                 spatstat.utils_3.0-4
 [43] seqLogo_1.68.0                goftest_1.2-3
 [45] RSpectra_0.16-1               annotate_1.80.0
 [47] spatstat.random_3.2-2         fitdistrplus_1.1-11
 [49] parallelly_1.36.0             DelayedArray_0.28.0
 [51] leiden_0.4.3.1                codetools_0.2-19
 [53] RcppRoll_0.3.0                ggforce_0.4.1
 [55] tidyselect_1.2.0              ggseqlogo_0.2
 [57] farver_2.1.1                  matrixStats_1.2.0
 [59] spatstat.explore_3.2-5        GenomicAlignments_1.38.2
 [61] jsonlite_1.8.8                ellipsis_0.3.2
 [63] progressr_0.14.0              ggridges_0.5.6
 [65] survival_3.5-7                systemfonts_1.0.5
 [67] tools_4.3.2                   ragg_1.2.7
 [69] TFMPvalue_0.0.9               ica_1.0-3
 [71] Rcpp_1.0.12                   glue_1.7.0
 [73] SparseArray_1.2.3             gridExtra_2.3
 [75] MatrixGenerics_1.14.0         withr_3.0.0
 [77] BiocManager_1.30.22           fastmap_1.1.1
 [79] fansi_1.0.6                   caTools_1.18.2
 [81] digest_0.6.34                 R6_2.5.1
 [83] mime_0.12                     textshaping_0.3.7
 [85] colorspace_2.1-0              GO.db_3.18.0
 [87] scattermore_1.2               poweRlaw_0.80.0
 [89] gtools_3.9.5                  tensor_1.5
 [91] spatstat.data_3.0-4           RSQLite_2.3.5
 [93] R.methodsS3_1.8.2             utf8_1.2.4
 [95] tidyr_1.3.1                   generics_0.1.3
 [97] data.table_1.14.10            S4Arrays_1.2.0
 [99] httr_1.4.7                    htmlwidgets_1.6.4
[101] uwot_0.1.16                   pkgconfig_2.0.3
[103] gtable_0.3.4                  blob_1.2.4
[105] lmtest_0.9-40                 htmltools_0.5.7
[107] dotCall64_1.1-1               scales_1.3.0
[109] Biobase_2.62.0                png_0.1-8
[111] tzdb_0.4.0                    rjson_0.2.21
[113] nlme_3.1-164                  curl_5.2.0
[115] zoo_1.8-12                    cachem_1.0.8
[117] stringr_1.5.1                 BiocVersion_3.18.1
[119] KernSmooth_2.23-22            parallel_4.3.2
[121] miniUI_0.1.1.1                AnnotationDbi_1.64.1
[123] restfulr_0.0.15               pillar_1.9.0
[125] grid_4.3.2                    vctrs_0.6.5
[127] RANN_2.6.1                    promises_1.2.1
[129] xtable_1.8-4                  cluster_2.1.6
[131] readr_2.1.5                   cli_3.6.2
[133] compiler_4.3.2                Rsamtools_2.18.0
[135] rlang_1.1.3                   crayon_1.5.2
[137] future.apply_1.11.1           labeling_0.4.3
[139] plyr_1.8.9                    stringi_1.8.3
[141] viridisLite_0.4.2             deldir_2.0-2
[143] BiocParallel_1.36.0           munsell_0.5.0
[145] lazyeval_0.2.2                spatstat.geom_3.2-7
[147] Matrix_1.6-5                  RcppHNSW_0.5.0
[149] hms_1.1.3                     bit64_4.0.5
[151] KEGGREST_1.42.0               shiny_1.8.0
[153] SummarizedExperiment_1.32.0   interactiveDisplayBase_1.40.0
[155] ROCR_1.0-11                   igraph_1.6.0
[157] memoise_2.0.1                 fastmatch_1.1-4
[159] bit_4.0.5

[timoast]

Hi @jgarces02, thanks for the bug report. This issue was related to the combined use of idents and split.by, should now be fixed on the develop branch.