error in scATAC
LidiaML opened this issue · comments
Dear developers,
I am trying to run scDblFinder to identify doublets in scATAC data processed with seurat and signac. I tried to use the same parameters you provide in the scATAC vignette but got an error during the "Training model" step. Any hint?
Congratulations for the work and many thanks in advance!
MRE -- Minimal example to reproduce the bug
scDblFinder(sce, clusters=NULL, artificialDoublets=1, aggregateFeatures=TRUE, nfeatures=25, processing="normFeatures"))
Traceback
Aggregating features... Creating ~4033 artificial doublets... Dimensional reduction Evaluating kNN... Training model... Error in if (length(expected) > 1 && x > min(expected) && x < max(expected)) return(0) : missing value where TRUE/FALSE needed
Session info
`
R version 4.1.3 (2022-03-10)
Platform: x86_64-conda-linux-gnu (64-bit)
Running under: Rocky Linux 8.8 (Green Obsidian)
Matrix products: default
BLAS/LAPACK: /home/lmateo/anaconda3/envs/R/lib/libopenblasp-r0.3.21.so
locale:
[1] LC_CTYPE=C 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
attached base packages:
[1] stats4 parallel stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] SingleCellExperiment_1.16.0 SummarizedExperiment_1.24.0
[3] MatrixGenerics_1.6.0 matrixStats_0.62.0
[5] scDblFinder_1.8.0 Rmagic_2.0.3
[7] future_1.26.1 RibosomalQC_0.1.0
[9] forcats_0.5.2 stringr_1.4.1
[11] purrr_0.3.5 readr_2.1.3
[13] tidyr_1.2.0 tibble_3.1.8
[15] tidyverse_1.3.1 gridExtra_2.3
[17] ggpubr_0.4.0 ggplot2_3.3.6
[19] dplyr_1.0.10 BSgenome.Mmusculus.UCSC.mm10_1.4.3
[21] BSgenome_1.62.0 rtracklayer_1.54.0
[23] Biostrings_2.62.0 XVector_0.34.0
[25] EnsDb.Mmusculus.v79_2.99.0 ensembldb_2.18.1
[27] AnnotationFilter_1.18.0 GenomicFeatures_1.46.1
[29] AnnotationDbi_1.56.2 Biobase_2.54.0
[31] GenomicRanges_1.46.1 GenomeInfoDb_1.30.1
[33] IRanges_2.28.0 S4Vectors_0.32.4
[35] BiocGenerics_0.40.0 Signac_1.8.0
[37] sp_1.5-1 SeuratObject_4.1.0
[39] Seurat_4.1.1 hwriter_1.3.2.1
[41] Matrix_1.5-3
loaded via a namespace (and not attached):
[1] rappdirs_0.3.3 scattermore_0.8
[3] bit64_4.0.5 irlba_2.3.5.1
[5] DelayedArray_0.20.0 data.table_1.14.4
[7] rpart_4.1.19 KEGGREST_1.34.0
[9] RCurl_1.98-1.9 generics_0.1.3
[11] ScaledMatrix_1.2.0 cowplot_1.1.1
[13] RSQLite_2.2.18 RANN_2.6.1
[15] bit_4.0.4 tzdb_0.3.0
[17] spatstat.data_3.0-0 xml2_1.3.3
[19] lubridate_1.8.0 httpuv_1.6.6
[21] assertthat_0.2.1 viridis_0.6.2
[23] hms_1.1.2 promises_1.2.0.1
[25] fansi_1.0.3 restfulr_0.0.15
[27] progress_1.2.2 dbplyr_2.2.1
[29] readxl_1.4.1 igraph_1.3.5
[31] DBI_1.1.3 htmlwidgets_1.5.4
[33] spatstat.geom_3.0-3 ellipsis_0.3.2
[35] backports_1.4.1 biomaRt_2.50.0
[37] deldir_1.0-6 sparseMatrixStats_1.6.0
[39] vctrs_0.5.0 here_1.0.1
[41] ROCR_1.0-11 abind_1.4-5
[43] cachem_1.0.6 withr_2.5.0
[45] progressr_0.11.0 sctransform_0.3.5
[47] GenomicAlignments_1.30.0 scran_1.22.1
[49] prettyunits_1.1.1 goftest_1.2-3
[51] cluster_2.1.4 lazyeval_0.2.2
[53] crayon_1.5.2 edgeR_3.36.0
[55] pkgconfig_2.0.3 nlme_3.1-160
[57] vipor_0.4.5 ProtGenerics_1.26.0
[59] rlang_1.0.6 globals_0.16.1
[61] lifecycle_1.0.3 miniUI_0.1.1.1
[63] filelock_1.0.2 BiocFileCache_2.2.0
[65] modelr_0.1.10 rsvd_1.0.5
[67] cellranger_1.1.0 rprojroot_2.0.3
[69] polyclip_1.10-4 lmtest_0.9-40
[71] carData_3.0-5 zoo_1.8-11
[73] reprex_2.0.2 beeswarm_0.4.0
[75] ggridges_0.5.3 png_0.1-7
[77] viridisLite_0.4.0 rjson_0.2.21
[79] bitops_1.0-7 KernSmooth_2.23-20
[81] tidyseurat_0.5.3 blob_1.2.3
[83] DelayedMatrixStats_1.16.0 parallelly_1.32.1
[85] spatstat.random_3.0-1 rstatix_0.7.1
[87] ggsignif_0.6.4 beachmat_2.10.0
[89] scales_1.2.1 memoise_2.0.1
[91] magrittr_2.0.3 plyr_1.8.8
[93] ica_1.0-3 zlibbioc_1.40.0
[95] compiler_4.1.3 dqrng_0.3.0
[97] BiocIO_1.4.0 RColorBrewer_1.1-3
[99] fitdistrplus_1.1-8 Rsamtools_2.10.0
[101] cli_3.4.1 listenv_0.8.0
[103] patchwork_1.1.1 pbapply_1.5-0
[105] MASS_7.3-58.1 mgcv_1.8-41
[107] tidyselect_1.2.0 stringi_1.7.8
[109] yaml_2.3.6 locfit_1.5-9.6
[111] BiocSingular_1.10.0 ggrepel_0.9.1
[113] grid_4.1.3 fastmatch_1.1-3
[115] tools_4.1.3 future.apply_1.9.0
[117] rstudioapi_0.14 bluster_1.4.0
[119] metapod_1.2.0 Rtsne_0.16
[121] digest_0.6.30 rgeos_0.5-9
[123] shiny_1.7.3 Rcpp_1.0.9
[125] car_3.1-1 broom_1.0.1
[127] scuttle_1.4.0 later_1.2.0
[129] RcppAnnoy_0.0.20 httr_1.4.4
[131] colorspace_2.0-3 rvest_1.0.3
[133] XML_3.99-0.12 fs_1.5.2
[135] tensor_1.5 reticulate_1.26
[137] splines_4.1.3 statmod_1.4.37
[139] uwot_0.1.14 RcppRoll_0.3.0
[141] spatstat.utils_3.0-1 scater_1.22.0
[143] xgboost_1.7.4.1 plotly_4.10.1
[145] xtable_1.8-4 jsonlite_1.8.3
[147] R6_2.5.1 pillar_1.8.1
[149] htmltools_0.5.3 mime_0.12
[151] glue_1.6.2 fastmap_1.1.0
[153] BiocParallel_1.28.3 BiocNeighbors_1.12.0
[155] codetools_0.2-18 utf8_1.2.2
[157] lattice_0.20-45 spatstat.sparse_3.0-0
[159] curl_4.3.3 ggbeeswarm_0.7.2
[161] leiden_0.4.3 colorRamps_2.3.1
[163] limma_3.50.3 survival_3.4-0
[165] ttservice_0.1.2 munsell_0.5.0
[167] GenomeInfoDbData_1.2.7 haven_2.5.1
[169] reshape2_1.4.4 gtable_0.3.1
[171] spatstat.core_2.4-4
`
Hi,
You're using an old version of scDblFinder (and, it seems, of bioconductor generally): you're at version 1.8, the current release version is 1.16. As far as I remember the ATAC variants were not even officially supported back then.
Please try updating to more recent versions. I'd recommend updating the whole bioconductor, because the single-cell field has been moving fast, but if for some reason you can't, you should be able to simply install scDblFinder from github using BiocManager::install("plger/scDblFinder").