DUTest function Error

[hkarakurt8742]

Hello, I am using Sierra and I replicated all results from vignette. I tried to use with my own data with same steps but in DUTest step I have an error:

Error in apply(annot.subset, 1, function(x) { : dim(X) must have a positive length

My function is:

res.table <- DUTest(lung_peaks,population.1 = "a",population.2 = "b",exp.thresh = 0.1,feature.type = c("UTR3", "exon"))

What might be the problem? I tested it with other clusters too but I have the same error.

Thank you in advance

`R version 4.2.2 (2022-10-31) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 20.04.5 LTS

Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale: [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C LC_TIME=tr_TR.UTF-8
[4] LC_COLLATE=en_US.UTF-8 LC_MONETARY=tr_TR.UTF-8 LC_MESSAGES=en_US.UTF-8
[7] LC_PAPER=tr_TR.UTF-8 LC_NAME=C LC_ADDRESS=C
[10] LC_TELEPHONE=C LC_MEASUREMENT=tr_TR.UTF-8 LC_IDENTIFICATION=C

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

other attached packages: [1] DEXSeq_1.42.0 RColorBrewer_1.1-3
[3] AnnotationDbi_1.58.0 DESeq2_1.36.0
[5] SummarizedExperiment_1.26.1 MatrixGenerics_1.8.1
[7] matrixStats_0.62.0 Biobase_2.56.0
[9] BiocParallel_1.30.4 patchwork_1.1.2
[11] dplyr_1.0.10 sp_1.5-0
[13] SeuratObject_4.1.2 Seurat_4.2.0
[15] BSgenome.Hsapiens.UCSC.hg38_1.4.4 BSgenome_1.64.0
[17] rtracklayer_1.56.1 Biostrings_2.64.1
[19] XVector_0.36.0 GenomicRanges_1.48.0
[21] GenomeInfoDb_1.32.4 IRanges_2.30.1
[23] S4Vectors_0.34.0 BiocGenerics_0.42.0
[25] magrittr_2.0.3 Sierra_0.99.27

loaded via a namespace (and not attached): [1] utf8_1.2.2 reticulate_1.26 R.utils_2.12.0
[4] tidyselect_1.2.0 RSQLite_2.2.18 htmlwidgets_1.5.4
[7] grid_4.2.2 Rtsne_0.16 munsell_0.5.0
[10] codetools_0.2-18 ica_1.0-3 statmod_1.4.37
[13] interp_1.1-3 future_1.28.0 miniUI_0.1.1.1
[16] withr_2.5.0 spatstat.random_2.2-0 colorspace_2.0-3
[19] progressr_0.11.0 filelock_1.0.2 knitr_1.40
[22] rstudioapi_0.14 SingleCellExperiment_1.18.1 ROCR_1.0-11
[25] tensor_1.5 listenv_0.8.0 labeling_0.4.2
[28] GenomeInfoDbData_1.2.8 hwriter_1.3.2.1 polyclip_1.10-0
[31] farver_2.1.1 bit64_4.0.5 parallelly_1.32.1
[34] vctrs_0.4.2 generics_0.1.3 xfun_0.33
[37] biovizBase_1.44.0 BiocFileCache_2.4.0 R6_2.5.1
[40] doParallel_1.0.17 locfit_1.5-9.6 AnnotationFilter_1.20.0
[43] spatstat.utils_2.3-1 bitops_1.0-7 cachem_1.0.6
[46] DelayedArray_0.22.0 assertthat_0.2.1 promises_1.2.0.1
[49] BiocIO_1.6.0 scales_1.2.1 nnet_7.3-18
[52] rgeos_0.5-9 gtable_0.3.1 globals_0.16.1
[55] goftest_1.2-3 ensembldb_2.20.2 rlang_1.0.6
[58] genefilter_1.78.0 splines_4.2.2 lazyeval_0.2.2
[61] dichromat_2.0-0.1 spatstat.geom_2.4-0 checkmate_2.1.0
[64] abind_1.4-5 yaml_2.3.6 reshape2_1.4.4
[67] GenomicFeatures_1.48.4 backports_1.4.1 httpuv_1.6.6
[70] Hmisc_4.7-1 tools_4.2.2 ggplot2_3.3.6
[73] ellipsis_0.3.2 spatstat.core_2.4-4 ggridges_0.5.4
[76] Rcpp_1.0.9 plyr_1.8.7 base64enc_0.1-3
[79] progress_1.2.2 zlibbioc_1.42.0 purrr_0.3.5
[82] RCurl_1.98-1.9 prettyunits_1.1.1 rpart_4.1.19
[85] deldir_1.0-6 pbapply_1.5-0 cowplot_1.1.1
[88] zoo_1.8-11 ggrepel_0.9.1 cluster_2.1.4
[91] data.table_1.14.4 scattermore_0.8 lmtest_0.9-40
[94] RANN_2.6.1 ProtGenerics_1.28.0 fitdistrplus_1.1-8
[97] hms_1.1.2 mime_0.12 xtable_1.8-4
[100] XML_3.99-0.11 jpeg_0.1-9 gridExtra_2.3
[103] compiler_4.2.2 biomaRt_2.52.0 tibble_3.1.8
[106] KernSmooth_2.23-20 crayon_1.5.2 R.oo_1.25.0
[109] htmltools_0.5.3 mgcv_1.8-41 later_1.3.0
[112] Formula_1.2-4 geneplotter_1.74.0 tidyr_1.2.1
[115] DBI_1.1.3 dbplyr_2.2.1 MASS_7.3-58.1
[118] rappdirs_0.3.3 Matrix_1.5-1 cli_3.4.1
[121] R.methodsS3_1.8.2 parallel_4.2.2 Gviz_1.40.1
[124] igraph_1.3.5 pkgconfig_2.0.3 GenomicAlignments_1.32.1
[127] foreign_0.8-83 spatstat.sparse_2.1-1 plotly_4.10.0
[130] xml2_1.3.3 foreach_1.5.2 annotate_1.74.0
[133] stringr_1.4.1 VariantAnnotation_1.42.1 digest_0.6.29
[136] sctransform_0.3.5 RcppAnnoy_0.0.19 spatstat.data_2.2-0
[139] leiden_0.4.3 htmlTable_2.4.1 uwot_0.1.14
[142] restfulr_0.0.15 curl_4.3.3 shiny_1.7.2
[145] Rsamtools_2.12.0 rjson_0.2.21 nlme_3.1-160
[148] lifecycle_1.0.3 jsonlite_1.8.2 limma_3.52.4
[151] viridisLite_0.4.1 fansi_1.0.3 pillar_1.8.1
[154] lattice_0.20-45 KEGGREST_1.36.3 fastmap_1.1.0
[157] httr_1.4.4 survival_3.4-0 glue_1.6.2
[160] png_0.1-7 iterators_1.0.14 bit_4.0.4
[163] stringi_1.7.8 blob_1.2.3 latticeExtra_0.6-30
[166] memoise_2.0.1 irlba_2.3.5.1 future.apply_1.9.1 `

[rj-patrick]

Sorry for the slow response. Can you confirm that the active identities in your Seurat object correspond to what is being input for population.1 and population.2?