Closed yojetsharma closed 1 month ago
Restarting R session... Loading required package: SeuratObject Loading required package: sp Attaching package: ‘SeuratObject’ The following object is masked from ‘package:base’: intersect > library(Seurat) > library(reticulate) > library(dplyr) Attaching package: ‘dplyr’ The following objects are masked from ‘package:stats’: filter, lag The following objects are masked from ‘package:base’: intersect, setdiff, setequal, union > library(Matrix) > Genotype1 <- + SCTransform( + npcs.genotype[["WT"]], + vst.flavor = "v2", + verbose = TRUE, + vars.to.regress = c("percent_ribo", "percent_mito"), assay = "RNA" + ) %>% + RunPCA(npcs = 15, verbose = TRUE) %>% + RunUMAP( + reduction = "pca", + dims = 1:15, + verbose = TRUE, assay = "RNA", + umap.method = 'umap-learn', + metric = 'correlation' + ) %>% + FindNeighbors(reduction = "pca", + dims = 1:15, + verbose = TRUE) %>% + FindClusters(resolution = 0.7, verbose = TRUE) Running SCTransform on assay: RNA Running SCTransform on layer: counts.d149npcs vst.flavor='v2' set. Using model with fixed slope and excluding poisson genes. Variance stabilizing transformation of count matrix of size 20921 by 1312 Model formula is y ~ log_umi Get Negative Binomial regression parameters per gene Using 2000 genes, 1312 cells Found 13 outliers - those will be ignored in fitting/regularization step Second step: Get residuals using fitted parameters for 20921 genes Computing corrected count matrix for 20921 genes Calculating gene attributes Wall clock passed: Time difference of 3.627898 secs Determine variable features Regressing out percent_ribo, percent_mito |============================================================================================================================================| 100% Centering data matrix |============================================================================================================================================| 100% Set default assay to SCT PC_ 1 Positive: DACH1, LINC01414, NRG3, DLG2, CREB5, HPSE2, NAV3, LINC00461, SYNE2, AC110023.1 PTPRZ1, AC096570.1, KCNQ3, FUT9, LINC01551, NAV1, NRCAM, DAB1, AL591519.1, IL1RAPL1 NKAIN3, PCDH15, ASPM, CENPF, GLI3, DLGAP1, CDH12, AC091078.1, GALNT13, NRXN3 Negative: RMST, GLIS3, ERBB4, CDH6, TPM1, WLS, EPHA7, COL1A2, TRPM3, COLEC12 NFIA, BNC2, LINC00472, UNC5C, PCDH7, RBMS3, SEMA3C, RBFOX1, SLIT2, ADGRL3 LMX1A, VIM, GNG12-AS1, PCDH9, SAMD4A, SULF1, SLIT3, AKAP12, CALD1, FGFR2 PC_ 2 Positive: COL1A2, COL1A1, CALD1, COL3A1, FN1, DLC1, COL5A1, EDNRA, LUM, TWIST1 HMCN1, PRRX1, COL12A1, COL8A1, COL4A1, ITGAV, MMP16, TAGLN, DDR2, TNC LAMB1, EBF1, TGFB2, CDH11, FSTL1, FLRT2, CPED1, KIF26B, CACNA1C, PAPPA Negative: RMST, TRPM3, ERBB4, EPHA7, RBFOX1, GLIS3, LMX1A, TPBG, NEBL, ADGRL3 PCDH7, SLIT2, UNC5D, CADPS2, NLGN1, PAX3, LINC00472, AC016152.1, ZNF521, MIR99AHG SAMD5, LINGO2, CTNNA2, AL033504.1, AC092957.1, CDH6, UBE3D, RFX3, EFNA5, PCDH9 PC_ 3 Positive: CENPF, ASPM, TOP2A, CENPE, KIF14, APOLD1, MKI67, TPX2, SGO2, CCNB1 ECT2, GAS2L3, HMGB2, NUSAP1, ARL6IP1, DLGAP5, UBE2C, KIF18A, KPNA2, KNL1 KIF4A, PIF1, BUB1, KIF20B, GTSE1, TUBB4B, KIF11, G2E3, NUF2, ARHGAP11B Negative: DACH1, LINC01414, KCNT2, PCDH15, IMMP2L, RIMS2, IPO9-AS1, NAV1, EBF1, ADAMTS6 GPM6A, NRG3, GLI3, CREB5, SOX5, FAM155A, NKAIN3, GALNT13, SLC44A5, HDAC9 PHACTR1, SLC1A3, NRXN3, DLGAP1, ST6GALNAC3, FUT9, GRM8, BMPR1B, BICC1, KCNB2 PC_ 4 Positive: UNC5C, NFIA, BNC2, NR2F2-AS1, SEMA5A, PCDH9, KIF26B, PRKG1, MIR99AHG, AL157778.1 ADGRV1, TWIST1, EDNRA, COL3A1, COLEC12, CDH11, TMEM132C, EPHA7, COBLL1, SLIT3 PRRX1, LSAMP, ROBO1, ST6GALNAC3, FGFR2, RBMS3, CPED1, RSPO3, ADGRL3, EBF1 Negative: TAGLN, MYH9, CCN2, TPM1, ANKRD1, CRIM1, VIM, AC016766.1, TGFB2, ACTN1 MAMDC2, MYL9, CCN1, COL11A1, CALD1, PAPPA, ACTB, ITGAV, ACTG1, ANKRD45 CLSTN2, ANXA1, DDAH1, NOX4, PLK2, GJA1, LPP, NEAT1, STK38L, RAI14 PC_ 5 Positive: TRPM3, RBFOX1, TMEM132C, OTX2-AS1, NLGN1, ADAMTS18, MECOM, PAX3, GRAMD1B, CLVS1 OCA2, LGR5, MIR181A1HG, CACHD1, CADPS2, MIR100HG, EFNA5, UNC5D, ROR1, KIAA1217 HMCN1, NRP1, XACT, EDIL3, NAV1, EBF1, MAML3, EDNRA, TMEFF2, AC016152.1 Negative: UNC5C, ADGRV1, GRIA1, AKAP12, CDH6, AC105411.1, GPC6, PTPRM, DIAPH3, BRIP1 MIR99AHG, MIR924HG, NAV2, FGFR2, ADCY2, RIMS2, DTL, RFC3, POLA1, PRKCA WLS, ATAD2, LINC00472, GUCY1A2, NEGR1, FAT3, MMS22L, PDE4B, CENPK, FRMD3 Error in py_get_attr(x, name, FALSE) : AttributeError: module 'umap' has no attribute 'pkg_resources' Run `reticulate::py_last_error()` for details. > reticulate::py_last_error() ── Python Exception Message ───────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── AttributeError: module 'umap' has no attribute 'pkg_resources' ── R Traceback ────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────────── ▆ 1. ├─... %>% FindClusters(resolution = 0.7, verbose = TRUE) 2. ├─Seurat::FindClusters(., resolution = 0.7, verbose = TRUE) 3. ├─Seurat::FindNeighbors(., reduction = "pca", dims = 1:15, verbose = TRUE) 4. ├─Seurat::RunUMAP(...) 5. └─Seurat:::RunUMAP.Seurat(...) 6. ├─Seurat::RunUMAP(...) 7. └─Seurat:::RunUMAP.default(...) 8. ├─base::numeric_version(x = umap_import$pkg_resources$get_distribution("umap-learn")$version) 9. │ └─base::.make_numeric_version(x, strict, .standard_regexps()$valid_numeric_version) 10. ├─umap_import$pkg_resources 11. └─reticulate:::`$.python.builtin.module`(umap_import, "pkg_resources") 12. └─reticulate::py_get_attr(x, name, FALSE) See `reticulate::py_last_error()$r_trace$full_call` for more details. > sessionInfo() R version 4.3.3 (2024-02-29) Platform: x86_64-pc-linux-gnu (64-bit) Running under: Ubuntu 22.04.4 LTS Matrix products: default BLAS: /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0 Random number generation: RNG: L'Ecuyer-CMRG Normal: Inversion Sample: Rejection locale: [1] LC_CTYPE=en_IN.UTF-8 LC_NUMERIC=C LC_TIME=en_IN.UTF-8 LC_COLLATE=en_IN.UTF-8 LC_MONETARY=en_IN.UTF-8 [6] LC_MESSAGES=en_IN.UTF-8 LC_PAPER=en_IN.UTF-8 LC_NAME=C LC_ADDRESS=C LC_TELEPHONE=C [11] LC_MEASUREMENT=en_IN.UTF-8 LC_IDENTIFICATION=C time zone: Asia/Kolkata tzcode source: system (glibc) attached base packages: [1] stats graphics grDevices utils datasets methods base other attached packages: [1] Matrix_1.6-5 dplyr_1.1.4 reticulate_1.36.0 Seurat_5.0.3 SeuratObject_5.0.1 sp_2.1-3 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_3.0-4 [6] zlibbioc_1.48.2 vctrs_0.6.5 ROCR_1.0-11 DelayedMatrixStats_1.24.0 spatstat.explore_3.2-7 [11] RCurl_1.98-1.14 S4Arrays_1.2.1 htmltools_0.5.8.1 SparseArray_1.2.4 sctransform_0.4.1 [16] parallelly_1.37.1 KernSmooth_2.23-22 htmlwidgets_1.6.4 ica_1.0-3 plyr_1.8.9 [21] plotly_4.10.4 zoo_1.8-12 igraph_2.0.3 mime_0.12 lifecycle_1.0.4 [26] pkgconfig_2.0.3 R6_2.5.1 fastmap_1.1.1 GenomeInfoDbData_1.2.11 MatrixGenerics_1.14.0 [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_0.40.2 rprojroot_2.0.4 tensor_1.5 RSpectra_0.16-1 [41] irlba_2.3.5.1 GenomicRanges_1.54.1 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_4.3.3 here_1.0.1 [51] withr_3.0.0 fastDummies_1.7.3 MASS_7.3-60.0.1 rappdirs_0.3.3 DelayedArray_0.28.0 [56] tools_4.3.3 lmtest_0.9-40 httpuv_1.6.15 future.apply_1.11.2 goftest_1.2-3 [61] glmGamPoi_1.14.3 glue_1.7.0 nlme_3.1-164 promises_1.3.0 grid_4.3.3 [66] Rtsne_0.17 cluster_2.1.6 reshape2_1.4.4 generics_0.1.3 gtable_0.3.4 [71] spatstat.data_3.0-4 tidyr_1.3.1 data.table_1.15.4 utf8_1.2.4 XVector_0.42.0 [76] BiocGenerics_0.48.1 spatstat.geom_3.2-9 RcppAnnoy_0.0.22 ggrepel_0.9.5 RANN_2.6.1 [81] pillar_1.9.0 stringr_1.5.1 spam_2.10-0 RcppHNSW_0.6.0 later_1.3.2 [86] splines_4.3.3 lattice_0.22-6 survival_3.5-8 deldir_2.0-4 tidyselect_1.2.1 [91] miniUI_0.1.1.1 pbapply_1.7-2 knitr_1.46 gridExtra_2.3 IRanges_2.36.0 [96] SummarizedExperiment_1.32.0 scattermore_1.2 stats4_4.3.3 xfun_0.43 Biobase_2.62.0 [101] matrixStats_1.3.0 stringi_1.8.3 lazyeval_0.2.2 codetools_0.2-18 tibble_3.2.1 [106] cli_3.6.2 uwot_0.2.1 xtable_1.8-4 munsell_0.5.1 Rcpp_1.0.12 [111] GenomeInfoDb_1.38.8 globals_0.16.3 spatstat.random_3.2-3 png_0.1-8 parallel_4.3.3 [116] ggplot2_3.5.0 dotCall64_1.1-1 sparseMatrixStats_1.14.0 bitops_1.0-7 listenv_0.9.1 [121] viridisLite_0.4.2 scales_1.3.0 ggridges_0.5.6 crayon_1.5.2 leiden_0.4.3.1 [126] purrr_1.0.2 rlang_1.1.3 cowplot_1.1.3
Hi, Thank you for reporting this. This is a known issue from our end and we will try to fix it as soon as possible. In the meantime, please down-grade the umap-learn package to umap-learn to 0.5.3 (please refer to this post for details).
yojetsharma
commented
3 months ago 
longmanz