* using log directory ‘/data/blackswan/ripley/R/packages/tests-devel/ggsector.Rcheck’ * using R Under development (unstable) (2025-12-12 r89161) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3) GNU Fortran (GCC) 14.2.1 20240912 (Red Hat 14.2.1-3) * running under: Fedora Linux 40 (Workstation Edition) * using session charset: UTF-8 * checking for file ‘ggsector/DESCRIPTION’ ... OK * this is package ‘ggsector’ version ‘1.7.0’ * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for executable files ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘ggsector’ can be installed ... OK * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd line widths ... OK * checking Rd cross-references ... OK * checking for missing documentation entries ... OK * checking for code/documentation mismatches ... OK * checking Rd \usage sections ... OK * checking Rd contents ... OK * checking for unstated dependencies in examples ... OK * checking installed files from ‘inst/doc’ ... OK * checking files in ‘vignettes’ ... OK * checking examples ... OK * checking examples with --run-donttest ... [47s/48s] ERROR Running examples in ‘ggsector-Ex.R’ failed The error most likely occurred in: > ### Name: SectorPlot > ### Title: Draw sector for seurat object > ### Aliases: SectorPlot > > ### ** Examples > > ## No test: > ## Download pbmc data from > # https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz > library(Seurat) Loading required package: SeuratObject Loading required package: sp Attaching package: ‘SeuratObject’ The following objects are masked from ‘package:base’: intersect, t > path <- paste0(tempdir(), "/pbmc3k.tar.gz") > file <- paste0(tempdir(), "/filtered_gene_bc_matrices/hg19") > download.file( + "https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz", + path + ) trying URL 'https://cf.10xgenomics.com/samples/cell/pbmc3k/pbmc3k_filtered_gene_bc_matrices.tar.gz' Content type 'application/x-tar' length 7621991 bytes (7.3 MB) ================================================== downloaded 7.3 MB > untar(path, exdir = tempdir()) > pbmc.data <- Read10X(data.dir = file) > pbmc <- CreateSeuratObject( + counts = pbmc.data, + project = "pbmc3k", + min.cells = 3, + min.features = 200 + ) Warning: Feature names cannot have underscores ('_'), replacing with dashes ('-') > pbmc <- NormalizeData(pbmc) Normalizing layer: counts Performing log-normalization 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| > pbmc <- FindVariableFeatures(pbmc, selection.method = "vst", nfeatures = 2000) Finding variable features for layer counts Calculating gene variances 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Calculating feature variances of standardized and clipped values 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| > pbmc <- ScaleData(pbmc, features = rownames(pbmc)) Centering and scaling data matrix | | | 0% | |===== | 7% | |========== | 14% | |=============== | 21% | |==================== | 29% | |========================= | 36% | |============================== | 43% | |=================================== | 50% | |======================================== | 57% | |============================================= | 64% | |================================================== | 71% | |======================================================= | 79% | |============================================================ | 86% | |================================================================= | 93% | |======================================================================| 100% > pbmc <- RunPCA(pbmc) PC_ 1 Positive: MALAT1, LTB, IL32, CD2, ACAP1, STK17A, CTSW, CD247, CCL5, GIMAP5 AQP3, GZMA, CST7, TRAF3IP3, MAL, HOPX, ITM2A, GZMK, MYC, BEX2 GIMAP7, ETS1, LDLRAP1, ZAP70, LYAR, RIC3, TNFAIP8, KLRG1, SAMD3, NKG7 Negative: CST3, TYROBP, LST1, AIF1, FTL, FCN1, LYZ, FTH1, S100A9, FCER1G TYMP, CFD, LGALS1, CTSS, S100A8, SERPINA1, LGALS2, SPI1, IFITM3, PSAP CFP, SAT1, IFI30, COTL1, S100A11, NPC2, LGALS3, GSTP1, PYCARD, NCF2 PC_ 2 Positive: CD79A, MS4A1, TCL1A, HLA-DQA1, HLA-DRA, HLA-DQB1, LINC00926, CD79B, HLA-DRB1, CD74 HLA-DPB1, HLA-DMA, HLA-DQA2, HLA-DRB5, HLA-DPA1, HLA-DMB, FCRLA, HVCN1, LTB, BLNK KIAA0125, P2RX5, IRF8, IGLL5, SWAP70, ARHGAP24, SMIM14, PPP1R14A, FCRL2, C16orf74 Negative: NKG7, PRF1, CST7, GZMA, GZMB, FGFBP2, CTSW, GNLY, GZMH, SPON2 CCL4, FCGR3A, CCL5, CD247, XCL2, CLIC3, AKR1C3, SRGN, HOPX, CTSC TTC38, S100A4, ANXA1, IL32, IGFBP7, ID2, ACTB, XCL1, APOBEC3G, SAMD3 PC_ 3 Positive: HLA-DQA1, CD79A, CD79B, HLA-DQB1, HLA-DPB1, CD74, HLA-DPA1, MS4A1, HLA-DRB1, HLA-DRB5 HLA-DRA, HLA-DQA2, TCL1A, LINC00926, HLA-DMB, HLA-DMA, HVCN1, FCRLA, IRF8, BLNK KIAA0125, SMIM14, PLD4, IGLL5, P2RX5, TMSB10, SWAP70, LAT2, MALAT1, IGJ Negative: PPBP, PF4, SDPR, SPARC, GNG11, NRGN, GP9, RGS18, TUBB1, CLU HIST1H2AC, AP001189.4, ITGA2B, CD9, TMEM40, CA2, PTCRA, ACRBP, MMD, TREML1 NGFRAP1, F13A1, RUFY1, SEPT5, MPP1, CMTM5, TSC22D1, MYL9, RP11-367G6.3, GP1BA PC_ 4 Positive: HLA-DQA1, CD79A, CD79B, HIST1H2AC, HLA-DQB1, PF4, MS4A1, SDPR, CD74, PPBP HLA-DPB1, GNG11, HLA-DQA2, SPARC, HLA-DRB1, HLA-DPA1, GP9, TCL1A, HLA-DRA, LINC00926 NRGN, RGS18, HLA-DRB5, PTCRA, CD9, AP001189.4, CA2, CLU, TUBB1, ITGA2B Negative: VIM, S100A8, S100A6, S100A4, S100A9, TMSB10, IL32, GIMAP7, LGALS2, S100A10 RBP7, FCN1, MAL, LYZ, S100A12, MS4A6A, CD2, FYB, S100A11, FOLR3 GIMAP4, AQP3, ANXA1, AIF1, MALAT1, GIMAP5, IL8, IFI6, TRABD2A, TMSB4X PC_ 5 Positive: GZMB, FGFBP2, NKG7, GNLY, PRF1, CCL4, CST7, SPON2, GZMA, CLIC3 GZMH, XCL2, CTSW, TTC38, AKR1C3, CCL5, IGFBP7, XCL1, CCL3, S100A8 TYROBP, HOPX, CD160, HAVCR2, S100A9, FCER1G, PTGDR, LGALS2, RBP7, S100A12 Negative: LTB, VIM, AQP3, PPA1, MAL, KIAA0101, CD2, CYTIP, CORO1B, FYB IL32, TRADD, ANXA5, TUBA1B, HN1, TYMS, PTGES3, ITM2A, COTL1, GPR183 TNFAIP8, ACTG1, TRAF3IP3, ATP5C1, GIMAP4, ZWINT, PRDX1, LDLRAP1, ABRACL, NGFRAP1 > pbmc <- RunUMAP(pbmc, dim = 1:10) Warning: The default method for RunUMAP has changed from calling Python UMAP via reticulate to the R-native UWOT using the cosine metric To use Python UMAP via reticulate, set umap.method to 'umap-learn' and metric to 'correlation' This message will be shown once per session 17:10:14 UMAP embedding parameters a = 0.9922 b = 1.112 17:10:14 Read 2700 rows and found 10 numeric columns 17:10:14 Using Annoy for neighbor search, n_neighbors = 30 17:10:14 Building Annoy index with metric = cosine, n_trees = 50 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 17:10:14 Writing NN index file to temp file /tmp/Rtmp77Uu7i/working_dir/RtmpMk51hx/fileb501f5b7c6d68 17:10:14 Searching Annoy index using 1 thread, search_k = 3000 17:10:15 Annoy recall = 100% 17:10:15 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30 17:10:16 Initializing from normalized Laplacian + noise (using RSpectra) 17:10:16 Commencing optimization for 500 epochs, with 107868 positive edges 17:10:16 Using rng type: pcg Using method 'umap' 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| 17:10:21 Optimization finished > pbmc <- FindNeighbors(pbmc, dims = 1:10) Computing nearest neighbor graph Computing SNN > pbmc <- FindClusters(pbmc, resolution = 1) Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck Number of nodes: 2700 Number of edges: 97892 Running Louvain algorithm... 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Maximum modularity in 10 random starts: 0.8072 Number of communities: 11 Elapsed time: 0 seconds > pbmc <- FindClusters(pbmc, resolution = 0.5) Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck Number of nodes: 2700 Number of edges: 97892 Running Louvain algorithm... 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Maximum modularity in 10 random starts: 0.8719 Number of communities: 9 Elapsed time: 0 seconds > markers <- tibble::tribble( + ~type, ~marker, + "Naive CD4+ T", "IL7R,CCR7", + "CD14+ Mono", "CD14,LYZ", + "Memory CD4+", "IL7R,S100A4", + "B", "MS4A1", + "CD8+ T", "CD8A", + "FCGR3A+ Mono", "FCGR3A,MS4A7", + "NK", "GNLY,NKG7", + "DC", "FCER1A,CST3", + "Platelet", "PPBP", + ) %>% + tidyr::separate_rows(marker, sep = ", *") %>% + dplyr::distinct() > > # Dotplot > DotPlot(pbmc, features = unique(markers$marker)) + coord_flip() > > # contrast with DotPlot > SectorPlot(pbmc, markers$marker, features.level = unique(rev(markers$marker))) Error: ! The `slot` argument of `GetAssayData()` was deprecated in SeuratObject 5.0.0 and is now defunct. ℹ Please use the `layer` argument instead. Backtrace: ▆ 1. └─ggsector::SectorPlot(pbmc, markers$marker, features.level = unique(rev(markers$marker))) 2. ├─Seurat::GetAssayData(sob, slot = slot, assay = assay) 3. └─SeuratObject:::GetAssayData.Seurat(sob, slot = slot, assay = assay) 4. └─SeuratObject::.Deprecate(...) 5. └─lifecycle::deprecate_stop(...) 6. └─lifecycle:::deprecate_stop0(msg) 7. └─rlang::cnd_signal(...) Execution halted * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... [35s/36s] OK * checking PDF version of manual ... OK * checking for non-standard things in the check directory ... OK * checking for detritus in the temp directory ... OK * checking for new files in some other directories ... OK * DONE Status: 1 ERROR See ‘/data/blackswan/ripley/R/packages/tests-devel/ggsector.Rcheck/00check.log’ for details. Command exited with non-zero status 1 Time 2:29.18, 127.41 + 20.44