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This page was generated on 2025-09-30 15:41 -0400 (Tue, 30 Sep 2025).
Hostname | OS | Arch (*) | R version | Installed pkgs |
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nebbiolo2 | Linux (Ubuntu 24.04.3 LTS) | x86_64 | 4.5.1 Patched (2025-08-23 r88802) -- "Great Square Root" | 4843 |
Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X |
Package 381/433 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | ||||||||
spatialLIBD 1.21.6 (landing page) Leonardo Collado-Torres
| nebbiolo2 | Linux (Ubuntu 24.04.3 LTS) / x86_64 | OK | OK | OK | ![]() | |||||||
To the developers/maintainers of the spatialLIBD package: - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
Package: spatialLIBD |
Version: 1.21.6 |
Command: /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:spatialLIBD.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings spatialLIBD_1.21.6.tar.gz |
StartedAt: 2025-09-30 13:05:57 -0400 (Tue, 30 Sep 2025) |
EndedAt: 2025-09-30 13:27:18 -0400 (Tue, 30 Sep 2025) |
EllapsedTime: 1280.2 seconds |
RetCode: 0 |
Status: OK |
CheckDir: spatialLIBD.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:spatialLIBD.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings spatialLIBD_1.21.6.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/home/biocbuild/bbs-3.22-data-experiment/meat/spatialLIBD.Rcheck’ * using R version 4.5.1 Patched (2025-08-23 r88802) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 * running under: Ubuntu 24.04.3 LTS * using session charset: UTF-8 * checking for file ‘spatialLIBD/DESCRIPTION’ ... OK * this is package ‘spatialLIBD’ version ‘1.21.6’ * package encoding: UTF-8 * checking package namespace information ... OK * checking package dependencies ... INFO Imports includes 36 non-default packages. Importing from so many packages makes the package vulnerable to any of them becoming unavailable. Move as many as possible to Suggests and use conditionally. * checking if this is a source package ... OK * checking if there is a namespace ... OK * checking for hidden files and directories ... OK * checking for portable file names ... OK * checking for sufficient/correct file permissions ... OK * checking whether package ‘spatialLIBD’ can be installed ... OK * checking installed package size ... 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 cross-references ... NOTE Found the following Rd file(s) with Rd \link{} targets missing package anchors: check_sce.Rd: SingleCellExperiment-class check_sce_layer.Rd: SingleCellExperiment-class fetch_data.Rd: SingleCellExperiment-class layer_boxplot.Rd: SingleCellExperiment-class run_app.Rd: SingleCellExperiment-class sce_to_spe.Rd: SingleCellExperiment-class sig_genes_extract.Rd: SingleCellExperiment-class sig_genes_extract_all.Rd: SingleCellExperiment-class Please provide package anchors for all Rd \link{} targets not in the package itself and the base packages. * 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 contents of ‘data’ directory ... OK * checking data for non-ASCII characters ... OK * checking LazyData ... OK * checking data for ASCII and uncompressed saves ... OK * checking files in ‘vignettes’ ... OK * checking examples ... OK Examples with CPU (user + system) or elapsed time > 5s user system elapsed vis_gene 28.930 3.755 33.734 vis_clus 24.209 3.874 35.619 add_images 22.527 2.916 26.234 img_update_all 21.187 2.174 22.335 add_qc_metrics 18.397 2.499 22.168 cluster_export 17.600 2.757 21.540 add_key 17.850 2.491 21.077 vis_grid_clus 17.093 3.092 21.272 vis_grid_gene 17.337 2.691 20.953 vis_image 16.629 2.462 19.864 cluster_import 16.745 2.232 19.871 vis_gene_p 15.987 2.255 19.409 vis_clus_p 15.723 2.228 24.080 check_spe 15.346 2.045 18.384 img_update 15.309 2.071 18.302 img_edit 15.330 2.033 17.875 geom_spatial 14.971 1.622 17.477 frame_limits 14.629 1.755 17.069 sce_to_spe 14.423 1.618 16.760 gene_set_enrichment_plot 8.382 1.040 9.872 layer_stat_cor_plot 4.844 0.724 5.923 * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘testthat.R’ OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... OK * checking PDF version of manual ... OK * DONE Status: 1 NOTE See ‘/home/biocbuild/bbs-3.22-data-experiment/meat/spatialLIBD.Rcheck/00check.log’ for details.
spatialLIBD.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD INSTALL spatialLIBD ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/bbs-3.22-bioc/R/site-library’ * installing *source* package ‘spatialLIBD’ ... ** this is package ‘spatialLIBD’ version ‘1.21.6’ ** using staged installation ** R ** data *** moving datasets to lazyload DB ** inst ** byte-compile and prepare package for lazy loading ** help *** installing help indices *** copying figures ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (spatialLIBD)
spatialLIBD.Rcheck/tests/testthat.Rout
R version 4.5.1 Patched (2025-08-23 r88802) -- "Great Square Root" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(testthat) > library(spatialLIBD) Loading required package: SpatialExperiment Loading required package: SingleCellExperiment Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats Attaching package: 'MatrixGenerics' The following objects are masked from 'package:matrixStats': colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars Loading required package: GenomicRanges Loading required package: stats4 Loading required package: BiocGenerics Loading required package: generics Attaching package: 'generics' The following objects are masked from 'package:base': as.difftime, as.factor, as.ordered, intersect, is.element, setdiff, setequal, union Attaching package: 'BiocGenerics' The following objects are masked from 'package:stats': IQR, mad, sd, var, xtabs The following objects are masked from 'package:base': Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, get, grep, grepl, is.unsorted, lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind, rownames, sapply, saveRDS, table, tapply, unique, unsplit, which.max, which.min Loading required package: S4Vectors Attaching package: 'S4Vectors' The following object is masked from 'package:utils': findMatches The following objects are masked from 'package:base': I, expand.grid, unname Loading required package: IRanges Loading required package: Seqinfo Loading required package: Biobase Welcome to Bioconductor Vignettes contain introductory material; view with 'browseVignettes()'. To cite Bioconductor, see 'citation("Biobase")', and for packages 'citation("pkgname")'. Attaching package: 'Biobase' The following object is masked from 'package:MatrixGenerics': rowMedians The following objects are masked from 'package:matrixStats': anyMissing, rowMedians > > test_check("spatialLIBD") rgstr_> ## Ensure reproducibility of example data rgstr_> set.seed(20220907) rgstr_> ## Generate example data rgstr_> sce <- scuttle::mockSCE() rgstr_> ## Add some sample IDs rgstr_> sce$sample_id <- sample(LETTERS[1:5], ncol(sce), replace = TRUE) rgstr_> ## Add a sample-level covariate: age rgstr_> ages <- rnorm(5, mean = 20, sd = 4) rgstr_> names(ages) <- LETTERS[1:5] rgstr_> sce$age <- ages[sce$sample_id] rgstr_> ## Add gene-level information rgstr_> rowData(sce)$gene_id <- paste0("ENSG", seq_len(nrow(sce))) rgstr_> rowData(sce)$gene_name <- paste0("gene", seq_len(nrow(sce))) rgstr_> ## Pseudo-bulk by Cell Cycle rgstr_> sce_pseudo <- registration_pseudobulk( rgstr_+ sce, rgstr_+ var_registration = "Cell_Cycle", rgstr_+ var_sample_id = "sample_id", rgstr_+ covars = c("age"), rgstr_+ min_ncells = NULL rgstr_+ ) rgstr_> colData(sce_pseudo) DataFrame with 20 rows and 9 columns Mutation_Status Cell_Cycle Treatment sample_id age <character> <character> <character> <character> <numeric> A_G0 NA G0 NA A 19.1872 B_G0 NA G0 NA B 25.3496 C_G0 NA G0 NA C 24.1802 D_G0 NA G0 NA D 15.5211 E_G0 NA G0 NA E 20.9701 ... ... ... ... ... ... A_S NA S NA A 19.1872 B_S NA S NA B 25.3496 C_S NA S NA C 24.1802 D_S NA S NA D 15.5211 E_S NA S NA E 20.9701 registration_variable registration_sample_id ncells pseudo_sum_umi <character> <character> <integer> <numeric> A_G0 G0 A 8 2946915 B_G0 G0 B 13 4922867 C_G0 G0 C 9 3398888 D_G0 G0 D 7 2630651 E_G0 G0 E 10 3761710 ... ... ... ... ... A_S S A 12 4516334 B_S S B 8 2960685 C_S S C 7 2595774 D_S S D 14 5233560 E_S S E 11 4151818 rgstr_> rowData(sce_pseudo) DataFrame with 2000 rows and 3 columns gene_id gene_name gene_search <character> <character> <character> Gene_0001 ENSG1 gene1 gene1; ENSG1 Gene_0002 ENSG2 gene2 gene2; ENSG2 Gene_0003 ENSG3 gene3 gene3; ENSG3 Gene_0004 ENSG4 gene4 gene4; ENSG4 Gene_0005 ENSG5 gene5 gene5; ENSG5 ... ... ... ... Gene_1996 ENSG1996 gene1996 gene1996; ENSG1996 Gene_1997 ENSG1997 gene1997 gene1997; ENSG1997 Gene_1998 ENSG1998 gene1998 gene1998; ENSG1998 Gene_1999 ENSG1999 gene1999 gene1999; ENSG1999 Gene_2000 ENSG2000 gene2000 gene2000; ENSG2000 rgstr_> ## Ensure reproducibility of example data rgstr_> set.seed(20220907) rgstr_> ## Generate example data rgstr_> sce <- scuttle::mockSCE() rgstr_> ## Add some sample IDs rgstr_> sce$sample_id <- sample(LETTERS[1:5], ncol(sce), replace = TRUE) rgstr_> ## Add a sample-level covariate: age rgstr_> ages <- rnorm(5, mean = 20, sd = 4) rgstr_> names(ages) <- LETTERS[1:5] rgstr_> sce$age <- ages[sce$sample_id] rgstr_> ## Add gene-level information rgstr_> rowData(sce)$gene_id <- paste0("ENSG", seq_len(nrow(sce))) rgstr_> rowData(sce)$gene_name <- paste0("gene", seq_len(nrow(sce))) rgstr_> ## Pseudo-bulk by Cell Cycle rgstr_> sce_pseudo <- registration_pseudobulk( rgstr_+ sce, rgstr_+ var_registration = "Cell_Cycle", rgstr_+ var_sample_id = "sample_id", rgstr_+ covars = c("age"), rgstr_+ min_ncells = NULL rgstr_+ ) rgstr_> colData(sce_pseudo) DataFrame with 20 rows and 9 columns Mutation_Status Cell_Cycle Treatment sample_id age <character> <character> <character> <character> <numeric> A_G0 NA G0 NA A 19.1872 B_G0 NA G0 NA B 25.3496 C_G0 NA G0 NA C 24.1802 D_G0 NA G0 NA D 15.5211 E_G0 NA G0 NA E 20.9701 ... ... ... ... ... ... A_S NA S NA A 19.1872 B_S NA S NA B 25.3496 C_S NA S NA C 24.1802 D_S NA S NA D 15.5211 E_S NA S NA E 20.9701 registration_variable registration_sample_id ncells pseudo_sum_umi <character> <character> <integer> <numeric> A_G0 G0 A 8 2946915 B_G0 G0 B 13 4922867 C_G0 G0 C 9 3398888 D_G0 G0 D 7 2630651 E_G0 G0 E 10 3761710 ... ... ... ... ... A_S S A 12 4516334 B_S S B 8 2960685 C_S S C 7 2595774 D_S S D 14 5233560 E_S S E 11 4151818 rgstr_> rowData(sce_pseudo) DataFrame with 2000 rows and 3 columns gene_id gene_name gene_search <character> <character> <character> Gene_0001 ENSG1 gene1 gene1; ENSG1 Gene_0002 ENSG2 gene2 gene2; ENSG2 Gene_0003 ENSG3 gene3 gene3; ENSG3 Gene_0004 ENSG4 gene4 gene4; ENSG4 Gene_0005 ENSG5 gene5 gene5; ENSG5 ... ... ... ... Gene_1996 ENSG1996 gene1996 gene1996; ENSG1996 Gene_1997 ENSG1997 gene1997 gene1997; ENSG1997 Gene_1998 ENSG1998 gene1998 gene1998; ENSG1998 Gene_1999 ENSG1999 gene1999 gene1999; ENSG1999 Gene_2000 ENSG2000 gene2000 gene2000; ENSG2000 rgst__> example("registration_model", package = "spatialLIBD") rgstr_> example("registration_pseudobulk", package = "spatialLIBD") rgstr_> ## Ensure reproducibility of example data rgstr_> set.seed(20220907) rgstr_> ## Generate example data rgstr_> sce <- scuttle::mockSCE() rgstr_> ## Add some sample IDs rgstr_> sce$sample_id <- sample(LETTERS[1:5], ncol(sce), replace = TRUE) rgstr_> ## Add a sample-level covariate: age rgstr_> ages <- rnorm(5, mean = 20, sd = 4) rgstr_> names(ages) <- LETTERS[1:5] rgstr_> sce$age <- ages[sce$sample_id] rgstr_> ## Add gene-level information rgstr_> rowData(sce)$gene_id <- paste0("ENSG", seq_len(nrow(sce))) rgstr_> rowData(sce)$gene_name <- paste0("gene", seq_len(nrow(sce))) rgstr_> ## Pseudo-bulk by Cell Cycle rgstr_> sce_pseudo <- registration_pseudobulk( rgstr_+ sce, rgstr_+ var_registration = "Cell_Cycle", rgstr_+ var_sample_id = "sample_id", rgstr_+ covars = c("age"), rgstr_+ min_ncells = NULL rgstr_+ ) rgstr_> colData(sce_pseudo) DataFrame with 20 rows and 9 columns Mutation_Status Cell_Cycle Treatment sample_id age <character> <character> <character> <character> <numeric> A_G0 NA G0 NA A 19.1872 B_G0 NA G0 NA B 25.3496 C_G0 NA G0 NA C 24.1802 D_G0 NA G0 NA D 15.5211 E_G0 NA G0 NA E 20.9701 ... ... ... ... ... ... A_S NA S NA A 19.1872 B_S NA S NA B 25.3496 C_S NA S NA C 24.1802 D_S NA S NA D 15.5211 E_S NA S NA E 20.9701 registration_variable registration_sample_id ncells pseudo_sum_umi <character> <character> <integer> <numeric> A_G0 G0 A 8 2946915 B_G0 G0 B 13 4922867 C_G0 G0 C 9 3398888 D_G0 G0 D 7 2630651 E_G0 G0 E 10 3761710 ... ... ... ... ... A_S S A 12 4516334 B_S S B 8 2960685 C_S S C 7 2595774 D_S S D 14 5233560 E_S S E 11 4151818 rgstr_> rowData(sce_pseudo) DataFrame with 2000 rows and 3 columns gene_id gene_name gene_search <character> <character> <character> Gene_0001 ENSG1 gene1 gene1; ENSG1 Gene_0002 ENSG2 gene2 gene2; ENSG2 Gene_0003 ENSG3 gene3 gene3; ENSG3 Gene_0004 ENSG4 gene4 gene4; ENSG4 Gene_0005 ENSG5 gene5 gene5; ENSG5 ... ... ... ... Gene_1996 ENSG1996 gene1996 gene1996; ENSG1996 Gene_1997 ENSG1997 gene1997 gene1997; ENSG1997 Gene_1998 ENSG1998 gene1998 gene1998; ENSG1998 Gene_1999 ENSG1999 gene1999 gene1999; ENSG1999 Gene_2000 ENSG2000 gene2000 gene2000; ENSG2000 rgstr_> registration_mod <- registration_model(sce_pseudo, "age") rgstr_> head(registration_mod) registration_variableG0 registration_variableG1 registration_variableG2M A_G0 1 0 0 B_G0 1 0 0 C_G0 1 0 0 D_G0 1 0 0 E_G0 1 0 0 A_G1 0 1 0 registration_variableS age A_G0 0 19.18719 B_G0 0 25.34965 C_G0 0 24.18019 D_G0 0 15.52107 E_G0 0 20.97006 A_G1 0 19.18719 rgst__> block_cor <- registration_block_cor(sce_pseudo, registration_mod) [ FAIL 0 | WARN 0 | SKIP 0 | PASS 47 ] > > proc.time() user system elapsed 118.429 11.442 134.537
spatialLIBD.Rcheck/spatialLIBD-Ex.timings
name | user | system | elapsed | |
add10xVisiumAnalysis | 0 | 0 | 0 | |
add_images | 22.527 | 2.916 | 26.234 | |
add_key | 17.850 | 2.491 | 21.077 | |
add_qc_metrics | 18.397 | 2.499 | 22.168 | |
annotate_registered_clusters | 1.325 | 0.126 | 1.670 | |
check_modeling_results | 1.190 | 0.090 | 1.496 | |
check_sce | 3.481 | 0.215 | 3.884 | |
check_sce_layer | 1.587 | 0.239 | 2.006 | |
check_spe | 15.346 | 2.045 | 18.384 | |
cluster_export | 17.600 | 2.757 | 21.540 | |
cluster_import | 16.745 | 2.232 | 19.871 | |
enough_ram | 0.002 | 0.006 | 0.009 | |
fetch_data | 1.287 | 0.150 | 1.679 | |
frame_limits | 14.629 | 1.755 | 17.069 | |
gene_set_enrichment | 1.411 | 0.275 | 1.904 | |
gene_set_enrichment_plot | 8.382 | 1.040 | 9.872 | |
geom_spatial | 14.971 | 1.622 | 17.477 | |
get_colors | 1.287 | 0.135 | 1.644 | |
img_edit | 15.330 | 2.033 | 17.875 | |
img_update | 15.309 | 2.071 | 18.302 | |
img_update_all | 21.187 | 2.174 | 22.335 | |
layer_boxplot | 3.453 | 0.325 | 4.208 | |
layer_stat_cor | 1.406 | 0.167 | 1.752 | |
layer_stat_cor_plot | 4.844 | 0.724 | 5.923 | |
locate_images | 0 | 0 | 0 | |
read10xVisiumAnalysis | 0 | 0 | 0 | |
read10xVisiumWrapper | 0 | 0 | 0 | |
registration_block_cor | 2.875 | 0.153 | 3.031 | |
registration_model | 0.792 | 0.020 | 0.812 | |
registration_pseudobulk | 0.683 | 0.012 | 0.696 | |
registration_stats_anova | 2.999 | 0.074 | 3.074 | |
registration_stats_enrichment | 3.135 | 0.084 | 3.219 | |
registration_stats_pairwise | 2.937 | 0.020 | 2.957 | |
registration_wrapper | 4.852 | 0.081 | 4.933 | |
run_app | 0 | 0 | 0 | |
sce_to_spe | 14.423 | 1.618 | 16.760 | |
sig_genes_extract | 2.777 | 0.388 | 3.560 | |
sig_genes_extract_all | 3.721 | 0.525 | 4.676 | |
sort_clusters | 0.009 | 0.000 | 0.008 | |
vis_clus | 24.209 | 3.874 | 35.619 | |
vis_clus_p | 15.723 | 2.228 | 24.080 | |
vis_gene | 28.930 | 3.755 | 33.734 | |
vis_gene_p | 15.987 | 2.255 | 19.409 | |
vis_grid_clus | 17.093 | 3.092 | 21.272 | |
vis_grid_gene | 17.337 | 2.691 | 20.953 | |
vis_image | 16.629 | 2.462 | 19.864 | |