Leonardo Collado-Torres
| Snapshot Date: 2025-07-10 07:30 -0400 (Thu, 10 Jul 2025) |
| git_url: https://git.bioconductor.org/packages/spatialLIBD |
| git_branch: devel |
| git_last_commit: aff00db |
| git_last_commit_date: 2025-05-09 11:38:42 -0400 (Fri, 09 May 2025) |
| Back to Build/check report for BioC 3.22 experimental data |
|
This page was generated on 2025-07-10 15:41 -0400 (Thu, 10 Jul 2025).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo2 | Linux (Ubuntu 24.04.1 LTS) | x86_64 | 4.5.1 (2025-06-13) -- "Great Square Root" | 4781 |
| 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 377/429 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | ||||||||
| spatialLIBD 1.21.5 (landing page) Leonardo Collado-Torres
| nebbiolo2 | Linux (Ubuntu 24.04.1 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: 1.21.5 |
| 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_1.21.5.tar.gz |
| StartedAt: 2025-07-10 13:03:58 -0400 (Thu, 10 Jul 2025) |
| EndedAt: 2025-07-10 13:22:51 -0400 (Thu, 10 Jul 2025) |
| EllapsedTime: 1133.7 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: spatialLIBD.Rcheck |
| Warnings: 0 |
##############################################################################
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###
### 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_1.21.5.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/home/biocbuild/bbs-3.22-data-experiment/meat/spatialLIBD.Rcheck’
* using R version 4.5.1 (2025-06-13)
* 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 24.04.2 LTS
* using session charset: UTF-8
* checking for file ‘spatialLIBD/DESCRIPTION’ ... OK
* this is package ‘spatialLIBD’ version ‘1.21.5’
* 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
read10xVisiumWrapper.Rd: SpatialExperiment-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 27.584 3.745 31.834
vis_clus 20.885 2.954 24.334
add_images 20.079 1.797 23.438
img_update_all 19.084 2.356 21.554
vis_grid_gene 16.350 2.382 19.234
vis_grid_clus 16.085 2.512 19.097
vis_clus_p 16.130 2.361 18.989
add_qc_metrics 16.317 2.007 18.492
cluster_import 16.011 1.903 18.528
add_key 16.001 1.896 18.387
cluster_export 15.682 2.131 18.445
vis_image 15.512 1.788 17.905
vis_gene_p 14.925 1.538 16.963
img_update 14.490 1.861 16.958
img_edit 14.474 1.853 16.784
frame_limits 14.208 1.888 16.569
geom_spatial 14.375 1.653 16.527
check_spe 14.204 1.623 16.317
sce_to_spe 14.137 1.400 16.528
gene_set_enrichment_plot 8.075 1.148 9.538
layer_stat_cor_plot 4.415 0.826 5.473
* 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 ‘1.21.5’ ** 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 (2025-06-13) -- "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$sample_id]
rgstr_> ## Add gene-level information
rgstr_> rowData(sce)$ensembl <- paste0("ENSG", seq_len(nrow(sce)))
rgstr_> rowData(sce)$gene_name <- paste0("gene", seq_len(nrow(sce)))
rgstr_> ## Pseudo-bulk by Cell Cycle
rgstr_> sce_pseudo <- registration_pseudobulk(
rgstr_+ sce,
rgstr_+ var_registration = "Cell_Cycle",
rgstr_+ var_sample_id = "sample_id",
rgstr_+ covars = c("age"),
rgstr_+ min_ncells = NULL
rgstr_+ )
rgstr_> colData(sce_pseudo)
DataFrame with 20 rows and 9 columns
Mutation_Status Cell_Cycle Treatment sample_id age
<character> <character> <character> <character> <numeric>
A_G0 NA G0 NA A 19.1872
B_G0 NA G0 NA B 25.3496
C_G0 NA G0 NA C 24.1802
D_G0 NA G0 NA D 15.5211
E_G0 NA G0 NA E 20.9701
... ... ... ... ... ...
A_S NA S NA A 19.1872
B_S NA S NA B 25.3496
C_S NA S NA C 24.1802
D_S NA S NA D 15.5211
E_S NA S NA E 20.9701
registration_variable registration_sample_id ncells pseudo_sum_umi
<character> <character> <integer> <numeric>
A_G0 G0 A 8 2946915
B_G0 G0 B 13 4922867
C_G0 G0 C 9 3398888
D_G0 G0 D 7 2630651
E_G0 G0 E 10 3761710
... ... ... ... ...
A_S S A 12 4516334
B_S S B 8 2960685
C_S S C 7 2595774
D_S S D 14 5233560
E_S S E 11 4151818
rgstr_> ## Ensure reproducibility of example data
rgstr_> set.seed(20220907)
rgstr_> ## Generate example data
rgstr_> sce <- scuttle::mockSCE()
rgstr_> ## Add some sample IDs
rgstr_> sce$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)$ensembl <- paste0("ENSG", seq_len(nrow(sce)))
rgstr_> rowData(sce)$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
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$sample_id]
rgstr_> ## Add gene-level information
rgstr_> rowData(sce)$ensembl <- 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_> 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
110.066 11.148 124.498
spatialLIBD.Rcheck/spatialLIBD-Ex.timings
| name | user | system | elapsed | |
| add10xVisiumAnalysis | 0 | 0 | 0 | |
| add_images | 20.079 | 1.797 | 23.438 | |
| add_key | 16.001 | 1.896 | 18.387 | |
| add_qc_metrics | 16.317 | 2.007 | 18.492 | |
| annotate_registered_clusters | 1.128 | 0.115 | 1.411 | |
| check_modeling_results | 1.096 | 0.070 | 1.329 | |
| check_sce | 3.416 | 0.262 | 3.831 | |
| check_sce_layer | 1.209 | 0.111 | 1.471 | |
| check_spe | 14.204 | 1.623 | 16.317 | |
| cluster_export | 15.682 | 2.131 | 18.445 | |
| cluster_import | 16.011 | 1.903 | 18.528 | |
| enough_ram | 0.006 | 0.003 | 0.009 | |
| fetch_data | 1.202 | 0.126 | 1.502 | |
| frame_limits | 14.208 | 1.888 | 16.569 | |
| gene_set_enrichment | 1.229 | 0.130 | 1.525 | |
| gene_set_enrichment_plot | 8.075 | 1.148 | 9.538 | |
| geom_spatial | 14.375 | 1.653 | 16.527 | |
| get_colors | 1.187 | 0.088 | 1.442 | |
| img_edit | 14.474 | 1.853 | 16.784 | |
| img_update | 14.490 | 1.861 | 16.958 | |
| img_update_all | 19.084 | 2.356 | 21.554 | |
| layer_boxplot | 3.212 | 0.247 | 3.778 | |
| layer_stat_cor | 1.145 | 0.050 | 1.346 | |
| layer_stat_cor_plot | 4.415 | 0.826 | 5.473 | |
| locate_images | 0 | 0 | 0 | |
| read10xVisiumAnalysis | 0.001 | 0.000 | 0.000 | |
| read10xVisiumWrapper | 0 | 0 | 0 | |
| registration_block_cor | 2.750 | 0.106 | 2.856 | |
| registration_model | 0.690 | 0.021 | 0.711 | |
| registration_pseudobulk | 0.638 | 0.019 | 0.656 | |
| registration_stats_anova | 2.915 | 0.138 | 3.053 | |
| registration_stats_enrichment | 3.081 | 0.039 | 3.120 | |
| registration_stats_pairwise | 2.927 | 0.029 | 2.956 | |
| registration_wrapper | 4.420 | 0.113 | 4.533 | |
| run_app | 0.000 | 0.000 | 0.001 | |
| sce_to_spe | 14.137 | 1.400 | 16.528 | |
| sig_genes_extract | 2.592 | 0.305 | 3.214 | |
| sig_genes_extract_all | 3.820 | 0.350 | 4.735 | |
| sort_clusters | 0.008 | 0.004 | 0.013 | |
| vis_clus | 20.885 | 2.954 | 24.334 | |
| vis_clus_p | 16.130 | 2.361 | 18.989 | |
| vis_gene | 27.584 | 3.745 | 31.834 | |
| vis_gene_p | 14.925 | 1.538 | 16.963 | |
| vis_grid_clus | 16.085 | 2.512 | 19.097 | |
| vis_grid_gene | 16.350 | 2.382 | 19.234 | |
| vis_image | 15.512 | 1.788 | 17.905 | |