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This page was generated on 2025-11-04 15:41 -0500 (Tue, 04 Nov 2025).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 24.04.3 LTS)x86_644.5.1 Patched (2025-08-23 r88802) -- "Great Square Root" 4902
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Package 383/435HostnameOS / ArchINSTALLBUILDCHECK
spatialLIBD 1.22.0  (landing page)
Leonardo Collado-Torres
Snapshot Date: 2025-11-04 07:30 -0500 (Tue, 04 Nov 2025)
git_url: https://git.bioconductor.org/packages/spatialLIBD
git_branch: RELEASE_3_22
git_last_commit: d06d43c
git_last_commit_date: 2025-10-29 10:02:34 -0500 (Wed, 29 Oct 2025)
nebbiolo2Linux (Ubuntu 24.04.3 LTS) / x86_64  OK    OK    OK  YES

CHECK results for spatialLIBD on nebbiolo2

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.

raw results


Summary

Package: spatialLIBD
Version: 1.22.0
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_1.22.0.tar.gz
StartedAt: 2025-11-04 12:59:14 -0500 (Tue, 04 Nov 2025)
EndedAt: 2025-11-04 13:19:22 -0500 (Tue, 04 Nov 2025)
EllapsedTime: 1207.8 seconds
RetCode: 0
Status:   OK  
CheckDir: spatialLIBD.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### 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_1.22.0.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 ‘1.22.0’
* 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                 29.904  2.795  33.989
vis_clus                 22.285  1.975  24.961
add_images               20.282  1.893  23.806
img_update_all           20.252  1.579  22.196
vis_image                17.543  1.837  20.432
frame_limits             16.251  2.715  19.701
vis_grid_gene            16.853  1.851  19.524
add_qc_metrics           16.937  1.611  18.770
vis_grid_clus            16.257  2.136  19.303
vis_clus_p               16.619  1.683  19.030
cluster_import           16.458  1.447  18.840
geom_spatial             16.478  1.419  18.618
cluster_export           16.273  1.415  18.446
add_key                  15.973  1.536  18.347
img_update               15.485  1.552  17.782
vis_gene_p               15.136  1.558  17.470
img_edit                 14.950  1.517  17.230
check_spe                14.525  1.307  16.574
sce_to_spe               14.141  1.293  16.320
gene_set_enrichment_plot  8.478  0.404  16.393
layer_stat_cor_plot       4.802  0.373   5.528
* 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.

Installation output

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 ‘1.22.0’
** 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)

Tests output

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 
117.574   7.985 134.488

Example timings

spatialLIBD.Rcheck/spatialLIBD-Ex.timings

nameusersystemelapsed
add10xVisiumAnalysis000
add_images20.282 1.89323.806
add_key15.973 1.53618.347
add_qc_metrics16.937 1.61118.770
annotate_registered_clusters1.2140.0651.502
check_modeling_results1.2090.0851.467
check_sce3.3540.1163.646
check_sce_layer1.5360.1401.852
check_spe14.525 1.30716.574
cluster_export16.273 1.41518.446
cluster_import16.458 1.44718.840
enough_ram0.0030.0080.010
fetch_data1.2600.0441.545
frame_limits16.251 2.71519.701
gene_set_enrichment1.3240.0751.703
gene_set_enrichment_plot 8.478 0.40416.393
geom_spatial16.478 1.41918.618
get_colors1.4450.0691.755
img_edit14.950 1.51717.230
img_update15.485 1.55217.782
img_update_all20.252 1.57922.196
layer_boxplot3.6590.1634.265
layer_stat_cor1.3050.0431.569
layer_stat_cor_plot4.8020.3735.528
locate_images000
read10xVisiumAnalysis000
read10xVisiumWrapper000
registration_block_cor2.8170.0032.821
registration_model0.7570.0060.763
registration_pseudobulk0.6500.0020.652
registration_stats_anova2.9040.0012.906
registration_stats_enrichment3.0260.0153.042
registration_stats_pairwise2.8640.0102.874
registration_wrapper4.3740.0154.389
run_app000
sce_to_spe14.141 1.29316.320
sig_genes_extract2.6670.1643.421
sig_genes_extract_all3.3640.1123.893
sort_clusters0.0070.0010.007
vis_clus22.285 1.97524.961
vis_clus_p16.619 1.68319.030
vis_gene29.904 2.79533.989
vis_gene_p15.136 1.55817.470
vis_grid_clus16.257 2.13619.303
vis_grid_gene16.853 1.85119.524
vis_image17.543 1.83720.432