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This page was generated on 2025-10-14 15:41 -0400 (Tue, 14 Oct 2025).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo2Linux (Ubuntu 24.04.3 LTS)x86_644.5.1 Patched (2025-08-23 r88802) -- "Great Square Root" 4865
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Package 382/434HostnameOS / ArchINSTALLBUILDCHECK
spatialLIBD 1.21.6  (landing page)
Leonardo Collado-Torres
Snapshot Date: 2025-10-14 07:30 -0400 (Tue, 14 Oct 2025)
git_url: https://git.bioconductor.org/packages/spatialLIBD
git_branch: devel
git_last_commit: b7bae6f
git_last_commit_date: 2025-09-26 09:40:00 -0400 (Fri, 26 Sep 2025)
nebbiolo2Linux (Ubuntu 24.04.3 LTS) / x86_64  OK    OK    ERROR  

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: 1.21.6
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: 2025-10-14 13:05:10 -0400 (Tue, 14 Oct 2025)
EndedAt: 2025-10-14 13:25:41 -0400 (Tue, 14 Oct 2025)
EllapsedTime: 1230.8 seconds
RetCode: 1
Status:   ERROR  
CheckDir: spatialLIBD.Rcheck
Warnings: NA

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_1.21.6.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                 29.281  5.017  36.629
vis_clus                 23.941  3.948  31.929
add_images               22.669  3.611  27.167
img_update_all           21.889  2.227  22.855
add_key                  18.120  3.304  22.257
vis_grid_gene            18.160  3.216  22.291
vis_grid_clus            17.011  3.796  21.630
add_qc_metrics           17.486  2.298  20.056
cluster_export           16.521  2.794  20.041
cluster_import           16.782  2.502  20.252
vis_image                16.697  2.537  19.973
img_edit                 15.776  2.165  18.447
vis_clus_p               15.413  2.366  18.812
img_update               15.405  2.365  18.387
vis_gene_p               15.532  2.145  18.521
frame_limits             14.865  2.691  18.431
geom_spatial             15.131  1.975  17.887
check_spe                14.650  2.088  17.421
sce_to_spe               14.795  1.796  17.547
gene_set_enrichment_plot  9.517  1.104  11.019
layer_stat_cor_plot       4.837  0.755   6.018
* 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 ... ERROR
Error(s) in re-building vignettes:
--- re-building ‘TenX_data_download.Rmd’ using rmarkdown
--- finished re-building ‘TenX_data_download.Rmd’

--- re-building ‘guide_to_spatial_registration.Rmd’ using rmarkdown
--- finished re-building ‘guide_to_spatial_registration.Rmd’

--- re-building ‘multi_gene_plots.Rmd’ using rmarkdown
Failed with error:  'there is no package called 'GenomeInfoDb''

Quitting from multi_gene_plots.Rmd:81-85 [setup]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
<error/rlang_error>
Error in `.requirePackage()`:
! unable to load required package 'GenomeInfoDb'
---
Backtrace:
     ▆
  1. ├─tools:::.buildOneVignette(...)
  2. │ ├─base::tryCatch(...)
  3. │ │ └─base (local) tryCatchList(expr, classes, parentenv, handlers)
  4. │ │   └─base (local) tryCatchOne(expr, names, parentenv, handlers[[1L]])
  5. │ │     └─base (local) doTryCatch(return(expr), name, parentenv, handler)
  6. │ └─engine$weave(file, quiet = quiet, encoding = enc)
  7. │   └─knitr:::vweave_rmarkdown(...)
  8. │     └─rmarkdown::render(...)
  9. │       └─knitr::knit(knit_input, knit_output, envir = envir, quiet = quiet)
 10. │         └─knitr:::process_file(text, output)
 11. │           ├─xfun:::handle_error(...)
 12. │           ├─base::withCallingHandlers(...)
 13. │           └─knitr:::process_group(group)
 14. │             └─knitr:::call_block(x)
 15. │               └─knitr:::block_exec(params)
 16. │                 └─knitr:::eng_r(options)
 17. │                   ├─knitr:::in_input_dir(...)
 18. │                   │ └─knitr:::in_dir(input_dir(), expr)
 19. │                   └─knitr (local) evaluate(...)
 20. │                     └─evaluate::evaluate(...)
 21. │                       ├─base::withRestarts(...)
 22. │                       │ └─base (local) withRestartList(expr, restarts)
 23. │                       │   ├─base (local) withOneRestart(withRestartList(expr, restarts[-nr]), restarts[[nr]])
 24. │                       │   │ └─base (local) doWithOneRestart(return(expr), restart)
 25. │                       │   └─base (local) withRestartList(expr, restarts[-nr])
 26. │                       │     └─base (local) withOneRestart(expr, restarts[[1L]])
 27. │                       │       └─base (local) doWithOneRestart(return(expr), restart)
 28. │                       ├─evaluate:::with_handlers(...)
 29. │                       │ ├─base::eval(call)
 30. │                       │ │ └─base::eval(call)
 31. │                       │ └─base::withCallingHandlers(...)
 32. │                       └─watcher$print_value(ev$value, ev$visible, envir)
 33. │                         ├─base::withVisible(handle_value(handler, value, visible, envir))
 34. │                         └─evaluate:::handle_value(handler, value, visible, envir)
 35. │                           └─handler$value(value, visible)
 36. │                             └─knitr (local) fun(x, options = options)
 37. │                               ├─base::withVisible(knit_print(x, ...))
 38. │                               ├─knitr::knit_print(x, ...)
 39. │                               └─knitr:::knit_print.default(x, ...)
 40. │                                 └─knitr::normal_print(x)
 41. │                                   ├─methods::show(x)
 42. │                                   └─methods::show(x)
 43. │                                     ├─methods::callNextMethod()
 44. │                                     └─SingleCellExperiment (local) .nextMethod(object = object)
 45. │                                       ├─S4Vectors::coolcat("reducedDimNames(%d): %s\n", reducedDimNames(object))
 46. │                                       │ └─base::ifelse(nzchar(vals), vals, "''")
 47. │                                       ├─SingleCellExperiment::reducedDimNames(object)
 48. │                                       └─SingleCellExperiment::reducedDimNames(object)
 49. │                                         └─SingleCellExperiment:::.get_internal_names(...)
 50. │                                           ├─BiocGenerics::updateObject(x)
 51. │                                           └─SingleCellExperiment::updateObject(x)
 52. │                                             ├─methods::callNextMethod()
 53. │                                             └─SummarizedExperiment (local) .nextMethod(object = object)
 54. │                                               ├─BiocGenerics::updateObject(object@rowRanges, ..., verbose = verbose)
 55. │                                               └─GenomicRanges::updateObject(object@rowRanges, ..., verbose = verbose)
 56. │                                                 └─BiocGenerics::updateObject(object@seqinfo, ..., verbose = verbose)
 57. └─methods:::.extendsForS3(`<chr>`)
 58.   └─methods::extends(Class, maybe = FALSE)
 59.     └─methods::getClassDef(class1)
 60.       └─methods:::.requirePackage(package)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

Error: processing vignette 'multi_gene_plots.Rmd' failed with diagnostics:
unable to load required package 'GenomeInfoDb'
--- failed re-building ‘multi_gene_plots.Rmd’

--- re-building ‘spatialLIBD.Rmd’ using rmarkdown
--- finished re-building ‘spatialLIBD.Rmd’

SUMMARY: processing the following file failed:
  ‘multi_gene_plots.Rmd’

Error: Vignette re-building failed.
Execution halted

* checking PDF version of manual ... OK
* DONE

Status: 1 ERROR, 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 ‘1.21.6’
** 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)

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 
120.930  12.764 138.377

Example timings

spatialLIBD.Rcheck/spatialLIBD-Ex.timings

nameusersystemelapsed
add10xVisiumAnalysis000
add_images22.669 3.61127.167
add_key18.120 3.30422.257
add_qc_metrics17.486 2.29820.056
annotate_registered_clusters1.3470.2861.863
check_modeling_results1.2290.2511.743
check_sce3.5550.4164.184
check_sce_layer1.5460.4212.147
check_spe14.650 2.08817.421
cluster_export16.521 2.79420.041
cluster_import16.782 2.50220.252
enough_ram0.0020.0060.007
fetch_data1.3740.1381.730
frame_limits14.865 2.69118.431
gene_set_enrichment1.4160.1361.767
gene_set_enrichment_plot 9.517 1.10411.019
geom_spatial15.131 1.97517.887
get_colors1.3640.1721.785
img_edit15.776 2.16518.447
img_update15.405 2.36518.387
img_update_all21.889 2.22722.855
layer_boxplot3.6240.3224.395
layer_stat_cor1.3350.1371.664
layer_stat_cor_plot4.8370.7556.018
locate_images000
read10xVisiumAnalysis000
read10xVisiumWrapper000
registration_block_cor3.0450.1623.214
registration_model0.8490.0290.878
registration_pseudobulk0.740.010.75
registration_stats_anova3.1140.1253.244
registration_stats_enrichment3.1720.0593.232
registration_stats_pairwise3.0590.0273.087
registration_wrapper4.5310.1134.644
run_app0.0010.0000.000
sce_to_spe14.795 1.79617.547
sig_genes_extract3.2300.4384.088
sig_genes_extract_all3.5810.4624.428
sort_clusters0.0080.0010.008
vis_clus23.941 3.94831.929
vis_clus_p15.413 2.36618.812
vis_gene29.281 5.01736.629
vis_gene_p15.532 2.14518.521
vis_grid_clus17.011 3.79621.630
vis_grid_gene18.160 3.21622.291
vis_image16.697 2.53719.973