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This page was generated on 2024-03-26 14:51:27 -0400 (Tue, 26 Mar 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_64R Under development (unstable) (2024-03-18 r86148) -- "Unsuffered Consequences" 4698
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Package 374/425HostnameOS / ArchINSTALLBUILDCHECK
spatialLIBD 1.15.1  (landing page)
Leonardo Collado-Torres
Snapshot Date: 2024-03-26 07:30:03 -0400 (Tue, 26 Mar 2024)
git_url: https://git.bioconductor.org/packages/spatialLIBD
git_branch: devel
git_last_commit: 079a0f8
git_last_commit_date: 2024-03-12 14:19:14 -0400 (Tue, 12 Mar 2024)
nebbiolo1Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published

CHECK results for spatialLIBD on nebbiolo1


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: 1.15.1
Command: /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD check --install=check:spatialLIBD.install-out.txt --library=/home/biocbuild/bbs-3.19-bioc/R/site-library --timings spatialLIBD_1.15.1.tar.gz
StartedAt: 2024-03-26 12:20:39 -0400 (Tue, 26 Mar 2024)
EndedAt: 2024-03-26 12:58:21 -0400 (Tue, 26 Mar 2024)
EllapsedTime: 2262.9 seconds
RetCode: 0
Status:   OK  
CheckDir: spatialLIBD.Rcheck
Warnings: 0

Command output

##############################################################################
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###
### Running command:
###
###   /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD check --install=check:spatialLIBD.install-out.txt --library=/home/biocbuild/bbs-3.19-bioc/R/site-library --timings spatialLIBD_1.15.1.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.19-data-experiment/meat/spatialLIBD.Rcheck’
* using R Under development (unstable) (2024-03-18 r86148)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
    GNU Fortran (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
* running under: Ubuntu 22.04.4 LTS
* using session charset: UTF-8
* checking for file ‘spatialLIBD/DESCRIPTION’ ... OK
* this is package ‘spatialLIBD’ version ‘1.15.1’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for 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 ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking 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       41.787  2.243  44.646
vis_clus       31.416  1.867  34.015
add_images     28.319  1.783  32.492
img_update_all 26.673  1.710  28.696
vis_grid_gene  25.809  2.271  28.761
vis_grid_clus  25.202  2.147  27.942
vis_gene_p     25.269  1.184  27.896
vis_clus_p     24.733  1.554  26.851
cluster_import 24.295  1.797  27.334
check_spe      24.508  1.282  26.348
geom_spatial   23.496  1.866  25.904
img_edit       22.832  1.864  25.257
add_key        23.053  1.477  25.132
frame_limits   22.641  1.761  25.109
img_update     22.438  1.895  25.038
cluster_export 22.826  1.257  24.674
sce_to_spe     22.408  1.236  24.413
* 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: OK


Installation output

spatialLIBD.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD INSTALL spatialLIBD
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.19-bioc/R/site-library’
* installing *source* package ‘spatialLIBD’ ...
** 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 Under development (unstable) (2024-03-18 r86148) -- "Unsuffered Consequences"
Copyright (C) 2024 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

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, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, setdiff, table, tapply,
    union, 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: GenomeInfoDb
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
rgstr_> sce_pseudo <- registration_pseudobulk(sce, "Cell_Cycle", "sample_id", c("age"), min_ncells = NULL)

rgstr_> colData(sce_pseudo)
DataFrame with 20 rows and 8 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
               <character>            <character> <integer>
A_G0                    G0                      A         8
B_G0                    G0                      B        13
C_G0                    G0                      C         9
D_G0                    G0                      D         7
E_G0                    G0                      E        10
...                    ...                    ...       ...
A_S                      S                      A        12
B_S                      S                      B         8
C_S                      S                      C         7
D_S                      S                      D        14
E_S                      S                      E        11

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
rgstr_> sce_pseudo <- registration_pseudobulk(sce, "Cell_Cycle", "sample_id", c("age"), min_ncells = NULL)

rgstr_> colData(sce_pseudo)
DataFrame with 20 rows and 8 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
               <character>            <character> <integer>
A_G0                    G0                      A         8
B_G0                    G0                      B        13
C_G0                    G0                      C         9
D_G0                    G0                      D         7
E_G0                    G0                      E        10
...                    ...                    ...       ...
A_S                      S                      A        12
B_S                      S                      B         8
C_S                      S                      C         7
D_S                      S                      D        14
E_S                      S                      E        11

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
rgstr_> sce_pseudo <- registration_pseudobulk(sce, "Cell_Cycle", "sample_id", c("age"), min_ncells = NULL)

rgstr_> colData(sce_pseudo)
DataFrame with 20 rows and 8 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
               <character>            <character> <integer>
A_G0                    G0                      A         8
B_G0                    G0                      B        13
C_G0                    G0                      C         9
D_G0                    G0                      D         7
E_G0                    G0                      E        10
...                    ...                    ...       ...
A_S                      S                      A        12
B_S                      S                      B         8
C_S                      S                      C         7
D_S                      S                      D        14
E_S                      S                      E        11

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 12 ]
> 
> proc.time()
   user  system elapsed 
 25.100   1.314  26.557 

Example timings

spatialLIBD.Rcheck/spatialLIBD-Ex.timings

nameusersystemelapsed
add10xVisiumAnalysis000
add_images28.319 1.78332.492
add_key23.053 1.47725.132
annotate_registered_clusters1.1760.1041.738
check_modeling_results1.1170.1001.334
check_sce3.4500.1083.762
check_sce_layer1.2350.0681.382
check_spe24.508 1.28226.348
cluster_export22.826 1.25724.674
cluster_import24.295 1.79727.334
enough_ram0.0060.0040.010
fetch_data1.2570.1371.502
frame_limits22.641 1.76125.109
gene_set_enrichment1.3990.0961.621
gene_set_enrichment_plot1.3980.1201.624
geom_spatial23.496 1.86625.904
get_colors1.2920.1201.662
img_edit22.832 1.86425.257
img_update22.438 1.89525.038
img_update_all26.673 1.71028.696
layer_boxplot3.1530.2483.802
layer_matrix_plot0.010.000.01
layer_stat_cor1.1580.0601.495
layer_stat_cor_plot1.2710.0561.541
locate_images000
read10xVisiumAnalysis000
read10xVisiumWrapper0.0000.0000.001
registration_block_cor3.9130.2624.176
registration_model0.8930.0010.892
registration_pseudobulk0.7950.0070.802
registration_stats_anova3.0530.0003.053
registration_stats_enrichment3.2140.0123.226
registration_stats_pairwise3.0630.0033.067
registration_wrapper4.5420.0094.550
run_app0.0010.0000.002
sce_to_spe22.408 1.23624.413
sig_genes_extract2.6990.7923.776
sig_genes_extract_all3.2090.1433.783
sort_clusters0.0030.0010.003
vis_clus31.416 1.86734.015
vis_clus_p24.733 1.55426.851
vis_gene41.787 2.24344.646
vis_gene_p25.269 1.18427.896
vis_grid_clus25.202 2.14727.942
vis_grid_gene25.809 2.27128.761