Leonardo Collado-Torres
| Snapshot Date: 2026-01-01 07:00 -0500 (Thu, 01 Jan 2026) |
| git_url: https://git.bioconductor.org/packages/spatialLIBD |
| git_branch: devel |
| git_last_commit: 20d2ef8 |
| git_last_commit_date: 2025-10-29 10:02:34 -0500 (Wed, 29 Oct 2025) |
| Back to Build/check report for BioC 3.23 experimental data |
|
This page was generated on 2026-01-01 15:01 -0500 (Thu, 01 Jan 2026).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo1 | Linux (Ubuntu 24.04.3 LTS) | x86_64 | R Under development (unstable) (2025-12-22 r89219) -- "Unsuffered Consequences" | 4808 |
| 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 379/431 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | ||||||||
| spatialLIBD 1.23.0 (landing page) Leonardo Collado-Torres
| nebbiolo1 | Linux (Ubuntu 24.04.3 LTS) / x86_64 | OK | OK | OK | | |||||||
|
To the developers/maintainers of the spatialLIBD package: - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
| Package: spatialLIBD |
| Version: 1.23.0 |
| Command: /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD check --install=check:spatialLIBD.install-out.txt --library=/home/biocbuild/bbs-3.23-bioc/R/site-library --timings spatialLIBD_1.23.0.tar.gz |
| StartedAt: 2026-01-01 13:04:46 -0500 (Thu, 01 Jan 2026) |
| EndedAt: 2026-01-01 13:24:21 -0500 (Thu, 01 Jan 2026) |
| EllapsedTime: 1174.0 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: spatialLIBD.Rcheck |
| Warnings: 0 |
##############################################################################
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###
### Running command:
###
### /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD check --install=check:spatialLIBD.install-out.txt --library=/home/biocbuild/bbs-3.23-bioc/R/site-library --timings spatialLIBD_1.23.0.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/home/biocbuild/bbs-3.23-data-experiment/meat/spatialLIBD.Rcheck’
* using R Under development (unstable) (2025-12-22 r89219)
* 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 ‘1.23.0’
* 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 27.749 2.405 31.055
vis_clus 21.989 1.993 25.141
add_images 20.958 2.068 25.452
img_update_all 19.064 1.827 21.202
add_qc_metrics 16.932 1.648 18.769
vis_image 16.740 1.734 19.317
vis_grid_gene 16.341 1.720 19.138
vis_grid_clus 15.984 1.967 19.267
vis_clus_p 16.244 1.571 22.327
add_key 16.164 1.518 18.504
cluster_export 16.063 1.466 18.635
cluster_import 16.185 1.334 18.604
geom_spatial 14.805 1.989 17.797
frame_limits 14.692 2.083 17.726
img_edit 14.820 1.688 17.562
img_update 14.718 1.656 17.299
vis_gene_p 14.823 1.218 16.908
check_spe 14.420 1.315 16.806
sce_to_spe 14.044 1.410 16.235
gene_set_enrichment_plot 8.252 0.700 9.312
layer_stat_cor_plot 4.269 0.394 5.011
* 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-3.23-data-experiment/meat/spatialLIBD.Rcheck/00check.log’
for details.
spatialLIBD.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.23-bioc/R/bin/R CMD INSTALL spatialLIBD ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/bbs-3.23-bioc/R/site-library’ * installing *source* package ‘spatialLIBD’ ... ** this is package ‘spatialLIBD’ version ‘1.23.0’ ** 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)
spatialLIBD.Rcheck/tests/testthat.Rout
R Under development (unstable) (2025-12-22 r89219) -- "Unsuffered Consequences"
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
112.507 9.989 127.925
spatialLIBD.Rcheck/spatialLIBD-Ex.timings
| name | user | system | elapsed | |
| add10xVisiumAnalysis | 0.001 | 0.000 | 0.000 | |
| add_images | 20.958 | 2.068 | 25.452 | |
| add_key | 16.164 | 1.518 | 18.504 | |
| add_qc_metrics | 16.932 | 1.648 | 18.769 | |
| annotate_registered_clusters | 1.141 | 0.110 | 1.440 | |
| check_modeling_results | 1.158 | 0.120 | 1.449 | |
| check_sce | 3.174 | 0.100 | 3.447 | |
| check_sce_layer | 1.267 | 0.093 | 1.532 | |
| check_spe | 14.420 | 1.315 | 16.806 | |
| cluster_export | 16.063 | 1.466 | 18.635 | |
| cluster_import | 16.185 | 1.334 | 18.604 | |
| enough_ram | 0.002 | 0.007 | 0.009 | |
| fetch_data | 1.228 | 0.078 | 1.494 | |
| frame_limits | 14.692 | 2.083 | 17.726 | |
| gene_set_enrichment | 1.240 | 0.162 | 1.592 | |
| gene_set_enrichment_plot | 8.252 | 0.700 | 9.312 | |
| geom_spatial | 14.805 | 1.989 | 17.797 | |
| get_colors | 1.230 | 0.097 | 1.516 | |
| img_edit | 14.820 | 1.688 | 17.562 | |
| img_update | 14.718 | 1.656 | 17.299 | |
| img_update_all | 19.064 | 1.827 | 21.202 | |
| layer_boxplot | 3.161 | 0.240 | 3.757 | |
| layer_stat_cor | 1.190 | 0.094 | 1.456 | |
| layer_stat_cor_plot | 4.269 | 0.394 | 5.011 | |
| locate_images | 0 | 0 | 0 | |
| read10xVisiumAnalysis | 0.000 | 0.001 | 0.000 | |
| read10xVisiumWrapper | 0.001 | 0.000 | 0.000 | |
| registration_block_cor | 2.788 | 0.024 | 2.812 | |
| registration_model | 0.717 | 0.007 | 0.724 | |
| registration_pseudobulk | 0.658 | 0.004 | 0.662 | |
| registration_stats_anova | 2.882 | 0.013 | 2.895 | |
| registration_stats_enrichment | 2.993 | 0.042 | 3.036 | |
| registration_stats_pairwise | 2.863 | 0.051 | 2.914 | |
| registration_wrapper | 4.352 | 0.073 | 4.425 | |
| run_app | 0.000 | 0.002 | 0.002 | |
| sce_to_spe | 14.044 | 1.410 | 16.235 | |
| sig_genes_extract | 2.581 | 0.773 | 3.715 | |
| sig_genes_extract_all | 3.233 | 0.132 | 3.708 | |
| sort_clusters | 0.008 | 0.001 | 0.009 | |
| vis_clus | 21.989 | 1.993 | 25.141 | |
| vis_clus_p | 16.244 | 1.571 | 22.327 | |
| vis_gene | 27.749 | 2.405 | 31.055 | |
| vis_gene_p | 14.823 | 1.218 | 16.908 | |
| vis_grid_clus | 15.984 | 1.967 | 19.267 | |
| vis_grid_gene | 16.341 | 1.720 | 19.138 | |
| vis_image | 16.740 | 1.734 | 19.317 | |