Leonardo Collado-Torres
| Snapshot Date: 2025-10-09 07:30 -0400 (Thu, 09 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) |
| Back to Build/check report for BioC 3.22 experimental data |
|
This page was generated on 2025-10-09 15:41 -0400 (Thu, 09 Oct 2025).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo2 | Linux (Ubuntu 24.04.3 LTS) | x86_64 | 4.5.1 Patched (2025-08-23 r88802) -- "Great Square Root" | 4854 |
| 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 381/433 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | ||||||||
| spatialLIBD 1.21.6 (landing page) Leonardo Collado-Torres
| nebbiolo2 | 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: 1.21.6 |
| 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: 2025-10-09 13:08:53 -0400 (Thu, 09 Oct 2025) |
| EndedAt: 2025-10-09 13:28:05 -0400 (Thu, 09 Oct 2025) |
| EllapsedTime: 1152.3 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: spatialLIBD.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### 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 26.704 2.021 29.459
add_images 22.199 2.828 25.680
vis_clus 22.797 1.778 25.269
img_update_all 21.290 1.478 20.569
add_key 17.125 1.619 19.600
add_qc_metrics 17.074 1.389 18.721
vis_grid_gene 16.143 1.799 18.757
vis_grid_clus 15.854 1.834 18.511
cluster_import 15.951 1.281 17.964
vis_image 15.663 1.560 17.993
cluster_export 15.868 1.292 18.029
vis_clus_p 14.934 1.444 17.304
vis_gene_p 14.969 1.302 17.393
frame_limits 14.389 1.401 19.660
img_edit 14.502 1.202 16.230
img_update 14.401 1.300 16.368
check_spe 14.266 1.372 16.432
geom_spatial 14.447 1.099 16.363
sce_to_spe 14.158 1.107 16.086
gene_set_enrichment_plot 7.711 0.304 8.413
* 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: 1 NOTE
See
‘/home/biocbuild/bbs-3.22-data-experiment/meat/spatialLIBD.Rcheck/00check.log’
for details.
spatialLIBD.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD INSTALL spatialLIBD ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/bbs-3.22-bioc/R/site-library’ * installing *source* package ‘spatialLIBD’ ... ** this is package ‘spatialLIBD’ version ‘1.21.6’ ** 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)
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
111.874 7.250 124.454
spatialLIBD.Rcheck/spatialLIBD-Ex.timings
| name | user | system | elapsed | |
| add10xVisiumAnalysis | 0.000 | 0.000 | 0.001 | |
| add_images | 22.199 | 2.828 | 25.680 | |
| add_key | 17.125 | 1.619 | 19.600 | |
| add_qc_metrics | 17.074 | 1.389 | 18.721 | |
| annotate_registered_clusters | 1.176 | 0.049 | 1.459 | |
| check_modeling_results | 1.093 | 0.030 | 1.373 | |
| check_sce | 3.355 | 0.125 | 3.658 | |
| check_sce_layer | 1.435 | 0.063 | 1.674 | |
| check_spe | 14.266 | 1.372 | 16.432 | |
| cluster_export | 15.868 | 1.292 | 18.029 | |
| cluster_import | 15.951 | 1.281 | 17.964 | |
| enough_ram | 0.004 | 0.005 | 0.008 | |
| fetch_data | 1.213 | 0.048 | 1.480 | |
| frame_limits | 14.389 | 1.401 | 19.660 | |
| gene_set_enrichment | 1.219 | 0.071 | 1.510 | |
| gene_set_enrichment_plot | 7.711 | 0.304 | 8.413 | |
| geom_spatial | 14.447 | 1.099 | 16.363 | |
| get_colors | 1.195 | 0.031 | 1.488 | |
| img_edit | 14.502 | 1.202 | 16.230 | |
| img_update | 14.401 | 1.300 | 16.368 | |
| img_update_all | 21.290 | 1.478 | 20.569 | |
| layer_boxplot | 3.044 | 0.117 | 3.560 | |
| layer_stat_cor | 1.161 | 0.017 | 1.385 | |
| layer_stat_cor_plot | 4.224 | 0.237 | 4.999 | |
| locate_images | 0.001 | 0.000 | 0.000 | |
| read10xVisiumAnalysis | 0 | 0 | 0 | |
| read10xVisiumWrapper | 0 | 0 | 0 | |
| registration_block_cor | 2.734 | 0.021 | 2.756 | |
| registration_model | 0.738 | 0.002 | 0.741 | |
| registration_pseudobulk | 0.637 | 0.000 | 0.638 | |
| registration_stats_anova | 2.840 | 0.028 | 2.869 | |
| registration_stats_enrichment | 2.958 | 0.033 | 2.991 | |
| registration_stats_pairwise | 2.780 | 0.019 | 2.799 | |
| registration_wrapper | 4.291 | 0.060 | 4.351 | |
| run_app | 0 | 0 | 0 | |
| sce_to_spe | 14.158 | 1.107 | 16.086 | |
| sig_genes_extract | 2.519 | 0.147 | 3.064 | |
| sig_genes_extract_all | 3.135 | 0.059 | 3.546 | |
| sort_clusters | 0.007 | 0.000 | 0.007 | |
| vis_clus | 22.797 | 1.778 | 25.269 | |
| vis_clus_p | 14.934 | 1.444 | 17.304 | |
| vis_gene | 26.704 | 2.021 | 29.459 | |
| vis_gene_p | 14.969 | 1.302 | 17.393 | |
| vis_grid_clus | 15.854 | 1.834 | 18.511 | |
| vis_grid_gene | 16.143 | 1.799 | 18.757 | |
| vis_image | 15.663 | 1.560 | 17.993 | |