##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.11-bioc/R/bin/R CMD check --install=check:tidybulk.install-out.txt --library=/home/biocbuild/bbs-3.11-bioc/R/library --no-vignettes --timings tidybulk_1.0.2.tar.gz
###
##############################################################################
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* using log directory ‘/home/biocbuild/bbs-3.11-bioc/meat/tidybulk.Rcheck’
* using R version 4.0.3 (2020-10-10)
* using platform: x86_64-pc-linux-gnu (64-bit)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘tidybulk/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘tidybulk’ version ‘1.0.2’
* 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 ‘tidybulk’ can be installed ... OK
* checking installed package size ... NOTE
  installed size is  7.5Mb
  sub-directories of 1Mb or more:
    data   6.7Mb
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... NOTE
File
  LICENSE
is not mentioned in the DESCRIPTION file.
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking R 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 dependencies in R code ... NOTE
package 'methods' is used but not declared
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
.cluster_elements: no visible binding for global variable ‘.’
.deconvolve_cellularity: no visible binding for global variable
  ‘X_cibersort’
.deconvolve_cellularity_se: no visible binding for global variable
  ‘X_cibersort’
.keep_abundant: no visible binding for global variable ‘.’
.tidybulk_se: no visible binding for global variable ‘.’
.tidybulk_se: no visible binding for global variable ‘feature’
add_scaled_counts_bulk.calcNormFactor: no visible binding for global
  variable ‘transcript’
add_scaled_counts_bulk.get_low_expressed: no visible binding for global
  variable ‘transcript’
add_scaled_counts_bulk.get_low_expressed: no visible binding for global
  variable ‘.’
aggregate_duplicated_transcripts_bulk: no visible binding for global
  variable ‘.abundance_scaled’
aggregate_duplicated_transcripts_bulk: no visible binding for global
  variable ‘n_aggr’
as_matrix: no visible binding for global variable ‘variable’
check_if_duplicated_genes: no visible binding for global variable
  ‘transcript’
check_if_duplicated_genes: no visible binding for global variable ‘read
  count’
create_tt_from_bam_sam_bulk: no visible binding for global variable ‘.’
create_tt_from_bam_sam_bulk: no visible binding for global variable
  ‘temp’
create_tt_from_bam_sam_bulk: no visible binding for global variable
  ‘Status’
create_tt_from_bam_sam_bulk: no visible binding for global variable
  ‘counts’
create_tt_from_bam_sam_bulk: no visible binding for global variable
  ‘GeneID’
create_tt_from_bam_sam_bulk: no visible binding for global variable
  ‘genes’
create_tt_from_bam_sam_bulk: no visible binding for global variable
  ‘transcript’
create_tt_from_bam_sam_bulk: no visible binding for global variable
  ‘samples’
create_tt_from_bam_sam_bulk: no visible binding for global variable
  ‘entrez’
deconvolve_cellularity: no visible binding for global variable
  ‘X_cibersort’
eliminate_sparse_transcripts: no visible binding for global variable
  ‘my_n’
entrez_rank_to_gsea: no visible binding for global variable ‘gs_cat’
entrez_rank_to_gsea: no visible binding for global variable ‘test’
error_if_duplicated_genes: no visible binding for global variable
  ‘transcript’
error_if_duplicated_genes: no visible binding for global variable ‘read
  count’
error_if_log_transformed: no visible binding for global variable ‘m’
fill_NA_using_formula: no visible binding for global variable ‘.’
get_abundance_norm_if_exists: no visible binding for global variable
  ‘.abundance_scaled’
get_adjusted_counts_for_unwanted_variation_bulk: no visible binding for
  global variable ‘.’
get_cell_type_proportions: no visible binding for global variable
  ‘X_cibersort’
get_cell_type_proportions: no visible binding for global variable ‘.’
get_clusters_SNN_bulk: no visible binding for global variable
  ‘seurat_clusters’
get_clusters_kmeans_bulk: no visible binding for global variable ‘.’
get_clusters_kmeans_bulk: no visible binding for global variable
  ‘cluster’
get_clusters_kmeans_bulk: no visible binding for global variable
  ‘cluster kmeans’
get_differential_transcript_abundance_bulk: no visible binding for
  global variable ‘.’
get_differential_transcript_abundance_bulk: no visible binding for
  global variable ‘lowly_abundant’
get_reduced_dimensions_MDS_bulk: no visible binding for global variable
  ‘cmdscale.out’
get_reduced_dimensions_PCA_bulk: no visible binding for global variable
  ‘sdev’
get_reduced_dimensions_PCA_bulk: no visible binding for global variable
  ‘name’
get_reduced_dimensions_PCA_bulk: no visible binding for global variable
  ‘value’
get_reduced_dimensions_PCA_bulk: no visible binding for global variable
  ‘rotation’
get_reduced_dimensions_TSNE_bulk: no visible binding for global
  variable ‘Y’
get_rotated_dimensions: no visible binding for global variable ‘value’
get_rotated_dimensions: no visible binding for global variable ‘rotated
  dimensions’
get_scaled_counts_bulk: no visible binding for global variable ‘med’
get_scaled_counts_bulk: no visible binding for global variable
  ‘tot_filt’
get_scaled_counts_bulk: no visible binding for global variable ‘.’
get_scaled_counts_bulk: no visible binding for global variable ‘tot’
get_scaled_counts_bulk: no visible binding for global variable
  ‘multiplier’
get_scaled_counts_bulk: no visible binding for global variable ‘x’
get_symbol_from_ensembl: no visible binding for global variable
  ‘ensembl_id’
get_symbol_from_ensembl: no visible binding for global variable
  ‘transcript’
get_symbol_from_ensembl: no visible binding for global variable
  ‘ref_genome’
get_tt_columns: no visible binding for global variable ‘tt_columns’
initialise_tt_internals: no visible binding for global variable ‘.’
remove_redundancy_elements_though_reduced_dimensions: no visible
  binding for global variable ‘sample b’
remove_redundancy_elements_though_reduced_dimensions: no visible
  binding for global variable ‘sample a’
remove_redundancy_elements_though_reduced_dimensions: no visible
  binding for global variable ‘sample 1’
remove_redundancy_elements_though_reduced_dimensions: no visible
  binding for global variable ‘sample 2’
remove_redundancy_elements_through_correlation: no visible binding for
  global variable ‘rc’
remove_redundancy_elements_through_correlation: no visible binding for
  global variable ‘transcript’
remove_redundancy_elements_through_correlation: no visible binding for
  global variable ‘correlation’
remove_redundancy_elements_through_correlation: no visible binding for
  global variable ‘item1’
scale_design: no visible binding for global variable ‘value’
scale_design: no visible binding for global variable ‘sample_idx’
scale_design: no visible binding for global variable ‘(Intercept)’
select_closest_pairs: no visible binding for global variable ‘sample 1’
select_closest_pairs: no visible binding for global variable ‘sample 2’
symbol_to_entrez: no visible binding for global variable ‘entrez’
test_gene_enrichment_bulk_EGSEA: no visible global function definition
  for ‘buildIdx’
test_gene_enrichment_bulk_EGSEA: no visible global function definition
  for ‘egsea’
test_gene_enrichment_bulk_EGSEA: no visible global function definition
  for ‘egsea.base’
test_gene_enrichment_bulk_EGSEA: no visible binding for global variable
  ‘med.rank’
test_gene_enrichment_bulk_EGSEA: no visible binding for global variable
  ‘data_base’
test_gene_enrichment_bulk_EGSEA: no visible binding for global variable
  ‘pathway’
tidybulk_to_SummarizedExperiment: no visible binding for global
  variable ‘.’
tidybulk_to_SummarizedExperiment: no visible binding for global
  variable ‘assay’
tidybulk_to_SummarizedExperiment: no visible binding for global
  variable ‘.a’
cluster_elements,spec_tbl_df: no visible binding for global variable
  ‘.’
cluster_elements,tbl_df: no visible binding for global variable ‘.’
cluster_elements,tidybulk: no visible binding for global variable ‘.’
deconvolve_cellularity,RangedSummarizedExperiment: no visible binding
  for global variable ‘X_cibersort’
deconvolve_cellularity,SummarizedExperiment: no visible binding for
  global variable ‘X_cibersort’
deconvolve_cellularity,spec_tbl_df: no visible binding for global
  variable ‘X_cibersort’
deconvolve_cellularity,tbl_df: no visible binding for global variable
  ‘X_cibersort’
deconvolve_cellularity,tidybulk: no visible binding for global variable
  ‘X_cibersort’
keep_abundant,spec_tbl_df: no visible binding for global variable ‘.’
keep_abundant,tbl_df: no visible binding for global variable ‘.’
keep_abundant,tidybulk: no visible binding for global variable ‘.’
tidybulk,RangedSummarizedExperiment: no visible binding for global
  variable ‘.’
tidybulk,RangedSummarizedExperiment: no visible binding for global
  variable ‘feature’
tidybulk,SummarizedExperiment: no visible binding for global variable
  ‘.’
tidybulk,SummarizedExperiment: no visible binding for global variable
  ‘feature’
Undefined global functions or variables:
  (Intercept) . .a .abundance_scaled GeneID Status X_cibersort Y assay
  buildIdx cluster cluster kmeans cmdscale.out correlation counts
  data_base egsea egsea.base ensembl_id entrez feature genes gs_cat
  item1 lowly_abundant m med med.rank multiplier my_n n_aggr name
  pathway rc read count ref_genome rotated dimensions rotation sample 1
  sample 2 sample a sample b sample_idx samples sdev seurat_clusters
  temp test tot tot_filt transcript tt_columns value variable x
Consider adding
  importFrom("base", "sample")
  importFrom("stats", "kmeans")
to your NAMESPACE file.
* 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 data for ASCII and uncompressed saves ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... WARNING
Found the following significant warnings:

  Warning: 'msigdbr::msigdbr_show_species' is deprecated.
Deprecated functions may be defunct as soon as of the next release of
R.
See ?Deprecated.
Examples with CPU (user + system) or elapsed time > 5s
                                       user system elapsed
deconvolve_cellularity-methods       86.769  5.909  70.473
test_gene_overrepresentation-methods 37.780  0.880  38.679
adjust_abundance-methods              5.480  0.247   5.743
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘testthat.R’
 OK
* checking for unstated dependencies in vignettes ... OK
* checking package vignettes in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 1 WARNING, 4 NOTEs
See
  ‘/home/biocbuild/bbs-3.11-bioc/meat/tidybulk.Rcheck/00check.log’
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.11-bioc/R/bin/R CMD INSTALL tidybulk
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.11-bioc/R/library’
* installing *source* package ‘tidybulk’ ...
** using staged installation
** R
** data
*** moving datasets to lazyload DB
** inst
** byte-compile and prepare package for lazy loading
in method for ‘tidybulk’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘tidybulk’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘scale_abundance’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘scale_abundance’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘cluster_elements’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘cluster_elements’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘reduce_dimensions’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘reduce_dimensions’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘rotate_dimensions’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘rotate_dimensions’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘remove_redundancy’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘remove_redundancy’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘adjust_abundance’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘adjust_abundance’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘aggregate_duplicates’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘aggregate_duplicates’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘deconvolve_cellularity’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘deconvolve_cellularity’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘test_differential_abundance’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘test_differential_abundance’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘keep_variable’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘keep_variable’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘keep_abundant’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘keep_abundant’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
in method for ‘impute_abundance’ with signature ‘"SummarizedExperiment"’: no definition for class “SummarizedExperiment”
in method for ‘impute_abundance’ with signature ‘"RangedSummarizedExperiment"’: no definition for class “RangedSummarizedExperiment”
Note: wrong number of arguments to '!' 
Note: wrong number of arguments to '>' 
Note: wrong number of arguments to '>' 
Note: wrong number of arguments to '<' 
Note: wrong number of arguments to '>' 
Note: wrong number of arguments to '!' 
Note: wrong number of arguments to '<' 
Note: wrong number of arguments to '>' 
Note: wrong number of arguments to '<' 
Note: wrong number of arguments to '<' 
Note: wrong number of arguments to '!' 
Note: wrong number of arguments to '!' 
Note: wrong number of arguments to '^' 
Note: wrong number of arguments to '/' 
Note: wrong number of arguments to 'floor' 
Note: wrong number of arguments to '>' 
Note: wrong number of arguments to '<' 
** help
*** installing help indices
** 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 (tidybulk)

R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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(tidybulk)

Attaching package: 'tidybulk'

The following object is masked from 'package:stats':

    filter

> 
> test_check("tidybulk")
Getting the 5 most variable genes

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 251
Number of edges: 8484

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5329
Number of communities: 4
Elapsed time: 0 seconds

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 251
Number of edges: 8484

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5329
Number of communities: 4
Elapsed time: 0 seconds

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
Calculating gene variances
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Calculating feature variances of standardized and clipped values
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 251
Number of edges: 8484

Running Louvain algorithm...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5329
Number of communities: 4
Elapsed time: 0 seconds
Getting the 500 most variable genes
Fraction of variance explained by the selected principal components
# A tibble: 2 x 2
  `Fraction of variance`    PC
                   <dbl> <int>
1                  0.581     1
2                  0.257     2
Getting the 500 most variable genes
Fraction of variance explained by the selected principal components
# A tibble: 2 x 2
  `Fraction of variance`    PC
                   <dbl> <int>
1                  0.581     1
2                  0.257     2
Getting the 500 most variable genes
Fraction of variance explained by the selected principal components
# A tibble: 2 x 2
  `Fraction of variance`    PC
                   <dbl> <int>
1                  0.581     1
2                  0.257     2
Getting the 500 most variable genes
Performing PCA
Read the 48 x 48 data matrix successfully!
OpenMP is working. 1 threads.
Using no_dims = 2, perplexity = 7.000000, and theta = 0.500000
Computing input similarities...
Building tree...
Done in 0.00 seconds (sparsity = 0.497396)!
Learning embedding...
Iteration 50: error is 51.120058 (50 iterations in 0.00 seconds)
Iteration 100: error is 44.302092 (50 iterations in 0.00 seconds)
Iteration 150: error is 48.253052 (50 iterations in 0.00 seconds)
Iteration 200: error is 45.889853 (50 iterations in 0.00 seconds)
Iteration 250: error is 48.668880 (50 iterations in 0.00 seconds)
Iteration 300: error is 1.050300 (50 iterations in 0.00 seconds)
Iteration 350: error is 0.747725 (50 iterations in 0.00 seconds)
Iteration 400: error is 0.421442 (50 iterations in 0.00 seconds)
Iteration 450: error is 0.538093 (50 iterations in 0.00 seconds)
Iteration 500: error is 0.719163 (50 iterations in 0.00 seconds)
Iteration 550: error is 0.660301 (50 iterations in 0.00 seconds)
Iteration 600: error is 0.336347 (50 iterations in 0.00 seconds)
Iteration 650: error is 0.323845 (50 iterations in 0.00 seconds)
Iteration 700: error is 0.172424 (50 iterations in 0.00 seconds)
Iteration 750: error is 0.113369 (50 iterations in 0.00 seconds)
Iteration 800: error is 0.069809 (50 iterations in 0.00 seconds)
Iteration 850: error is 0.051707 (50 iterations in 0.00 seconds)
Iteration 900: error is 0.024178 (50 iterations in 0.00 seconds)
Iteration 950: error is 0.023049 (50 iterations in 0.00 seconds)
Iteration 1000: error is 0.021362 (50 iterations in 0.00 seconds)
Fitting performed in 0.05 seconds.
Getting the 500 most variable genes
Performing PCA
Read the 48 x 48 data matrix successfully!
OpenMP is working. 1 threads.
Using no_dims = 2, perplexity = 7.000000, and theta = 0.500000
Computing input similarities...
Building tree...
Done in 0.00 seconds (sparsity = 0.497396)!
Learning embedding...
Iteration 50: error is 51.120058 (50 iterations in 0.00 seconds)
Iteration 100: error is 44.302092 (50 iterations in 0.00 seconds)
Iteration 150: error is 48.253052 (50 iterations in 0.00 seconds)
Iteration 200: error is 45.889853 (50 iterations in 0.00 seconds)
Iteration 250: error is 48.668880 (50 iterations in 0.00 seconds)
Iteration 300: error is 1.050300 (50 iterations in 0.00 seconds)
Iteration 350: error is 0.747725 (50 iterations in 0.00 seconds)
Iteration 400: error is 0.421442 (50 iterations in 0.00 seconds)
Iteration 450: error is 0.538093 (50 iterations in 0.00 seconds)
Iteration 500: error is 0.719163 (50 iterations in 0.00 seconds)
Iteration 550: error is 0.660301 (50 iterations in 0.00 seconds)
Iteration 600: error is 0.336347 (50 iterations in 0.00 seconds)
Iteration 650: error is 0.323845 (50 iterations in 0.00 seconds)
Iteration 700: error is 0.172424 (50 iterations in 0.00 seconds)
Iteration 750: error is 0.113369 (50 iterations in 0.00 seconds)
Iteration 800: error is 0.069809 (50 iterations in 0.00 seconds)
Iteration 850: error is 0.051707 (50 iterations in 0.00 seconds)
Iteration 900: error is 0.024178 (50 iterations in 0.00 seconds)
Iteration 950: error is 0.023049 (50 iterations in 0.00 seconds)
Iteration 1000: error is 0.021362 (50 iterations in 0.00 seconds)
Fitting performed in 0.06 seconds.
Getting the 500 most variable genes
Fraction of variance explained by the selected principal components
# A tibble: 2 x 2
  `Fraction of variance`    PC
                   <dbl> <int>
1                  0.581     1
2                  0.257     2
Getting the 500 most variable genes
Fraction of variance explained by the selected principal components
# A tibble: 2 x 2
  `Fraction of variance`    PC
                   <dbl> <int>
1                  0.581     1
2                  0.257     2
Getting the 500 most variable genes
Fraction of variance explained by the selected principal components
# A tibble: 2 x 2
  `Fraction of variance`    PC
                   <dbl> <int>
1                  0.581     1
2                  0.257     2
Getting the 527 most variable genes
Getting the 100 most variable genes
Fraction of variance explained by the selected principal components
# A tibble: 2 x 2
  `Fraction of variance`    PC
                   <dbl> <int>
1                0.990       1
2                0.00310     2
Getting the 100 most variable genes
Fraction of variance explained by the selected principal components
# A tibble: 2 x 2
  `Fraction of variance`    PC
                   <dbl> <int>
1                0.990       1
2                0.00310     2
Getting the 100 most variable genes
Getting the 5 most variable genes
Getting the 5 most variable genes
══ testthat results  ═══════════════════════════════════════════════════════════
[ OK: 157 | SKIPPED: 0 | WARNINGS: 13 | FAILED: 0 ]
> 
> proc.time()
   user  system elapsed 
194.018   4.990 194.397