Stefano Mangiola
| Snapshot Date: 2020-10-16 14:40:19 -0400 (Fri, 16 Oct 2020) |
| URL: https://git.bioconductor.org/packages/tidybulk |
| Branch: RELEASE_3_11 |
| Last Commit: bfa4dd1 |
| Last Changed Date: 2020-06-08 00:40:18 -0400 (Mon, 08 Jun 2020) |
| Back to Multiple platform build/check report for BioC 3.11 |
|
This page was generated on 2020-10-17 11:57:56 -0400 (Sat, 17 Oct 2020).
| TO THE DEVELOPERS/MAINTAINERS OF THE tidybulk PACKAGE: Please make sure to use the following settings in order to reproduce any error or warning you see on this page. |
| Package 1797/1905 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||
| tidybulk 1.0.2 Stefano Mangiola
| malbec2 | Linux (Ubuntu 18.04.4 LTS) / x86_64 | OK | OK | WARNINGS | | ||||||
| tokay2 | Windows Server 2012 R2 Standard / x64 | OK | OK | [ WARNINGS ] | NA | |||||||
| machv2 | macOS 10.14.6 Mojave / x86_64 | OK | OK | WARNINGS | OK | |
| Package: tidybulk |
| Version: 1.0.2 |
| Command: C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:tidybulk.install-out.txt --library=C:\Users\biocbuild\bbs-3.11-bioc\R\library --no-vignettes --timings tidybulk_1.0.2.tar.gz |
| StartedAt: 2020-10-17 08:50:35 -0400 (Sat, 17 Oct 2020) |
| EndedAt: 2020-10-17 09:04:47 -0400 (Sat, 17 Oct 2020) |
| EllapsedTime: 852.7 seconds |
| RetCode: 0 |
| Status: WARNINGS |
| CheckDir: tidybulk.Rcheck |
| Warnings: 2 |
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###
### Running command:
###
### C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD check --force-multiarch --install=check:tidybulk.install-out.txt --library=C:\Users\biocbuild\bbs-3.11-bioc\R\library --no-vignettes --timings tidybulk_1.0.2.tar.gz
###
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* using log directory 'C:/Users/biocbuild/bbs-3.11-bioc/meat/tidybulk.Rcheck'
* using R version 4.0.3 (2020-10-10)
* using platform: x86_64-w64-mingw32 (64-bit)
* using session charset: ISO8859-1
* 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 whether package 'tidybulk' can be installed ... OK
* checking installed package size ... NOTE
installed size is 7.9Mb
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
* loading checks for arch 'i386'
** 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
* loading checks for arch 'x64'
** 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 ...
** running examples for arch 'i386' ... 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 67.46 0.66 68.14
test_gene_overrepresentation-methods 35.94 3.82 39.87
adjust_abundance-methods 6.92 0.27 7.19
** running examples for arch 'x64' ... 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 72.16 0.50 72.67
test_gene_overrepresentation-methods 38.00 2.16 40.17
adjust_abundance-methods 8.66 0.16 8.81
* checking for unstated dependencies in 'tests' ... OK
* checking tests ...
** running tests for arch 'i386' ...
Running 'testthat.R'
OK
** running tests for arch 'x64' ...
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: 2 WARNINGs, 4 NOTEs
See
'C:/Users/biocbuild/bbs-3.11-bioc/meat/tidybulk.Rcheck/00check.log'
for details.
tidybulk.Rcheck/00install.out
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###
### Running command:
###
### C:\cygwin\bin\curl.exe -O https://malbec2.bioconductor.org/BBS/3.11/bioc/src/contrib/tidybulk_1.0.2.tar.gz && rm -rf tidybulk.buildbin-libdir && mkdir tidybulk.buildbin-libdir && C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD INSTALL --merge-multiarch --build --library=tidybulk.buildbin-libdir tidybulk_1.0.2.tar.gz && C:\Users\biocbuild\bbs-3.11-bioc\R\bin\R.exe CMD INSTALL tidybulk_1.0.2.zip && rm tidybulk_1.0.2.tar.gz tidybulk_1.0.2.zip
###
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% Total % Received % Xferd Average Speed Time Time Time Current
Dload Upload Total Spent Left Speed
0 0 0 0 0 0 0 0 --:--:-- --:--:-- --:--:-- 0
100 4458k 100 4458k 0 0 30.5M 0 --:--:-- --:--:-- --:--:-- 32.2M
install for i386
* 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
converting help for package 'tidybulk'
finding HTML links ... done
X_cibersort html
add_attr html
add_class html
add_scaled_counts_bulk.calcNormFactor html
add_scaled_counts_bulk.get_low_expressed
html
adjust_abundance-methods html
aggregate_duplicated_transcripts_bulk html
aggregate_duplicates-methods html
as_matrix html
bind html
breast_tcga_mini html
check_if_counts_is_na html
check_if_duplicated_genes html
check_if_wrong_input html
cluster_elements-methods html
counts html
counts_ensembl html
counts_mini html
create_tt_from_bam_sam_bulk html
create_tt_from_tibble_bulk html
deconvolve_cellularity-methods html
distinct html
dplyr-methods html
drop_attr html
drop_class html
ensembl_symbol_mapping html
ensembl_to_symbol-methods html
error_if_counts_is_na html
error_if_duplicated_genes html
error_if_log_transformed html
error_if_wrong_input html
fill_NA_using_formula html
fill_NA_with_row_median html
filter html
flybaseIDs html
full_join html
get_abundance_norm_if_exists html
get_adjusted_counts_for_unwanted_variation_bulk
html
get_cell_type_proportions html
get_clusters_SNN_bulk html
get_clusters_kmeans_bulk html
get_differential_transcript_abundance_bulk
html
get_elements html
get_elements_features html
get_elements_features_abundance html
get_reduced_dimensions_MDS_bulk html
get_reduced_dimensions_PCA_bulk html
get_reduced_dimensions_TSNE_bulk html
get_rotated_dimensions html
get_sample html
get_sample_counts html
get_sample_transcript html
get_sample_transcript_counts html
get_scaled_counts_bulk html
get_symbol_from_ensembl html
get_transcript html
get_x_y_annotation_columns html
group_by html
ifelse2_pipe html
ifelse_pipe html
impute_abundance-methods html
inner_join html
keep_abundant-methods html
keep_variable-methods html
keep_variable_transcripts html
left_join html
mutate html
parse_formula html
pivot_sample-methods html
pivot_transcript-methods html
prepend html
reduce_dimensions-methods html
reexports html
remove_redundancy-methods html
remove_redundancy_elements_though_reduced_dimensions
html
remove_redundancy_elements_through_correlation
html
rename html
right_join html
rotate_dimensions-methods html
rowwise html
run_llsr html
scale_abundance-methods html
scale_design html
se html
se_mini html
select_closest_pairs html
summarise html
symbol_to_entrez html
test_differential_abundance-methods html
test_gene_enrichment-methods html
test_gene_enrichment_bulk_EGSEA html
test_gene_overrepresentation-methods html
tidybulk-methods html
tidybulk_SAM_BAM-methods html
tidybulk_to_SummarizedExperiment html
tidyr-methods html
** 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
install for x64
* installing *source* package 'tidybulk' ...
** testing if installed package can be loaded
* MD5 sums
packaged installation of 'tidybulk' as tidybulk_1.0.2.zip
* DONE (tidybulk)
* installing to library 'C:/Users/biocbuild/bbs-3.11-bioc/R/library'
package 'tidybulk' successfully unpacked and MD5 sums checked
|
tidybulk.Rcheck/tests_i386/testthat.Rout
R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: i386-w64-mingw32/i386 (32-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 0.02 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 0.01 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 0.02 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 0.02 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 0.01 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 0.08 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 0.02 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 0.01 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 0.02 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 0.01 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
172.07 6.92 194.51
|
tidybulk.Rcheck/tests_x64/testthat.Rout
R version 4.0.3 (2020-10-10) -- "Bunny-Wunnies Freak Out"
Copyright (C) 2020 The R Foundation for Statistical Computing
Platform: x86_64-w64-mingw32/x64 (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 0.02 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 0.01 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 0.02 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 0.02 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 0.01 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 0.02 seconds)
Iteration 950: error is 0.023049 (50 iterations in 0.01 seconds)
Iteration 1000: error is 0.021362 (50 iterations in 0.00 seconds)
Fitting performed in 0.11 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 0.02 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 0.01 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 0.02 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 0.02 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 0.01 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
231.53 3.53 260.17
|
|
tidybulk.Rcheck/examples_i386/tidybulk-Ex.timings
|
tidybulk.Rcheck/examples_x64/tidybulk-Ex.timings
|