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BiocCheckGitClone('TimeSeriesAnalysis')
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─ BiocCheckVersion: 1.35.17
─ BiocVersion: 3.17
─ Package: TimeSeriesAnalysis
─ PackageVersion: 00.99.02
─ sourceDir: /home/pkgbuild/packagebuilder/workers/jobs/2883/57c13576bfc46202be0ddb109cea4c392335016f/TimeSeriesAnalysis
─ platform: unix
─ isTarBall: FALSE
* Checking valid files...
* Checking for stray BiocCheck output folders...
* Checking for inst/doc folders...
* Checking DESCRIPTION...
* Checking if DESCRIPTION is well formatted...
* Checking for valid maintainer...
* Checking CITATION...
─ BiocCheck results ──
0 ERRORS | 0 WARNINGS | 0 NOTES
For more details, run
browseVignettes(package = 'BiocCheck')
===============================
R CMD CHECK
===============================
* using log directory ‘/home/pkgbuild/packagebuilder/workers/jobs/2883/57c13576bfc46202be0ddb109cea4c392335016f/TimeSeriesAnalysis.Rcheck’
* using R version 4.3.0 alpha (2023-04-03 r84154)
* using platform: x86_64-pc-linux-gnu (64-bit)
* R was compiled by
gcc (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
GNU Fortran (Ubuntu 11.3.0-1ubuntu1~22.04) 11.3.0
* running under: Ubuntu 22.04.2 LTS
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘TimeSeriesAnalysis/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘TimeSeriesAnalysis’ version ‘00.99.02’
* 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 ... WARNING
Found the following files with non-portable file names:
docs/articles/TS_analysis_PBMC_files/figure-html/show_MDS_clust -1.png
docs/articles/TS_analysis_PBMC_files/figure-html/show_PCA_clust -1.png
docs/articles/TS_analysis_PBMC_files/figure-html/show_ancestor_MDS -1.png
docs/articles/TS_analysis_PBMC_files/figure-html/show_cond_heat_clust -1.png
docs/articles/TS_analysis_PBMC_files/figure-html/show_dotplot -1.png
These are not fully portable file names.
See section ‘Package structure’ in the ‘Writing R Extensions’ manual.
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘TimeSeriesAnalysis’ can be installed ... WARNING
Found the following significant warnings:
Warning: replacing previous import ‘SummarizedExperiment::shift’ by ‘data.table::shift’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘BiocGenerics::Position’ by ‘ggplot2::Position’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘BiocGenerics::residuals’ by ‘stats::residuals’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘SummarizedExperiment::start’ by ‘stats::start’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘SummarizedExperiment::end’ by ‘stats::end’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘BiocGenerics::weights’ by ‘stats::weights’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘BiocGenerics::IQR’ by ‘stats::IQR’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘BiocGenerics::var’ by ‘stats::var’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘BiocGenerics::xtabs’ by ‘stats::xtabs’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘BiocGenerics::sd’ by ‘stats::sd’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘BiocGenerics::density’ by ‘stats::density’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘BiocGenerics::mad’ by ‘stats::mad’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘GenomicRanges::update’ by ‘stats::update’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘data.table::shift’ by ‘tictoc::shift’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘AnnotationDbi::tail’ by ‘utils::tail’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘AnnotationDbi::head’ by ‘utils::head’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘BiocGenerics::relist’ by ‘utils::relist’ when loading ‘TimeSeriesAnalysis’
Warning: replacing previous import ‘data.table::melt’ by ‘reshape2::melt’ when loading ‘TimeSeriesAnalysis’
See ‘/home/pkgbuild/packagebuilder/workers/jobs/2883/57c13576bfc46202be0ddb109cea4c392335016f/TimeSeriesAnalysis.Rcheck/00install.out’ for details.
* checking installed package size ... OK
* checking package directory ... OK
* checking for future file timestamps ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... NOTE
License components which are templates and need '+ file LICENSE':
MIT
Packages listed in more than one of Depends, Imports, Suggests, Enhances:
‘SummarizedExperiment’ ‘BiocFileCache’
A package should be listed in only one of these fields.
* 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 ... [9s/9s] OK
* checking whether the package can be loaded with stated dependencies ... [9s/9s] OK
* checking whether the package can be unloaded cleanly ... [9s/9s] OK
* checking whether the namespace can be loaded with stated dependencies ... [9s/9s] OK
* checking whether the namespace can be unloaded cleanly ... [9s/9s] OK
* checking loading without being on the library search path ... [9s/9s] OK
* checking use of S3 registration ... 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 ... [33s/33s] NOTE
process_microarr_dta_limma: warning in read.maimages(file =
raw_files_path, source = micro_arr_source, green.only = green.only):
partial argument match of 'file' to 'files'
SS_GO_clusters: no visible binding for global variable ‘N’
SS_GO_clusters: no visible binding for global variable ‘IC’
TS_load_example_data: no visible binding for global variable
‘PBMC_TS_data’
add_semantic_similarity_data: no visible binding for global variable
‘PBMC_pre_loaded’
calculate_EB: no visible binding for global variable ‘G2’
calculate_EB: no visible binding for global variable ‘G1’
conditional_DE_wrapper: no visible binding for global variable
‘PBMC_pre_loaded’
convert_eb_res_to_DE_results: no visible binding for global variable
‘GeneName’
create_example_data_for_R: no visible binding for global variable
‘PBMC_TS_data’
create_example_object_for_R: no visible binding for global variable
‘my_ont_sem_sim’
create_raw_count_matrix: no visible binding for global variable
‘my_path_data’
custom_gpro_dotplot: no visible binding for global variable
‘group_name’
custom_gpro_dotplot: no visible binding for global variable ‘term_name’
custom_gpro_dotplot: no visible binding for global variable
‘-log10(padj)’
custom_gpro_dotplot: no visible binding for global variable ‘term_size’
dotplot_ancestors: no visible binding for global variable ‘timepoint’
dotplot_ancestors: no visible binding for global variable ‘term_name’
dotplot_ancestors: no visible binding for global variable ‘ancestor’
dotplot_ancestors: no visible binding for global variable ‘enrichment’
dotplot_ancestors: no visible binding for global variable ‘term_size’
dotplot_ancestors: no visible binding for global variable ‘group_name’
dotplot_ancestors: no visible binding for global variable
‘-log10(padj)’
find_relation_to_ancestors: no visible binding for global variable
‘group_color’
maplot_alt: no visible binding for global variable ‘baseMean_logged’
maplot_alt: no visible binding for global variable ‘log2FoldChange’
maplot_alt: no visible binding for global variable ‘Significance’
maplot_alt: no visible binding for global variable ‘gene_id’
part_gprofiler_vignettes: no visible binding for global variable
‘PBMC_pre_loaded’
part_load_results_vignettes: no visible binding for global variable
‘PBMC_pre_loaded’
plot_MDS: no visible binding for global variable ‘Dim.1’
plot_MDS: no visible binding for global variable ‘Dim.2’
plot_MDS: no visible binding for global variable ‘group_name’
plot_PCA_TS: no visible binding for global variable ‘PC1’
plot_PCA_TS: no visible binding for global variable ‘PC2’
plot_PCA_TS: no visible binding for global variable ‘group’
plot_PCA_TS: no visible binding for global variable ‘timepoint’
plot_PCA_TS: no visible binding for global variable ‘name’
plot_ancestor_clust_MDS: no visible binding for global variable ‘Dim.1’
plot_ancestor_clust_MDS: no visible binding for global variable ‘Dim.2’
plot_ancestor_clust_MDS: no visible binding for global variable
‘Ancestor’
plot_ancestor_clust_MDS: no visible binding for global variable
‘-log10(padj)’
plot_cluster_traj: no visible binding for global variable ‘trans_mean’
plot_cluster_traj: no visible binding for global variable
‘log10_timepoint’
plot_cluster_traj: no visible binding for global variable ‘group’
plot_cluster_traj: no visible binding for global variable ‘timepoint’
plot_cluster_traj: no visible binding for global variable ‘gene_id’
plot_clustered_mds: no visible binding for global variable ‘text’
plot_clustered_mds: no visible binding for global variable ‘Dim.1’
plot_clustered_mds: no visible binding for global variable ‘Dim.2’
plot_clustered_mds: no visible binding for global variable ‘GO.cluster’
plot_clustered_mds: no visible binding for global variable ‘nb’
plot_single_gene_traj: no visible binding for global variable
‘log10_timepoint’
plot_single_gene_traj: no visible binding for global variable ‘reads’
plot_single_gene_traj: no visible binding for global variable ‘group’
plot_single_gene_traj: no visible binding for global variable
‘timepoint’
temporal_DE_wrapper: no visible binding for global variable
‘PBMC_pre_loaded’
volcanoplot_alt: no visible binding for global variable
‘log2FoldChange’
volcanoplot_alt: no visible binding for global variable ‘pvalue’
volcanoplot_alt: no visible binding for global variable ‘Significance’
volcanoplot_alt: no visible binding for global variable ‘gene_id’
write_example_data_to_dir: no visible binding for global variable
‘PBMC_TS_data’
Undefined global functions or variables:
-log10(padj) Ancestor Dim.1 Dim.2 G1 G2 GO.cluster GeneName IC N
PBMC_TS_data PBMC_pre_loaded PC1 PC2 Significance ancestor
baseMean_logged enrichment gene_id group group_color group_name
log10_timepoint log2FoldChange my_ont_sem_sim my_path_data name nb
pvalue reads term_name term_size text timepoint trans_mean
Consider adding
importFrom("graphics", "text")
to your NAMESPACE file.
* checking Rd files ... [0s/0s] OK
* checking Rd metadata ... OK
* checking Rd line widths ... NOTE
Rd file 'GO_dotplot_wrapper.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
gpro_dotplot<-GO_dotplot_wrapper(TS_object,file_loc=NULL,target_ontology='GO:BP',top_n=10,return_plot=TRUE)
Rd file 'PART_heat_map.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'PartRec.Rd':
\examples lines wider than 100 characters:
default.par <- list(q=0.25,Kmax.rec=5,B=10,ref.gen="PC",dist.method="euclidean",cl.method="hclust",linkage="average",cor.method="pearso ... [TRUNCATED]
fixed.par <- c(minDist=NULL,minSize=2,modifyList(default.par,list(cor.method='pearson',linkage='average')))
Rd file 'SS_GO_clusters.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'add_experiment_data.Rd':
\examples lines wider than 100 characters:
TS_object <- add_experiment_data(TS_object,sample_dta_path=my_path_sample_dta,count_dta_path=my_path_data)
Rd file 'calculate_and_format_MDS.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'calculate_cluster_traj_data.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
ts_data<-calculate_cluster_traj_data(TS_object,scale_feat=TRUE) #Calculate scaled gene values for genes of clusters
Rd file 'calculate_mean_cluster_traj.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
ts_data<-calculate_cluster_traj_data(TS_object,scale_feat=TRUE) #Calculate scaled gene values for genes of clusters
mean_ts_data<-calculate_mean_cluster_traj(ts_data) #Calculate the mean scaled values for each cluster
Rd file 'compute_PART.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'create_DE_data_results.Rd':
\examples lines wider than 100 characters:
my_res<-create_DE_data_results(TS_object,DE_type='conditional',exp_name='IgM_vs_LPS_TP_1',save_location=NULL)
Rd file 'create_clustered_module_dataframe.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'custom_gpro_dotplot.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
GO_top_cluster <- GO_top_cluster[order(GO_top_cluster[,'term_id'],-GO_top_cluster[,'-log10(padj)']),]
Rd file 'doHclust.Rd':
\examples lines wider than 100 characters:
default.par <- list(q=0.25,Kmax.rec=5,B=100,ref.gen="PC",dist.method="euclidean",cl.method="hclust",linkage="average",cor.method="pears ... [TRUNCATED]
fixed.par <- c(minDist=NULL,minSize=2,modifyList(default.par,list(cor.method='pearson',linkage='average')))
gap.res <- gap(X=X,Kmax=10,cl.lab=cl.lab,B=fixed.par$B,ref.gen=fixed.par$ref.gen,fixed.par=fixed.par)
hc.res <- doHclust(getDist(X,dist.method=fixed.par$dist.method,cor.method=fixed.par$cor.method),k=1,linkage=fixed.par$linkage)$cl #( ... [TRUNCATED]
Rd file 'doKmeans.Rd':
\examples lines wider than 100 characters:
default.par <- list(q=0.25,Kmax.rec=5,B=100,ref.gen="PC",dist.method="euclidean",cl.method="hclust",linkage="average",cor.method="pears ... [TRUNCATED]
fixed.par <- c(minDist=NULL,minSize=2,modifyList(default.par,list(cor.method='pearson',linkage='average')))
kmeans=doKmeans(X,k,nstart=10)$lab) #note:kmeans only calculated for euclidean distance!
Rd file 'dotplot_ancestors.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
GOs_ancestors_clust<-find_relation_to_ancestors(target_ancestors,GO_clusters,ontology = ancestor_ontology)
Rd file 'findPartition.Rd':
\examples lines wider than 100 characters:
default.par <- list(q=0.25,Kmax.rec=5,B=100,ref.gen="PC",dist.method="euclidean",cl.method="hclust",linkage="average",cor.method="pears ... [TRUNCATED]
fixed.par <- c(minDist=NULL,minSize=2,modifyList(default.par,list(cor.method='pearson',linkage='average')))
Rd file 'findW.Rd':
\examples lines wider than 100 characters:
default.par <- list(q=0.25,Kmax.rec=5,B=100,ref.gen="PC",dist.method="euclidean",cl.method="hclust",linkage="average",cor.method="pears ... [TRUNCATED]
fixed.par <- c(minDist=NULL,minSize=2,modifyList(default.par,list(cor.method='pearson',linkage='average')))
Rd file 'find_clusters_from_termdist.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'find_most_variable_cluster.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
ts_data<-calculate_cluster_traj_data(TS_object,scale_feat=TRUE) #Calculate scaled gene values for genes of clusters
mean_ts_data<-calculate_mean_cluster_traj(ts_data) #Calculate the mean scaled values for each cluster
Rd file 'find_relation_to_ancestors.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
GOs_ancestors_clust<-find_relation_to_ancestors(target_ancestors,GO_clusters,ontology = ancestor_ontology)
Rd file 'gap.Rd':
\examples lines wider than 100 characters:
default.par <- list(q=0.25,Kmax.rec=5,B=100,ref.gen="PC",dist.method="euclidean",cl.method="hclust",linkage="average",cor.method="pears ... [TRUNCATED]
fixed.par <- c(minDist=NULL,minSize=2,modifyList(default.par,list(cor.method='pearson',linkage='average')))
gap.res <- gap(X=X,Kmax=10,cl.lab=cl.lab,B=fixed.par$B,ref.gen=fixed.par$ref.gen,fixed.par=fixed.par)
Rd file 'get.threshold.Rd':
\examples lines wider than 100 characters:
default.par <- list(q=0.25,Kmax.rec=5,B=100,ref.gen="PC",dist.method="euclidean",cl.method="hclust",linkage="average",cor.method="pears ... [TRUNCATED]
fixed.par <- c(minDist=NULL,minSize=2,modifyList(default.par,list(cor.method='pearson',linkage='average')))
Rd file 'getPARTlabels.Rd':
\examples lines wider than 100 characters:
default.par <- list(q=0.25,Kmax.rec=5,B=10,ref.gen="PC",dist.method="euclidean",cl.method="hclust",linkage="average",cor.method="pearso ... [TRUNCATED]
fixed.par <- c(minDist=NULL,minSize=2,modifyList(default.par,list(cor.method='pearson',linkage='average')))
Rd file 'getReferenceW.Rd':
\examples lines wider than 100 characters:
default.par <- list(q=0.25,Kmax.rec=5,B=100,ref.gen="PC",dist.method="euclidean",cl.method="hclust",linkage="average",cor.method="pears ... [TRUNCATED]
fixed.par <- c(minDist=NULL,minSize=2,modifyList(default.par,list(cor.method='pearson',linkage='average')))
Rd file 'gprofiler_cluster_analysis.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'merge_duplicate_modules.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'plot_MDS.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'plot_ancestor_clust_MDS.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'plot_cluster_traj.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
ts_data<-calculate_cluster_traj_data(TS_object,scale_feat=TRUE) #Calculate scaled gene values for genes of clusters
mean_ts_data<-calculate_mean_cluster_traj(ts_data) #Calculate the mean scaled values for each cluster
Rd file 'plot_clustered_mds.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'prep_RNAseq_matrix.Rd':
\examples lines wider than 100 characters:
TS_object <- add_experiment_data(TS_object,sample_dta_path=my_path_sample_dta,count_dta_path=my_path_data)
Rd file 'prep_counts_for_PART.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
Rd file 'prepare_top_annotation_PART_heat.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'read_gprofiler_results.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'run_gprofiler_PART_clusters.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
Rd file 'wrapper_MDS_and_MDS_clusters.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
MDS_plots<-wrapper_MDS_and_MDS_clusters(GO_clusters,sem_dta,sem_ontology='BP',target_dir=NULL,return_plot=TRUE)
Rd file 'wrapper_ancestor_curation_plots.Rd':
\examples lines wider than 100 characters:
TS_object<-prep_counts_for_PART(object=TS_object,target_genes=signi_genes,scale=TRUE,target_samples=c(samps_2,samps_1))
TS_object<-compute_PART(TS_object,part_recursion=10,part_min_clust=10,dist_param="euclidean", hclust_param="average",vignette_run=TRUE)
GOs_ancestors_clust<-find_relation_to_ancestors(target_ancestors,GO_clusters,ontology = ancestor_ontology)
ancestor_plots<-wrapper_ancestor_curation_plots(GOs_ancestors_clust,sem_dta,return_plot=TRUE,target_dir=NULL)
These lines will be truncated in the PDF manual.
* checking Rd cross-references ... OK
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* checking data for non-ASCII characters ... [7s/7s] NOTE
Note: found 27 marked UTF-8 strings
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... [491s/489s] OK
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
wrapper_MDS_and_MDS_clusters 51.810 0.207 51.139
plot_clustered_mds 51.669 0.256 51.165
SS_GO_clusters 38.289 0.380 37.960
plot_MDS 33.204 0.200 33.404
merge_duplicate_modules 32.909 0.132 33.042
create_clustered_module_dataframe 32.513 0.292 32.805
calculate_and_format_MDS 32.263 0.232 32.496
find_clusters_from_termdist 32.037 0.164 32.201
DE_plots_vignettes 13.818 0.332 14.150
plot_custom_DE_heatmap 9.381 0.108 9.489
custom_heatmap_wrapper 9.388 0.064 9.452
wrapper_ancestor_curation_plots 8.759 0.384 9.143
plot_ancestor_clust_MDS 8.440 0.080 8.521
gap 8.108 0.048 8.156
doHclust 7.641 0.044 7.684
identify_problematic_combs 7.039 0.024 7.063
dotplot_ancestors 6.579 0.068 6.648
find_relation_to_ancestors 6.467 0.040 6.507
GO_dotplot_wrapper 5.850 0.064 5.914
custom_gpro_dotplot 5.880 0.028 5.908
read_gprofiler_results 5.841 0.016 5.857
gprofiler_cluster_analysis 5.686 0.016 5.702
* 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 ... [4s/4s] OK
* DONE
Status: 2 WARNINGs, 5 NOTEs
See
‘/home/pkgbuild/packagebuilder/workers/jobs/2883/57c13576bfc46202be0ddb109cea4c392335016f/TimeSeriesAnalysis.Rcheck/00check.log’
for details.
WARNING: R CMD check exceeded 10 min requirement
===============================
BiocCheck('TimeSeriesAnalysis_00.99.02.tar.gz')
===============================
─ BiocCheckVersion: 1.35.17
─ BiocVersion: 3.17
─ Package: TimeSeriesAnalysis
─ PackageVersion: 00.99.02
─ sourceDir: /tmp/RtmpqaS8R1/file15374c3fe0fabd/TimeSeriesAnalysis
─ installDir: /tmp/RtmpqaS8R1/file15374c7d477a6c
─ BiocCheckDir: /home/pkgbuild/packagebuilder/workers/jobs/2883/57c13576bfc46202be0ddb109cea4c392335016f/TimeSeriesAnalysis.BiocCheck
─ platform: unix
─ isTarBall: TRUE
* Installing package...
* Checking for deprecated package usage...
* Checking for remote package usage...
* Checking for 'LazyData: true' usage...
* NOTE: 'LazyData:' in the 'DESCRIPTION' should be set to false or
removed
* Checking version number...
* Checking for version number mismatch...
* Checking new package version number...
* Checking R version dependency...
* NOTE: Update R version dependency from 4.2 to 4.3.0.
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* Checking biocViews...
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* Checking package type based on biocViews...
Software
* Checking for non-trivial biocViews...
* Checking that biocViews come from the same category...
* Checking biocViews validity...
* Checking for recommended biocViews...
* NOTE: Consider adding these automatically suggested biocViews:
Coverage, GeneTarget, DataImport
Search 'biocViews' at https://contributions.bioconductor.org
* Checking build system compatibility...
* Checking for blank lines in DESCRIPTION...
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* NOTE: The Description field in the DESCRIPTION is made up by less
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* NOTE: Consider adding the maintainer's ORCID iD in 'Authors@R'
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* NOTE: Avoid 1:...; use seq_len() or seq_along()
* NOTE: Avoid 'cat' and 'print' outside of 'show' methods
* NOTE: Avoid using '=' for assignment and use '<-' instead
* NOTE: Avoid the use of 'paste' in condition signals
* Checking parsed R code in R directory, examples, vignettes...
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* NOTE: The recommended function length is 50 lines or less. There
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* Checking man page documentation...
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* NOTE: Consider adding runnable examples to man pages that
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* Checking package NEWS...
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* NOTE: Consider adding unit tests. We strongly encourage them. See
https://contributions.bioconductor.org/tests.html
* Checking skip_on_bioc() in tests...
* Checking formatting of DESCRIPTION, NAMESPACE, man pages, R source,
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* NOTE: Consider shorter lines; 1292 lines (13%) are > 80
characters long.
* NOTE: Consider 4 spaces instead of tabs; 1 lines (0%) contain
tabs.
* NOTE: Consider multiples of 4 spaces for line indents; 1776 lines
(18%) are not.
See https://contributions.bioconductor.org/r-code.html
See styler package: https://cran.r-project.org/package=styler as
described in the BiocCheck vignette.
* Checking if package already exists in CRAN...
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* Checking for support site registration...
Maintainer is registered at support site.
Package name is in support site watched tags.
─ BiocCheck results ──
0 ERRORS | 1 WARNINGS | 16 NOTES
See the TimeSeriesAnalysis.BiocCheck folder and run
browseVignettes(package = 'BiocCheck')
for details.