Back to Multiple platform build/check report for BioC 3.22: simplified long |
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This page was generated on 2025-10-04 12:03 -0400 (Sat, 04 Oct 2025).
Hostname | OS | Arch (*) | R version | Installed pkgs |
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nebbiolo2 | Linux (Ubuntu 24.04.3 LTS) | x86_64 | 4.5.1 Patched (2025-08-23 r88802) -- "Great Square Root" | 4853 |
lconway | macOS 12.7.1 Monterey | x86_64 | 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" | 4640 |
kjohnson3 | macOS 13.7.7 Ventura | arm64 | 4.5.1 Patched (2025-09-10 r88807) -- "Great Square Root" | 4585 |
taishan | Linux (openEuler 24.03 LTS) | aarch64 | 4.5.0 (2025-04-11) -- "How About a Twenty-Six" | 4576 |
Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X |
Package 1146/2341 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
looking4clusters 0.99.4 (landing page) David Barrios
| nebbiolo2 | Linux (Ubuntu 24.04.3 LTS) / x86_64 | OK | OK | OK | ![]() | ||||||||
lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | ![]() | ||||||||
kjohnson3 | macOS 13.7.7 Ventura / arm64 | OK | OK | OK | OK | ![]() | ||||||||
taishan | Linux (openEuler 24.03 LTS) / aarch64 | NA | NA | NA | ||||||||||
To the developers/maintainers of the looking4clusters package: - Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/looking4clusters.git to reflect on this report. See Troubleshooting Build Report for more information. - Use the following Renviron settings to reproduce errors and warnings. - If 'R CMD check' started to fail recently on the Linux builder(s) over a missing dependency, add the missing dependency to 'Suggests:' in your DESCRIPTION file. See Renviron.bioc for more information. |
Package: looking4clusters |
Version: 0.99.4 |
Command: /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:looking4clusters.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings looking4clusters_0.99.4.tar.gz |
StartedAt: 2025-10-04 01:08:30 -0400 (Sat, 04 Oct 2025) |
EndedAt: 2025-10-04 01:12:58 -0400 (Sat, 04 Oct 2025) |
EllapsedTime: 267.4 seconds |
RetCode: 0 |
Status: OK |
CheckDir: looking4clusters.Rcheck |
Warnings: 0 |
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD check --install=check:looking4clusters.install-out.txt --library=/home/biocbuild/bbs-3.22-bioc/R/site-library --timings looking4clusters_0.99.4.tar.gz ### ############################################################################## ############################################################################## * using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/looking4clusters.Rcheck’ * using R version 4.5.1 Patched (2025-08-23 r88802) * using platform: x86_64-pc-linux-gnu * R was compiled by gcc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 GNU Fortran (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 * running under: Ubuntu 24.04.3 LTS * using session charset: UTF-8 * checking for file ‘looking4clusters/DESCRIPTION’ ... OK * checking extension type ... Package * this is package ‘looking4clusters’ version ‘0.99.4’ * 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 ‘looking4clusters’ can be installed ... OK * checking installed package size ... OK * checking package directory ... OK * checking ‘build’ directory ... OK * checking DESCRIPTION meta-information ... OK * checking top-level files ... OK * checking for left-over files ... OK * checking index information ... OK * checking package subdirectories ... OK * checking code files for non-ASCII characters ... OK * checking R files for syntax errors ... OK * checking whether the package can be loaded ... OK * checking whether the package can be loaded with stated dependencies ... OK * checking whether the package can be unloaded cleanly ... OK * checking whether the namespace can be loaded with stated dependencies ... OK * checking whether the namespace can be unloaded cleanly ... OK * checking loading without being on the library search path ... OK * checking dependencies in R code ... OK * checking S3 generic/method consistency ... OK * checking replacement functions ... OK * checking foreign function calls ... OK * checking R code for possible problems ... OK * checking Rd files ... OK * checking Rd metadata ... OK * checking Rd cross-references ... 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 files in ‘vignettes’ ... OK * checking examples ... OK Examples with CPU (user + system) or elapsed time > 5s user system elapsed looking4clusters 6.462 0.303 6.764 * checking for unstated dependencies in ‘tests’ ... OK * checking tests ... Running ‘looking4clusters-Tests.R’ OK * checking for unstated dependencies in vignettes ... OK * checking package vignettes ... OK * checking re-building of vignette outputs ... OK * checking PDF version of manual ... OK * DONE Status: OK
looking4clusters.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/bbs-3.22-bioc/R/bin/R CMD INSTALL looking4clusters ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/bbs-3.22-bioc/R/site-library’ * installing *source* package ‘looking4clusters’ ... ** this is package ‘looking4clusters’ version ‘0.99.4’ ** using staged installation ** R ** inst ** byte-compile and prepare package for lazy loading ** 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 (looking4clusters)
looking4clusters.Rcheck/tests/looking4clusters-Tests.Rout
R version 4.5.1 Patched (2025-08-23 r88802) -- "Great Square Root" Copyright (C) 2025 The R Foundation for Statistical Computing Platform: x86_64-pc-linux-gnu R is free software and comes with ABSOLUTELY NO WARRANTY. You are welcome to redistribute it under certain conditions. Type 'license()' or 'licence()' for distribution details. R is a collaborative project with many contributors. Type 'contributors()' for more information and 'citation()' on how to cite R or R packages in publications. Type 'demo()' for some demos, 'help()' for on-line help, or 'help.start()' for an HTML browser interface to help. Type 'q()' to quit R. > library(looking4clusters) > > object <- looking4clusters(iris[,1:4],running_all=FALSE) > object <- addcluster(object,iris[,5],"species",myGroups=TRUE) > PCAcomponents <- prcomp(data.matrix(iris[,1:4]),scale=FALSE) > pca<-PCAcomponents$x[,1:2] > object <- addreduction(object,pca,"pca") > l4chtml(object,includeData=TRUE,directory="l4c_saved") The graph has been generated in the "/home/biocbuild/bbs-3.22-bioc/meat/looking4clusters.Rcheck/tests/l4c_saved" path. > > # get clusters (auto) > obj <- looking4clusters(iris[,1:4], groups=iris[,5]) Running kmeans... Running pam and hclust... Running pca... Running tsne... Running mds... Running nmf... Running umap... > print(object) An object of class looking4clusters 4 variables across 150 samples 1 clusters added: species 1 dimensional reductions added: pca > > # single-cell RNAseq > library(scRNAseq) Loading required package: SingleCellExperiment Loading required package: SummarizedExperiment Loading required package: MatrixGenerics Loading required package: matrixStats Attaching package: 'matrixStats' The following objects are masked from 'package:Biobase': anyMissing, rowMedians Attaching package: 'MatrixGenerics' The following objects are masked from 'package:matrixStats': colAlls, colAnyNAs, colAnys, colAvgsPerRowSet, colCollapse, colCounts, colCummaxs, colCummins, colCumprods, colCumsums, colDiffs, colIQRDiffs, colIQRs, colLogSumExps, colMadDiffs, colMads, colMaxs, colMeans2, colMedians, colMins, colOrderStats, colProds, colQuantiles, colRanges, colRanks, colSdDiffs, colSds, colSums2, colTabulates, colVarDiffs, colVars, colWeightedMads, colWeightedMeans, colWeightedMedians, colWeightedSds, colWeightedVars, rowAlls, rowAnyNAs, rowAnys, rowAvgsPerColSet, rowCollapse, rowCounts, rowCummaxs, rowCummins, rowCumprods, rowCumsums, rowDiffs, rowIQRDiffs, rowIQRs, rowLogSumExps, rowMadDiffs, rowMads, rowMaxs, rowMeans2, rowMedians, rowMins, rowOrderStats, rowProds, rowQuantiles, rowRanges, rowRanks, rowSdDiffs, rowSds, rowSums2, rowTabulates, rowVarDiffs, rowVars, rowWeightedMads, rowWeightedMeans, rowWeightedMedians, rowWeightedSds, rowWeightedVars The following object is masked from 'package:Biobase': rowMedians Loading required package: GenomicRanges Loading required package: stats4 Loading required package: S4Vectors Attaching package: 'S4Vectors' The following object is masked from 'package:utils': findMatches The following objects are masked from 'package:base': I, expand.grid, unname Loading required package: IRanges Loading required package: Seqinfo > sce <- ReprocessedAllenData("tophat_counts") > counts <- assay(sce, "tophat_counts") > > obj <- looking4clusters(t(counts), groups=colData(sce)[,'dissection_s'], + components=TRUE) Too large matrix, could cause performance problems with some methods, they will be omitted Running kmeans... Running pam and hclust... Running pca... Running tsne... Running mds... Running umap... > l4chtml(obj, includeData=TRUE) > > # SingleCellExperiment > libsizes <- colSums(counts) > size.factors <- libsizes/mean(libsizes) > logcounts(sce) <- log2(t(t(counts)/size.factors) + 1) > > pca_data <- prcomp(t(logcounts(sce)), rank=50) /system.slice/cron.service is not a snap cgroup > > reducedDims(sce) <- list(PCA=pca_data$x) > > obj <- looking4clusters(sce, groups="dissection_s") > l4chtml(object,directory="l4c_saved") The graph has been generated in the "/home/biocbuild/bbs-3.22-bioc/meat/looking4clusters.Rcheck/tests/l4c_saved" path. > > # seurat > library(Seurat) Loading required package: SeuratObject Loading required package: sp Attaching package: 'sp' The following object is masked from 'package:IRanges': %over% Attaching package: 'SeuratObject' The following object is masked from 'package:SummarizedExperiment': Assays The following object is masked from 'package:GenomicRanges': intersect The following object is masked from 'package:Seqinfo': intersect The following object is masked from 'package:IRanges': intersect The following object is masked from 'package:S4Vectors': intersect The following object is masked from 'package:BiocGenerics': intersect The following objects are masked from 'package:base': intersect, t Attaching package: 'Seurat' The following object is masked from 'package:SummarizedExperiment': Assays > library(Matrix) Attaching package: 'Matrix' The following object is masked from 'package:S4Vectors': expand > > test_mat <- Matrix(as.matrix(iris[,1:4]),sparse=T) > rownames(test_mat) <- paste0("sample",seq_len(nrow(test_mat))) > > seurat_object <- CreateSeuratObject(counts = test_mat) > seurat_object <- NormalizeData(seurat_object) Normalizing layer: counts Performing log-normalization 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| > seurat_object <- ScaleData(seurat_object, features = rownames(seurat_object)) Centering and scaling data matrix | | | 0% | |======================================================================| 100% > > seurat_object[["CITE"]] <- CreateAssayObject(counts = test_mat[1:6,]) > seurat_object <- NormalizeData(seurat_object, assay="CITE") Performing log-normalization 0% 10 20 30 40 50 60 70 80 90 100% [----|----|----|----|----|----|----|----|----|----| **************************************************| Warning messages: 1: The `slot` argument of `SetAssayData()` is deprecated as of SeuratObject 5.0.0. ℹ Please use the `layer` argument instead. ℹ The deprecated feature was likely used in the Seurat package. Please report the issue at <https://github.com/satijalab/seurat/issues>. 2: The `slot` argument of `GetAssayData()` is deprecated as of SeuratObject 5.0.0. ℹ Please use the `layer` argument instead. ℹ The deprecated feature was likely used in the Seurat package. Please report the issue at <https://github.com/satijalab/seurat/issues>. > seurat_object <- ScaleData(seurat_object, + features = rownames(seurat_object[["CITE"]]), assay="CITE") Centering and scaling data matrix | | | 0% | |======================================================================| 100% > > seurat_object <- FindVariableFeatures(seurat_object) Finding variable features for layer counts 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% [----|----|----|----|----|----|----|----|----|----| **************************************************| > seurat_object <- RunPCA(seurat_object, npcs = 2, + features = VariableFeatures(object = seurat_object)) Warning in svd.function(A = t(x = object), nv = npcs, ...) : You're computing too large a percentage of total singular values, use a standard svd instead. PC_ 1 Positive: sample129, sample79, sample52, sample103, sample112, sample133, sample102, sample143, sample105, sample113 sample114, sample124, sample127, sample78, sample117, sample148, sample144, sample140, sample147, sample125 sample138, sample128, sample104, sample136, sample121, sample118, sample150, sample100, sample84, sample139 Negative: sample6, sample7, sample27, sample23, sample22, sample16, sample20, sample41, sample45, sample18 sample24, sample32, sample46, sample15, sample17, sample34, sample19, sample43, sample36, sample3 sample5, sample37, sample39, sample1, sample48, sample29, sample50, sample49, sample11, sample28 PC_ 2 Positive: sample68, sample80, sample82, sample63, sample74, sample70, sample59, sample51, sample58, sample94 sample81, sample61, sample75, sample77, sample93, sample83, sample88, sample98, sample91, sample135 sample130, sample96, sample53, sample66, sample76, sample108, sample10, sample56, sample13, sample120 Negative: sample65, sample86, sample99, sample149, sample137, sample71, sample116, sample115, sample145, sample142 sample60, sample101, sample62, sample110, sample111, sample44, sample122, sample146, sample141, sample139 sample57, sample85, sample107, sample121, sample128, sample67, sample144, sample125, sample148, sample150 Warning message: In print.DimReduc(x = reduction.data, dims = ndims.print, nfeatures = nfeatures.print) : Only 2 dimensions have been computed. > > obj <- looking4clusters(seurat_object,assay="all") > > # all 0 column > wrongmat <- matrix(c(0,0,0,0,1,3,3,2,1,2,2,1),4) > obj <- looking4clusters(wrongmat) Running kmeans... Running pam and hclust... Running pca... Running tsne... Running mds... Running nmf... Running umap... Warning message: In run_nmf(object) : Your data has columns that are all zeros, this is not supported for nmf. > > # all 0 row > wrongmat <- matrix(c(0,1,1,1,0,3,3,2,0,2,2,1),4) > obj <- looking4clusters(wrongmat) Running kmeans... Running pam and hclust... Running pca... Running tsne... Running mds... Running nmf... Running umap... Warning message: In run_nmf(object) : Your data has rows that are all zeros, this is not supported for nmf. > > # negative entries > wrongmat <- matrix(c(3,1,1,1,3,-1,-1,2,3,2,2,1),4) > obj <- looking4clusters(wrongmat) Running kmeans... Running pam and hclust... Running pca... Running tsne... Running mds... Running nmf... Running umap... Warning message: In run_nmf(object) : Your data contains some negative entries, this is not supported for nmf. > > > proc.time() user system elapsed 105.272 3.374 108.562
looking4clusters.Rcheck/looking4clusters-Ex.timings
name | user | system | elapsed | |
addcluster | 0.001 | 0.001 | 0.001 | |
addreduction | 0.002 | 0.000 | 0.003 | |
l4chtml | 0.005 | 0.007 | 0.023 | |
looking4clusters | 6.462 | 0.303 | 6.764 | |