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###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD check --install=check:looking4clusters.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings looking4clusters_0.99.4.tar.gz
###
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* using log directory ‘/home/biocbuild/bbs-3.22-bioc/meat/looking4clusters.Rcheck’
* using R version 4.5.0 (2025-04-11)
* using platform: aarch64-unknown-linux-gnu
* R was compiled by
    aarch64-unknown-linux-gnu-gcc (GCC) 14.2.0
    GNU Fortran (GCC) 14.2.0
* running under: openEuler 24.03 (LTS)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* 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 ... ERROR
Running examples in ‘looking4clusters-Ex.R’ failed
The error most likely occurred in:

> base::assign(".ptime", proc.time(), pos = "CheckExEnv")
> ### Name: looking4clusters
> ### Title: Clustering determination and visualization
> ### Aliases: looking4clusters
> 
> ### ** Examples
> 
> obj <- looking4clusters(iris[,1:4], groups=iris[,5], threads = 2)
Running kmeans...
Running pam and hclust...

 *** caught segfault ***
address 0x2100000002, cause 'memory not mapped'

Traceback:
 1: parallelDist::parDist(data, method = distance, threads = threads)
 2: get_dissimilarity(distance, data, threads)
 3: run_pam_hclust(object, distance, agglomeration, selectedk, threads)
 4: running_clusters(object, selectedk, data, force_execution, distance,     agglomeration, threads)
 5: l4c(data, groups, components, running_all, distance, agglomeration,     selectedk, perplex, maxIter, threads, force_execution)
 6: looking4clusters(iris[, 1:4], groups = iris[, 5], threads = 2)
An irrecoverable exception occurred. R is aborting now ...
* 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 running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE

Status: 1 ERROR
See
  ‘/home/biocbuild/bbs-3.22-bioc/meat/looking4clusters.Rcheck/00check.log’
for details.

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###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD INSTALL looking4clusters
###
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* installing to library ‘/home/biocbuild/R/R-4.5.0/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)

R version 4.5.0 (2025-04-11) -- "How About a Twenty-Six"
Copyright (C) 2025 The R Foundation for Statistical Computing
Platform: aarch64-unknown-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)
> 
> 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%

'SeuratObject' was built with package 'Matrix' 1.7.3 but the current
version is 1.7.4; it is recomended that you reinstall 'SeuratObject' as
the ABI for 'Matrix' may have changed

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 
 81.426   2.053  83.667