##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD check --install=check:BUSseq.install-out.txt --library=/home/biocbuild/R/R/site-library --no-vignettes --timings BUSseq_1.12.0.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.20-bioc/meat/BUSseq.Rcheck’
* using R version 4.4.1 (2024-06-14)
* using platform: aarch64-unknown-linux-gnu
* R was compiled by
    gcc (GCC) 12.2.1 20220819 (openEuler 12.2.1-14)
    GNU Fortran (GCC) 10.3.1
* running under: openEuler 22.03 (LTS-SP1)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘BUSseq/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘BUSseq’ version ‘1.12.0’
* 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 ‘BUSseq’ can be installed ... OK
* used C++ compiler: ‘g++ (conda-forge gcc 14.2.0-1) 14.2.0’
* 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 contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking LazyData ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking line endings in C/C++/Fortran sources/headers ... OK
* checking line endings in Makefiles ... OK
* checking compilation flags in Makevars ... OK
* checking for GNU extensions in Makefiles ... OK
* checking for portable use of $(BLAS_LIBS) and $(LAPACK_LIBS) ... OK
* checking use of PKG_*FLAGS in Makefiles ... OK
* checking compiled code ... NOTE
Note: information on .o files is not available
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘BUSseq_example.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 NOTE
See
  ‘/home/biocbuild/bbs-3.20-bioc/meat/BUSseq.Rcheck/00check.log’
for details.

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/R/R/bin/R CMD INSTALL BUSseq
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/R/R-4.4.1/site-library’
* installing *source* package ‘BUSseq’ ...
** using staged installation
** libs
using C++ compiler: ‘g++ (conda-forge gcc 14.2.0-1) 14.2.0’
/home/biocbuild/R/R-4.4.1/share/make/shlib.mk:10: warning: overriding recipe for target 'BUSseq.so'
Makevars:9: warning: ignoring old recipe for target 'BUSseq.so'
g++ -std=gnu++17 -I"/home/biocbuild/R/R-4.4.1/include" -DNDEBUG   -I/usr/local/include   -fopenmp -fPIC  -g -O2  -Wall  -c BUSseq.cpp -o BUSseq.o
BUSseq.cpp: In function 'double postprob_DE_thr_fun(double*, double, int, int)':
BUSseq.cpp:964:69: warning: comparison of integer expressions of different signedness: 'int' and 'unsigned int' [-Wsign-compare]
  964 |     while((fdr <= _fdr_threshold) & (kappa <= postprob_DE_thr) & (i < (unsigned)vec_PPI.size())){
      |                                                                   ~~^~~~~~~~~~~~~~~~~~~~~~~~~~
BUSseq.cpp: In function 'void BUSseq_inference(int*, int*, int*, int*, char**, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, double*, int*, double*, int*, double*)':
BUSseq.cpp:2674:14: warning: variable 'All_Drop' set but not used [-Wunused-but-set-variable]
 2674 |         bool All_Drop = true;
      |              ^~~~~~~~
g++ -std=gnu++17 -I"/home/biocbuild/R/R-4.4.1/include" -DNDEBUG   -I/usr/local/include   -fopenmp -fPIC  -g -O2  -Wall  -c rngstream.cpp -o rngstream.o
g++ -std=gnu++17 -shared -L/home/biocbuild/R/R-4.4.1/lib -L/usr/local/lib -o BUSseq.so BUSseq.o rngstream.o -fopenmp -lopenblas -L/usr/lib/gcc/aarch64-linux-gnu/10.3.1 -lgfortran -lm -L/home/biocbuild/R/R-4.4.1/lib -lR
installing to /home/biocbuild/R/R-4.4.1/site-library/00LOCK-BUSseq/00new/BUSseq/libs
** R
** data
*** moving datasets to lazyload DB
** 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
** checking absolute paths in shared objects and dynamic libraries
** testing if installed package can be loaded from final location
** testing if installed package keeps a record of temporary installation path
* DONE (BUSseq)

R version 4.4.1 (2024-06-14) -- "Race for Your Life"
Copyright (C) 2024 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.

> #######################################
> # Apply BUSseq to the Simulation Data #
> #######################################
> library(BUSseq)
> library(SingleCellExperiment)
Loading required package: SummarizedExperiment
Loading required package: MatrixGenerics
Loading required package: matrixStats

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

Loading required package: GenomicRanges
Loading required package: stats4
Loading required package: BiocGenerics

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, aperm, append,
    as.data.frame, basename, cbind, colnames, dirname, do.call,
    duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    pmin.int, rank, rbind, rownames, sapply, saveRDS, setdiff, table,
    tapply, union, unique, unsplit, which.max, which.min

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: GenomeInfoDb
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.


Attaching package: 'Biobase'

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

    rowMedians

The following objects are masked from 'package:matrixStats':

    anyMissing, rowMedians

> RawCountData <- assay(BUSseqfits_example, "counts")
> batch_ind <- unlist(colData(BUSseqfits_example))
> sce <- SingleCellExperiment(assays = list(counts = RawCountData),
+                             colData = DataFrame(Batch_ind = batch_ind))
> BUSseqfits_res <- BUSseq_MCMC(ObservedData = sce, 
+                               seed = 1234, n.cores = 2,
+                               n.celltypes = 4, n.iterations = 500)
   conducting the posterior sampling...

[>                                       ] Finish 0.00k/0.50k iterations.
[====>                                   ] Finish 0.05k/0.50k iterations.
[========>                               ] Finish 0.10k/0.50k iterations.
[============>                           ] Finish 0.15k/0.50k iterations.
[================>                       ] Finish 0.20k/0.50k iterations.
[====================>                   ] Finish 0.25k/0.50k iterations.
[========================>               ] Finish 0.30k/0.50k iterations.
[============================>           ] Finish 0.35k/0.50k iterations.
[================================>       ] Finish 0.40k/0.50k iterations.
[====================================>   ] Finish 0.45k/0.50k iterations.
[========================================] Finish 0.50k/0.50k iterations.

   The MCMC sampling takes: 0.38 mins

   conducting the posterior inference...

   Posterior inference takes: 0.011 mins

> 
> ################################################
> # Extract Estimates from the BUSseqfits Object #
> ################################################
> 
> #return cell type indicators
> w.est <- celltypes(BUSseqfits_res)
Batch 1 cells' cell type indicators: 1,1,1... ...

Batch 2 cells' cell type indicators: 1,1,1... ...

The output format is an N-dimensional verctor.

> 
> #return the intercept and odds ratio of the logistic regression
> #for dropout events
> gamma.est <- dropout_coefficient_values(BUSseqfits_res)
The output format is a matrix.

Each row represents a batch, the first column corresponds to intercept and the second column is the odd ratio.

> 
> #return the log-scale baseline expression values
> alpha.est <-  baseline_expression_values(BUSseqfits_res)
The output format is a vector.

> 
> #return the cell-type effects
> beta.est <- celltype_effects(BUSseqfits_res)
The output format is a matrix.

Each row represents a gene, and each column corresponds to a cell type.

> 
> #return the mean expression levels
> mu.est <- celltype_mean_expression(BUSseqfits_res)
The output format is a matrix.

Each row represents a gene, and each column corresponds to a cell type.

> 
> #return the cell-specific global effects
> delta.est <- cell_effect_values(BUSseqfits_res)
The output format is an N-dimensional vector.

> 
> #return the location batch effects
> nu.est <- location_batch_effects(BUSseqfits_res)
The output format is a matrix.

Each row represents a gene, and each column corresponds to a batch.

> 
> #return the overdispersion parameters
> phi.est <- overdispersions(BUSseqfits_res)
The output format is a matrix.

Each row represents a gene, and each column corresponds to a batch.

> 
> #return the intrinsic gene indices
> D.est <- intrinsic_genes_BUSseq(BUSseqfits_res)
> 
> #return the BIC value
> BIC <- BIC_BUSseq(BUSseqfits_res)
BIC is 460947.370514477

The output is a scalar.

> 
> #return the raw read count matrix
> CountData_raw <- raw_read_counts(BUSseqfits_res)
The output format is a matrix, in which each row represents a gene and each column does a cell.

> 
> #return the imputed read count matrix
> CountData_imputed <- imputed_read_counts(BUSseqfits_res)
The output format is a matrix, in which each row represents a gene and each column does a cell.

> 
> #return the corrected read count matrix
> BUSseqfits_res <- corrected_read_counts(BUSseqfits_res)
   correcting read counts...

The corrected read count matrix is added into the output "SingleCellExperiment" object.

> 
> #################
> # Visualization #
> #################
> #generate the heatmap of raw read count data
> heatmap_data_BUSseq(BUSseqfits_res, project_name="Heatmap_raw")
null device 
          1 
> 
> #generate the heatmap of imputed read count data
> heatmap_data_BUSseq(BUSseqfits_res, data_type = "Imputed",
+                     project_name="Heatmap_imputed")
null device 
          1 
> 
> #generate the heatmap of corrected read count data
> heatmap_data_BUSseq(BUSseqfits_res, data_type = "Corrected", 
+                     project_name="Heatmap_corrected")
null device 
          1 
> 
> proc.time()
   user  system elapsed 
 56.106   0.872  35.792