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This page was generated on 2024-03-28 11:36:20 -0400 (Thu, 28 Mar 2024).

HostnameOSArch (*)R versionInstalled pkgs
nebbiolo1Linux (Ubuntu 22.04.3 LTS)x86_64R Under development (unstable) (2024-03-18 r86148) -- "Unsuffered Consequences" 4708
palomino3Windows Server 2022 Datacenterx64R Under development (unstable) (2024-03-16 r86144 ucrt) -- "Unsuffered Consequences" 4446
lconwaymacOS 12.7.1 Montereyx86_64R Under development (unstable) (2024-03-18 r86148) -- "Unsuffered Consequences" 4471
kunpeng2Linux (openEuler 22.03 LTS-SP1)aarch64R Under development (unstable) (2024-03-19 r86153) -- "Unsuffered Consequences" 4426
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Package 447/2270HostnameOS / ArchINSTALLBUILDCHECKBUILD BIN
COTAN 2.3.4  (landing page)
Galfrè Silvia Giulia
Snapshot Date: 2024-03-27 14:00:18 -0400 (Wed, 27 Mar 2024)
git_url: https://git.bioconductor.org/packages/COTAN
git_branch: devel
git_last_commit: 61f4c88
git_last_commit_date: 2024-03-18 08:23:15 -0400 (Mon, 18 Mar 2024)
nebbiolo1Linux (Ubuntu 22.04.3 LTS) / x86_64  OK    OK    OK  UNNEEDED, same version is already published
palomino3Windows Server 2022 Datacenter / x64  OK    OK    OK    OK  UNNEEDED, same version is already published
lconwaymacOS 12.7.1 Monterey / x86_64  OK    OK    OK    OK  UNNEEDED, same version is already published
kunpeng2Linux (openEuler 22.03 LTS-SP1) / aarch64  OK    OK    OK  

CHECK results for COTAN on nebbiolo1


To the developers/maintainers of the COTAN package:
- Allow up to 24 hours (and sometimes 48 hours) for your latest push to git@git.bioconductor.org:packages/COTAN.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.

raw results


Summary

Package: COTAN
Version: 2.3.4
Command: /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD check --install=check:COTAN.install-out.txt --library=/home/biocbuild/bbs-3.19-bioc/R/site-library --timings COTAN_2.3.4.tar.gz
StartedAt: 2024-03-27 21:27:37 -0400 (Wed, 27 Mar 2024)
EndedAt: 2024-03-27 21:54:16 -0400 (Wed, 27 Mar 2024)
EllapsedTime: 1599.0 seconds
RetCode: 0
Status:   OK  
CheckDir: COTAN.Rcheck
Warnings: 0

Command output

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD check --install=check:COTAN.install-out.txt --library=/home/biocbuild/bbs-3.19-bioc/R/site-library --timings COTAN_2.3.4.tar.gz
###
##############################################################################
##############################################################################


* using log directory ‘/home/biocbuild/bbs-3.19-bioc/meat/COTAN.Rcheck’
* using R Under development (unstable) (2024-03-18 r86148)
* using platform: x86_64-pc-linux-gnu
* R was compiled by
    gcc (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
    GNU Fortran (Ubuntu 11.4.0-1ubuntu1~22.04) 11.4.0
* running under: Ubuntu 22.04.4 LTS
* using session charset: UTF-8
* checking for file ‘COTAN/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘COTAN’ version ‘2.3.4’
* 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 for sufficient/correct file permissions ... OK
* checking whether package ‘COTAN’ 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 ... NOTE
Unexported object imported by a ':::' call: ‘ggplot2:::ggname’
  See the note in ?`:::` about the use of this operator.
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
GDIPlot: no visible binding for global variable ‘sum.raw.norm’
GDIPlot: no visible binding for global variable ‘GDI’
UMAPPlot: no visible binding for global variable ‘x’
UMAPPlot: no visible binding for global variable ‘y’
calculateG: no visible binding for global variable ‘observedNN’
calculateG: no visible binding for global variable ‘observedNY’
calculateG: no visible binding for global variable ‘observedYN’
calculateG: no visible binding for global variable ‘observedYY’
calculateG: no visible binding for global variable ‘expectedNN’
calculateG: no visible binding for global variable ‘expectedNY’
calculateG: no visible binding for global variable ‘expectedYN’
calculateG: no visible binding for global variable ‘expectedYY’
calculatePartialCoex: no visible binding for global variable
  ‘expectedNN’
calculatePartialCoex: no visible binding for global variable
  ‘expectedNY’
calculatePartialCoex: no visible binding for global variable
  ‘expectedYN’
calculatePartialCoex: no visible binding for global variable
  ‘expectedYY’
calculatePartialCoex: no visible binding for global variable
  ‘observedYY’
calculatePartialCoex: no visible binding for global variable ‘.’
cellsUniformClustering: no visible binding for global variable
  ‘objSeurat’
cellsUniformClustering: no visible binding for global variable
  ‘usedMaxResolution’
checkClusterUniformity: ... may be used in an incorrect context:
  ‘c(..., nuPlot, zoomedNuPlot)’
checkClusterUniformity: no visible binding for global variable ‘nuPlot’
checkClusterUniformity: no visible binding for global variable
  ‘zoomedNuPlot’
cleanPlots: no visible binding for global variable ‘PC1’
cleanPlots: no visible binding for global variable ‘PC2’
cleanPlots: no visible binding for global variable ‘n’
cleanPlots: no visible binding for global variable ‘means’
cleanPlots: no visible binding for global variable ‘nu’
clustersMarkersHeatmapPlot: no visible binding for global variable
  ‘condName’
clustersMarkersHeatmapPlot: no visible binding for global variable
  ‘conditions’
clustersSummaryPlot: no visible binding for global variable ‘keys’
clustersSummaryPlot: no visible binding for global variable ‘values’
clustersSummaryPlot: no visible binding for global variable
  ‘CellNumber’
clustersSummaryPlot: no visible binding for global variable ‘ExpGenes’
clustersSummaryPlot: no visible binding for global variable ‘Cluster’
clustersSummaryPlot: no visible binding for global variable ‘Condition’
establishGenesClusters: no visible binding for global variable
  ‘secondaryMarkers’
establishGenesClusters: no visible binding for global variable ‘GCS’
establishGenesClusters: no visible binding for global variable
  ‘rankGenes’
expectedContingencyTables: no visible binding for global variable
  ‘expectedN’
expectedPartialContingencyTables: no visible binding for global
  variable ‘expectedNN’
expectedPartialContingencyTables: no visible binding for global
  variable ‘expectedN’
geom_flat_violin : <anonymous>: no visible binding for global variable
  ‘group’
geom_flat_violin : <anonymous>: no visible binding for global variable
  ‘y’
geom_flat_violin : <anonymous>: no visible binding for global variable
  ‘x’
geom_flat_violin : <anonymous>: no visible binding for global variable
  ‘width’
geom_flat_violin : <anonymous>: no visible binding for global variable
  ‘violinwidth’
geom_flat_violin : <anonymous>: no visible binding for global variable
  ‘xmax’
geom_flat_violin : <anonymous>: no visible binding for global variable
  ‘xminv’
geom_flat_violin : <anonymous>: no visible binding for global variable
  ‘xmaxv’
heatmapPlot: no visible binding for global variable ‘g2’
mergeUniformCellsClusters : testPairListMerge: no visible binding for
  global variable ‘cl1’
mergeUniformCellsClusters : testPairListMerge: no visible binding for
  global variable ‘cl2’
mitochondrialPercentagePlot: no visible binding for global variable
  ‘mit.percentage’
observedContingencyTables: no visible binding for global variable
  ‘observedY’
observedPartialContingencyTables: no visible binding for global
  variable ‘observedYY’
observedPartialContingencyTables: no visible binding for global
  variable ‘observedY’
reorderClusterization: no visible global function definition for
  ‘as.dist’
scatterPlot: no visible binding for global variable ‘.x’
calculateCoex,COTAN: no visible binding for global variable
  ‘expectedNN’
calculateCoex,COTAN: no visible binding for global variable
  ‘expectedNY’
calculateCoex,COTAN: no visible binding for global variable
  ‘expectedYN’
calculateCoex,COTAN: no visible binding for global variable
  ‘expectedYY’
calculateCoex,COTAN: no visible binding for global variable
  ‘observedYY’
calculateCoex,COTAN: no visible binding for global variable ‘.’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘rawNorm’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘nu’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘lambda’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘a’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘hk’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘clusters’
coerce,COTAN-scCOTAN: no visible binding for global variable
  ‘clusterData’
Undefined global functions or variables:
  . .x CellNumber Cluster Condition ExpGenes GCS GDI PC1 PC2 a as.dist
  cl1 cl2 clusterData clusters condName conditions expectedN expectedNN
  expectedNY expectedYN expectedYY g2 group hk keys lambda means
  mit.percentage n nu nuPlot objSeurat observedNN observedNY observedY
  observedYN observedYY rankGenes rawNorm secondaryMarkers sum.raw.norm
  usedMaxResolution values violinwidth width x xmax xmaxv xminv y
  zoomedNuPlot
Consider adding
  importFrom("stats", "as.dist")
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 ... OK
Examples with CPU (user + system) or elapsed time > 5s
                           user system elapsed
UniformClusters         156.579  0.764 155.855
CalculatingCOEX          51.642  1.477  51.826
HeatmapPlots             46.523  0.957  46.504
HandlingClusterizations  31.835  0.355  32.172
ParametersEstimations    22.978  0.182  23.161
GenesCoexSpace           11.762  0.240  11.652
COTANObjectCreation      10.411  0.247  10.329
RawDataCleaning           6.641  0.129   6.701
* checking for unstated dependencies in ‘tests’ ... OK
* checking tests ...
  Running ‘outputTestDatasetCreation.R’
  Running ‘spelling.R’
  Running ‘testthat.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: 2 NOTEs
See
  ‘/home/biocbuild/bbs-3.19-bioc/meat/COTAN.Rcheck/00check.log’
for details.


Installation output

COTAN.Rcheck/00install.out

##############################################################################
##############################################################################
###
### Running command:
###
###   /home/biocbuild/bbs-3.19-bioc/R/bin/R CMD INSTALL COTAN
###
##############################################################################
##############################################################################


* installing to library ‘/home/biocbuild/bbs-3.19-bioc/R/site-library’
* installing *source* package ‘COTAN’ ...
** using staged installation
** R
** data
** inst
** byte-compile and prepare package for lazy loading
Note: ... may be used in an incorrect context 
** 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 (COTAN)

Tests output

COTAN.Rcheck/tests/outputTestDatasetCreation.Rout


R Under development (unstable) (2024-03-18 r86148) -- "Unsuffered Consequences"
Copyright (C) 2024 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.

> 
> # Creates the files to be reloaded by the tests for comparisons
> library(zeallot)
> 
> outputTestDatasetCreation <- function(testsDir = file.path("tests",
+                                                            "testthat")) {
+   utils::data("test.dataset", package = "COTAN")
+   options(parallelly.fork.enable = TRUE)
+ 
+   obj <- COTAN(raw = test.dataset)
+   obj <- initializeMetaDataset(obj, GEO = " ",
+                                sequencingMethod = "artificial",
+                                sampleCondition = "test")
+ 
+   obj <- proceedToCoex(obj, cores = 12L, saveObj = FALSE)
+   #saveRDS(obj, file = file.path(testsDir,"temp.RDS"))
+ 
+   cell.names.test  <- getCells(obj)[c(1L:10L, 591L:610L, 991L:1000L)]
+   genes.names.test <- getGenes(obj)[c(1L:10L, 291L:310L, 591L: 600L)]
+   saveRDS(cell.names.test, file.path(testsDir, "cell.names.test.RDS"))
+   saveRDS(genes.names.test, file.path(testsDir, "genes.names.test.RDS"))
+ 
+   dispersion.test <- getDispersion(obj)[genes.names.test]
+   saveRDS(dispersion.test, file.path(testsDir, "dispersion.test.RDS"))
+ 
+   raw.norm.test <- getNormalizedData(obj)[genes.names.test, cell.names.test]
+   saveRDS(raw.norm.test, file.path(testsDir, "raw.norm.test.RDS"))
+ 
+   coex.test <- getGenesCoex(obj, genes = genes.names.test, zeroDiagonal = FALSE)
+   saveRDS(coex.test, file.path(testsDir, "coex.test.RDS"))
+ 
+   lambda.test <- getLambda(obj)[genes.names.test]
+   saveRDS(lambda.test, file.path(testsDir, "lambda.test.RDS"))
+ 
+   GDI.test <- calculateGDI(obj)
+   GDI.test <- GDI.test[genes.names.test, ]
+   saveRDS(GDI.test, file.path(testsDir, "GDI.test.RDS"))
+ 
+   nu.test <- getNu(obj)[cell.names.test]
+   saveRDS(nu.test, file.path(testsDir, "nu.test.RDS"))
+ 
+   pval.test <- calculatePValue(obj, geneSubsetCol = genes.names.test)
+   saveRDS(pval.test, file.path(testsDir, "pval.test.RDS"))
+ 
+   GDIThreshold <- 1.5
+   initialResolution <- 0.8
+ 
+   clusters <- cellsUniformClustering(obj, GDIThreshold = GDIThreshold,
+                                      initialResolution =   initialResolution,
+                                      cores = 12L, saveObj = FALSE)[["clusters"]]
+   saveRDS(clusters, file.path(testsDir, "clusters1.RDS"))
+ 
+   coexDF <- DEAOnClusters(obj, clusters = clusters)
+   obj <- addClusterization(obj, clName = "clusters",
+                            clusters = clusters, coexDF = coexDF)
+ 
+   saveRDS(coexDF[genes.names.test, ],
+           file.path(testsDir, "coex.test.cluster1.RDS"))
+ 
+   pvalDF <- pValueFromDEA(coexDF, getNumCells(obj), method = "none")
+ 
+   saveRDS(pvalDF[genes.names.test, ],
+           file.path(testsDir, "pval.test.cluster1.RDS"))
+ 
+   c(mergedClusters, mCoexDF) %<-%
+     mergeUniformCellsClusters(objCOTAN = obj,
+                               clusters = NULL,
+                               GDIThreshold = GDIThreshold,
+                               cores = 12L,
+                               distance = "cosine",
+                               hclustMethod = "ward.D2",
+                               saveObj = FALSE)
+ 
+   saveRDS(mergedClusters[genes.names.test],
+           file.path(testsDir, "cluster_data_merged.RDS"))
+ }
> 
> proc.time()
   user  system elapsed 
  0.179   0.023   0.190 

COTAN.Rcheck/tests/spelling.Rout


R Under development (unstable) (2024-03-18 r86148) -- "Unsuffered Consequences"
Copyright (C) 2024 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.

> if (requireNamespace('spelling', quietly = TRUE))
+   spelling::spell_check_test(vignettes = TRUE, error = FALSE,
+                              skip_on_cran = TRUE)
NULL
> 
> proc.time()
   user  system elapsed 
  0.171   0.023   0.184 

COTAN.Rcheck/tests/testthat.Rout


R Under development (unstable) (2024-03-18 r86148) -- "Unsuffered Consequences"
Copyright (C) 2024 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.

> Sys.setenv(R_TESTS = "")
> library(testthat)
> library(COTAN)
> test_check("COTAN")
Setting new log level to 3
Initializing `COTAN` meta-data
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [10] genes and [20] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.9033203125 | max: 4.6796875 | % negative: 10
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.181818181818182
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [10] genes and [20] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 4 genes batches from [1:2] to [7:8]
Executing 1 genes batches from [9:10] to [9:10]
Estimate dispersion: DONE
dispersion | min: 0.9033203125 | max: 4.6796875 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 4 cells batches from [1:3] to [10:11]
Executing 3 cells batches from [12:14] to [18:20]
Estimate nu: DONE
nu change (abs) | max: 1.75595238095238 | median:  1.07174634176587 | mean:  1.07174634176587
Estimate 'dispersion'/'nu': START
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 1.0362548828125 | max: 4.60986328125 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.0265938895089288 | median:  0.0144680038331048 | mean:  0.0144680038331048
Nu mean: 1.69633192486233
Marginal errors | max: 1.95570586131367 | median 1.32068160171502 | mean: 1.33375826507259
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.058837890625 | max: 3.528076171875 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.416683423613994 | median:  0.239880630367975 | mean:  0.239880630367975
Nu mean: 0.823197206753982
Marginal errors | max: 0.836359531101206 | median 0.703684202571891 | mean: 0.645537958989614
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.32879638671875 | max: 4.0302734375 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.164237872898673 | median:  0.0955985184389135 | mean:  0.0955985184389135
Nu mean: 1.06863935445976
Marginal errors | max: 0.259872988828244 | median 0.213703042752633 | mean: 0.197386407582083
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.2294921875 | max: 3.8720703125 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.055185575120883 | median:  0.0319991762044448 | mean:  0.0319991762044448
Nu mean: 0.976813601083562
Marginal errors | max: 0.0951586919577032 | median 0.0794297037094669 | mean: 0.0724140148396652
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.2637939453125 | max: 3.929443359375 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.0196211148938294 | median:  0.01138609597457 | mean:  0.01138609597457
Nu mean: 1.00823501891926
Marginal errors | max: 0.0327747321002274 | median 0.0272104747849529 | mean: 0.0248963830312036
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.25177001953125 | max: 3.90966796875 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.00670099066960717 | median:  0.00388888266671264 | mean:  0.00388888266671264
Nu mean: 0.997187891997105
Marginal errors | max: 0.0114324509186883 | median 0.00942326497706159 | mean: 0.00863113610779536
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.25592041015625 | max: 3.91650390625 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.00230093811689414 | median:  0.00132529122122246 | mean:  0.00132529122122246
Nu mean: 1.00097564689567
Marginal errors | max: 0.00387133150664809 | median 0.0031091017608853 | mean: 0.00286071175800213
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.2545166015625 | max: 3.914306640625 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.000837011646904529 | median:  0.000470837393363011 | mean:  0.000470837393363011
Nu mean: 0.999633825746458
Marginal errors | max: 0.00122501723202184 | median 0.00102126435760308 | mean: 0.000943992659051851
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.2550048828125 | max: 3.9150390625 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.000209227351122054 | median:  0.000122070312500028 | mean:  0.000122070312500028
Nu mean: 1.00008715703862
Marginal errors | max: 0.000364602956583582 | median 0.000313956936819793 | mean: 0.000282899574318485
Estimate dispersion/nu: DONE
Estimate 'dispersion'/'nu': START
Initializing `COTAN` meta-data
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [10] genes and [20] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.9033203125 | max: 4.6796875 | % negative: 10
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.181818181818182
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Calculate cells' coex: START
Retrieving expected cells' contingency table
calculating NN.. done
calculating YN..NY..YY..t().. done
Calculating cells' coex normalization factor
Fraction of genes with very low expected contingency tables: 0
Retrieving observed cells' yes/yes contingency table
calculating YY.. done
Estimating cells' coex
Calculate cells' coex: DONE
Initializing `COTAN` meta-data
Initializing `COTAN` meta-data
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [10] genes and [20] cells
Estimate 'dispersion'/'nu': START
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.903564453125 | max: 4.679443359375 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 1.75719246031746 | median:  1.07229953342014 | mean:  1.07229953342014
Nu mean: 1.68489292689732
Marginal errors | max: 1.73564890252257 | median 1.37996360874076 | mean: 1.32180348113228
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.0655517578125 | max: 3.5439453125 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.402649984216273 | median:  0.231868788425666 | mean:  0.231868788425666
Nu mean: 0.829218804209393
Marginal errors | max: 0.803213159865939 | median 0.677497553540579 | mean: 0.61937543089282
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.3260498046875 | max: 4.026123046875 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.158004893526231 | median:  0.0919692884670312 | mean:  0.0919692884670312
Nu mean: 1.0660356050592
Marginal errors | max: 0.250724014302325 | median 0.206232152124435 | mean: 0.190425623677197
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.23040771484375 | max: 3.8736572265625 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.0532774732102337 | median:  0.0308837890624999 | mean:  0.0308837890624999
Nu mean: 0.977606315852266
Marginal errors | max: 0.0916983669060105 | median 0.0765266929824948 | mean: 0.0697593208689693
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.26348876953125 | max: 3.928955078125 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.0189966206463044 | median:  0.0110199320575908 | mean:  0.0110199320575908
Nu mean: 1.00797668858871
Marginal errors | max: 0.0317151207459254 | median 0.0262702142278233 | mean: 0.0240886952086955
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.2518310546875 | max: 3.9097900390625 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.00670088501353994 | median:  0.00388888101583662 | mean:  0.00388888101583662
Nu mean: 0.997187996002297
Marginal errors | max: 0.011369331635624 | median 0.00939669372836338 | mean: 0.00860715734056949
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.2559814453125 | max: 3.9166259765625 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.00251007446998996 | median:  0.00144735987374958 | mean:  0.00144735987374958
Nu mean: 1.00106271459624
Marginal errors | max: 0.00406746973787264 | median 0.00343393462175801 | mean: 0.00313496757119527
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.25445556640625 | max: 3.9140625 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.000837027590858019 | median:  0.000488281249999889 | mean:  0.000488281249999889
Nu mean: 0.999651253659142
Marginal errors | max: 0.00143433714371355 | median 0.00116636244706836 | mean: 0.00109289166947804
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:10] to [1:10]
Estimate dispersion: DONE
dispersion | min: 0.2550048828125 | max: 3.9150390625 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 1 cells batches from [1:20] to [1:20]
Estimate nu: DONE
nu change (abs) | max: 0.000209227688885871 | median:  0.0001220703125 | mean:  0.0001220703125
Nu mean: 1.00008715737639
Marginal errors | max: 0.000379524846206181 | median 0.000325685250687435 | mean: 0.000295532844331703
Estimate dispersion/nu: DONE
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.181818181818182
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Calculate cells' coex: START
Retrieving expected cells' contingency table
calculating NN.. done
calculating YN..NY..YY..t().. done
Calculating cells' coex normalization factor
Fraction of genes with very low expected contingency tables: 0
Retrieving observed cells' yes/yes contingency table
calculating YY.. done
Estimating cells' coex
Calculate cells' coex: DONE
Calculate genes' partial coex: START
Retrieving expected genes' partial contingency table
calculating partial NN.. done
calculating partial NY..YN..YY.. done
Calculating genes' partial coex normalization factor
Fraction of genes with very low expected contingency tables: 0.325
Retrieving observed genes' yes/yes partial contingency table
calculating partial YY.. done
Estimating genes' partial coex
Calculate genes' partial coex: DONE
Calculate cells' partial coex: START
Retrieving expected cells' partial contingency table
calculating partial NN.. done
calculating partial YN..NY..YY.. done
Calculating cells' partial coex normalization factor
Fraction of genes with very low expected contingency tables: 0
Retrieving observed cells' yes/yes partial contingency table
calculating partial YY.. done
Estimating cells' partial coex
Calculate cells' partial coex: DONE
Asked to drop 2 genes and 0 cells
Asked to drop 0 genes and 4 cells
Asked to drop 2 genes and 2 cells

Attaching package: 'rlang'

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

    is_false, is_null, is_true

Genes/cells selection done: dropped [0] genes and [0] cells
Working on [10] genes and [20] cells
calculating YY.. done
calculating YY.. done
calculating YN..NY..NN..t().. done
Estimate dispersion: START
Effective number of cores used: 1
Executing 3 genes batches from [1:3] to [8:10]
Estimate dispersion: DONE
dispersion | min: 0.9033203125 | max: 4.6796875 | % negative: 10
calculating NN.. done
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.181818181818182
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Calculate genes' partial coex: START
Retrieving expected genes' partial contingency table
calculating partial NN.. done
calculating partial NY..YN..YY.. done
Calculating genes' partial coex normalization factor
Fraction of genes with very low expected contingency tables: 0.1
Retrieving observed genes' yes/yes partial contingency table
calculating partial YY.. done
Estimating genes' partial coex
Calculate genes' partial coex: DONE
Calculate genes' partial coex: START
Retrieving expected genes' partial contingency table
calculating partial NN.. done
calculating partial NY..YN..YY.. done
Calculating genes' partial coex normalization factor
Fraction of genes with very low expected contingency tables: 0.4
Retrieving observed genes' yes/yes partial contingency table
calculating partial YY.. done
Estimating genes' partial coex
Calculate genes' partial coex: DONE
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [10] genes and [20] cells
calculating YY.. done
calculating YY.. done
calculating NY..YN..NN..t().. done
Estimate 'dispersion'/'nu': START
Estimate dispersion: START
Effective number of cores used: 1
Executing 3 genes batches from [1:3] to [8:10]
Estimate dispersion: DONE
dispersion | min: 0.903564453125 | max: 4.679443359375 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 4 cells batches from [1:4] to [13:16]
Executing 1 cells batches from [17:20] to [17:20]
Estimate nu: DONE
nu change (abs) | max: 1.75719246031746 | median:  1.07229953342014 | mean:  1.07229953342014
Nu mean: 1.68489292689732
Marginal errors | max: 0.255353289373158 | median 0.0807577993228143 | mean: 0.101980750205761
Estimate dispersion: START
Effective number of cores used: 1
Executing 3 genes batches from [1:3] to [8:10]
Estimate dispersion: DONE
dispersion | min: 1.037109375 | max: 4.6107177734375 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 4 cells batches from [1:4] to [13:16]
Executing 1 cells batches from [17:20] to [17:20]
Estimate nu: DONE
nu change (abs) | max: 0.0273438105507502 | median:  0.0148852611818011 | mean:  0.0148852611818011
Nu mean: 1.69735147626627
Marginal errors | max: 0.00326864580272002 | median 0.00111524657842832 | mean: 0.00131556083122533
Estimate dispersion: START
Effective number of cores used: 1
Executing 3 genes batches from [1:3] to [8:10]
Estimate dispersion: DONE
dispersion | min: 1.03887939453125 | max: 4.6097412109375 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 4 cells batches from [1:4] to [13:16]
Executing 1 cells batches from [17:20] to [17:20]
Estimate nu: DONE
nu change (abs) | max: 0 | median:  0 | mean:  0
Nu mean: 1.69735147626627
Marginal errors | max: 7.56328383637594e-05 | median 1.72948087246994e-05 | mean: 2.99252342141898e-05
Estimate dispersion/nu: DONE
calculating NN.. done
calculating NN.. done
calculating YN..NY..YY..t().. done
Calculate cells' coex: START
Retrieving expected cells' contingency table
calculating NN.. done
calculating YN..NY..YY..t().. done
Calculating cells' coex normalization factor
Fraction of genes with very low expected contingency tables: 0
Retrieving observed cells' yes/yes contingency table
calculating YY.. done
Estimating cells' coex
Calculate cells' coex: DONE
Calculate cells' partial coex: START
Retrieving expected cells' partial contingency table
calculating partial NN.. done
calculating partial YN..NY..YY.. done
Calculating cells' partial coex normalization factor
Fraction of genes with very low expected contingency tables: 0
Retrieving observed cells' yes/yes partial contingency table
calculating partial YY.. done
Estimating cells' partial coex
Calculate cells' partial coex: DONE
Calculate cells' partial coex: START
Retrieving expected cells' partial contingency table
calculating partial NN.. done
calculating partial YN..NY..YY.. done
Calculating cells' partial coex normalization factor
Fraction of genes with very low expected contingency tables: 0
Retrieving observed cells' yes/yes partial contingency table
calculating partial YY.. done
Estimating cells' partial coex
Calculate cells' partial coex: DONE
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [10] genes and [20] cells
Estimate 'dispersion'/'nu': START
Estimate dispersion: START
Effective number of cores used: 1
Executing 3 genes batches from [1:3] to [8:10]
Estimate dispersion: DONE
dispersion | min: 0.903564453125 | max: 4.679443359375 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 4 cells batches from [1:4] to [13:16]
Executing 1 cells batches from [17:20] to [17:20]
Estimate nu: DONE
nu change (abs) | max: 1.75719246031746 | median:  1.07229953342014 | mean:  1.07229953342014
Nu mean: 1.68489292689732
Marginal errors | max: 0.255353289373158 | median 0.0807577993228143 | mean: 0.101980750205761
Estimate dispersion: START
Effective number of cores used: 1
Executing 3 genes batches from [1:3] to [8:10]
Estimate dispersion: DONE
dispersion | min: 1.037109375 | max: 4.6107177734375 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 4 cells batches from [1:4] to [13:16]
Executing 1 cells batches from [17:20] to [17:20]
Estimate nu: DONE
nu change (abs) | max: 0.0273438105507502 | median:  0.0148852611818011 | mean:  0.0148852611818011
Nu mean: 1.69735147626627
Marginal errors | max: 0.00326864580272002 | median 0.00111524657842832 | mean: 0.00131556083122533
Estimate dispersion: START
Effective number of cores used: 1
Executing 3 genes batches from [1:3] to [8:10]
Estimate dispersion: DONE
dispersion | min: 1.03887939453125 | max: 4.6097412109375 | % negative: 10
Estimate nu: START
Effective number of cores used: 1
Executing 4 cells batches from [1:4] to [13:16]
Executing 1 cells batches from [17:20] to [17:20]
Estimate nu: DONE
nu change (abs) | max: 0 | median:  0 | mean:  0
Nu mean: 1.69735147626627
Marginal errors | max: 7.56328383637594e-05 | median 1.72948087246994e-05 | mean: 2.99252342141898e-05
Estimate dispersion/nu: DONE
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.181818181818182
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Calculating S: START
Calculating S: DONE
Calculating G: START
calculating YY.. done
calculating YN..NY..NN..t().. done
calculating NN.. done
calculating NY..YN..YY..t().. done
Estimating G
Calculating G: DONE
Using S
Calculating S: START
Calculating S: DONE
calculating PValues: START
Get p-values genome wide on columns and genome wide on rows
calculating PValues: DONE
Using G
Calculating G: START
calculating YY.. done
calculating YN..NY..NN..t().. done
calculating NN.. done
calculating NY..YN..YY..t().. done
Estimating G
Calculating G: DONE
calculating PValues: START
Get p-values on a set of genes on columns and on a set of genes on rows
calculating PValues: DONE
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
Calculate GDI dataframe: START
Using G
Calculating G: START
calculating YY.. done
calculating YN..NY..NN..t().. done
calculating NN.. done
calculating NY..YN..YY..t().. done
Estimating G
Calculating G: DONE
S matrix sorted
Calculate GDI dataframe: DONE
Calculating S: START
Calculating S: DONE
S matrix sorted
S matrix sorted
Initializing `COTAN` meta-data
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [1200] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: 0.2197265625 | max: 6.08056640625 | % negative: 0
Only analysis time 0.0428006887435913
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.106961651643117
Total time 0.162815765539805
Initializing `COTAN` meta-data
Condition test
n cells 1200
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [1200] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: 0.2197265625 | max: 6.08056640625 | % negative: 0
Only analysis time 0.0429008364677429
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.102625358104706
Total time 0.156280521551768
Using S
Calculating S: START
Calculating S: DONE
calculating PValues: START
Get p-values on a set of genes on columns and genome wide on rows
calculating PValues: DONE
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
Initializing `COTAN` meta-data
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [1200] cells
PCA: START
PCA: DONE
Hierarchical clustering: START
Hierarchical clustering: DONE
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: 0.2197265625 | max: 6.08056640625 | % negative: 0
Cotan genes' coex estimation not requested
Total time 0.119648802280426
Saving elaborated data locally at: /tmp/RtmpIcook5/test.cotan.RDS
Creating cells' uniform clustering: START
In iteration 0 the number of cells to re-cluster is 1200 cells belonging to 0 clusters
Creating Seurat object: START
Normalizing layer: counts
Performing log-normalization
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
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%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Centering and scaling data matrix

  |                                                                            
  |                                                                      |   0%
  |                                                                            
  |======================================================================| 100%
PC_ 1 
Positive:  g-000558, g-000570, g-000499, g-000504, g-000546, g-000506, g-000503, g-000517, g-000596, g-000528 
	   g-000527, g-000580, g-000592, g-000578, g-000509, g-000488, g-000555, g-000577, g-000534, g-000583 
	   g-000598, g-000535, g-000512, g-000554, g-000519, g-000525, g-000548, g-000544, g-000502, g-000541 
Negative:  g-000133, g-000007, g-000074, g-000141, g-000057, g-000235, g-000170, g-000019, g-000195, g-000140 
	   g-000183, g-000031, g-000046, g-000178, g-000177, g-000161, g-000157, g-000139, g-000011, g-000135 
	   g-000125, g-000208, g-000061, g-000085, g-000204, g-000104, g-000237, g-000004, g-000038, g-000128 
PC_ 2 
Positive:  g-000039, g-000050, g-000175, g-000078, g-000116, g-000189, g-000135, g-000047, g-000072, g-000087 
	   g-000063, g-000235, g-000066, g-000109, g-000018, g-000074, g-000231, g-000136, g-000034, g-000207 
	   g-000128, g-000167, g-000171, g-000049, g-000182, g-000013, g-000054, g-000062, g-000240, g-000158 
Negative:  g-000584, g-000583, g-000544, g-000519, g-000575, g-000516, g-000585, g-000486, g-000489, g-000539 
	   g-000484, g-000502, g-000523, g-000595, g-000305, g-000574, g-000599, g-000589, g-000509, g-000538 
	   g-000526, g-000551, g-000579, g-000590, g-000445, g-000556, g-000543, g-000501, g-000504, g-000570 
PC_ 3 
Positive:  g-000015, g-000575, g-000483, g-000316, g-000025, g-000364, g-000050, g-000278, g-000443, g-000360 
	   g-000332, g-000124, g-000212, g-000387, g-000536, g-000252, g-000251, g-000321, g-000501, g-000470 
	   g-000582, g-000106, g-000455, g-000368, g-000081, g-000104, g-000437, g-000288, g-000386, g-000317 
Negative:  g-000211, g-000337, g-000129, g-000185, g-000397, g-000403, g-000253, g-000098, g-000390, g-000303 
	   g-000052, g-000088, g-000463, g-000468, g-000236, g-000209, g-000005, g-000375, g-000342, g-000262 
	   g-000388, g-000091, g-000413, g-000285, g-000003, g-000095, g-000142, g-000205, g-000432, g-000241 
PC_ 4 
Positive:  g-000334, g-000398, g-000292, g-000095, g-000097, g-000202, g-000382, g-000195, g-000007, g-000079 
	   g-000086, g-000240, g-000263, g-000317, g-000576, g-000557, g-000160, g-000154, g-000214, g-000228 
	   g-000313, g-000053, g-000524, g-000374, g-000568, g-000188, g-000358, g-000528, g-000362, g-000150 
Negative:  g-000379, g-000193, g-000212, g-000434, g-000593, g-000513, g-000177, g-000223, g-000069, g-000131 
	   g-000162, g-000345, g-000462, g-000484, g-000448, g-000229, g-000365, g-000302, g-000010, g-000366 
	   g-000051, g-000535, g-000269, g-000270, g-000155, g-000529, g-000373, g-000008, g-000393, g-000306 
PC_ 5 
Positive:  g-000451, g-000339, g-000295, g-000328, g-000544, g-000061, g-000227, g-000391, g-000556, g-000237 
	   g-000067, g-000165, g-000449, g-000591, g-000087, g-000129, g-000197, g-000203, g-000487, g-000505 
	   g-000333, g-000029, g-000271, g-000064, g-000583, g-000156, g-000448, g-000153, g-000526, g-000393 
Negative:  g-000518, g-000108, g-000186, g-000170, g-000401, g-000337, g-000047, g-000599, g-000432, g-000578 
	   g-000042, g-000065, g-000493, g-000261, g-000533, g-000256, g-000560, g-000596, g-000368, g-000381 
	   g-000535, g-000338, g-000215, g-000159, g-000365, g-000234, g-000173, g-000387, g-000225, g-000272 
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1200
Number of edges: 55489

Running Louvain algorithm with multilevel refinement...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.5973
Number of communities: 4
Elapsed time: 0 seconds
Used resolution for Seurat clusterization is: 0.8
21:41:19 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
21:41:19 Read 1200 rows and found 50 numeric columns
21:41:19 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
21:41:19 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:20 Writing NN index file to temp file /tmp/RtmpIcook5/file278ea379c251
21:41:20 Searching Annoy index using 1 thread, search_k = 3000
21:41:20 Annoy recall = 100%
21:41:21 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
21:41:22 Initializing from normalized Laplacian + noise (using RSpectra)
21:41:22 Commencing optimization for 500 epochs, with 42274 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:41:24 Optimization finished
Creating PDF UMAP in file:/tmp/RtmpIcook5/test/reclustering_0/pdf_umap.pdf
Creating Seurat object: DONE
* checking uniformity of cluster '0' of 4 clusters
Asked to drop 0 genes and 847 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [353] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: -0.038818359375 | max: 11.109375 | % negative: 5
Only analysis time 0.0373998085657756
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.000521353300055463
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.102976274490356
Total time 0.151982005437215
Checking uniformity for the cluster '0' with 353 cells
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
GDI plot
Removed 0 low GDI genes (such as the fully-expressed) in GDI plot
Cluster 0 is uniform
0.17% of the genes is above the given GDI threshold 1.5
GDI 99% quantile is at 1.4439
cluster 0 is uniform
* checking uniformity of cluster '1' of 4 clusters
Asked to drop 0 genes and 879 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [321] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: -0.0501708984375 | max: 14.515625 | % negative: 8.5
Only analysis time 0.0357773383458455
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.00168053244592346
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.103054988384247
Total time 0.149716099103292
Checking uniformity for the cluster '1' with 321 cells
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
GDI plot
Removed 0 low GDI genes (such as the fully-expressed) in GDI plot
Cluster 1 is uniform
0.17% of the genes is above the given GDI threshold 1.5
GDI 99% quantile is at 1.4305
cluster 1 is uniform
* checking uniformity of cluster '2' of 4 clusters
Asked to drop 0 genes and 905 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [295] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: -0.054443359375 | max: 104 | % negative: 36.5
Only analysis time 0.0366660316785177
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.35504159733777
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.0996188958485921
Total time 0.147791024049123
Checking uniformity for the cluster '2' with 295 cells
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
GDI plot
Removed 0 low GDI genes (such as the fully-expressed) in GDI plot
Cluster 2 is uniform
0% of the genes is above the given GDI threshold 1.5
GDI 99% quantile is at 1.3925
cluster 2 is uniform
* checking uniformity of cluster '3' of 4 clusters
Asked to drop 0 genes and 969 cells
Cotan analysis functions started
Asked to drop 1 genes and 0 cells
Genes/cells selection done: dropped [1] genes and [0] cells
Working on [599] genes and [231] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:599] to [1:599]
Estimate dispersion: DONE
dispersion | min: -0.0662841796875 | max: 82.5 | % negative: 33.889816360601
Only analysis time 0.0375099380811056
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.318091263216472
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.104706565539042
Total time 0.155169435342153
Checking uniformity for the cluster '3' with 231 cells
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
GDI plot
Removed 0 low GDI genes (such as the fully-expressed) in GDI plot
Cluster 3 is uniform
0% of the genes is above the given GDI threshold 1.5
GDI 99% quantile is at 1.373
cluster 3 is uniform

Found 4 uniform and  0 non-uniform clusters
NO new possible uniform clusters! Unclustered cell left: 0
The final raw clusterization contains [ 4 ] different clusters: 00_0000, 00_0001, 00_0002, 00_0003
Differential Expression Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE

Differential Expression Analysis - DONE
Applied reordering to clusterization is:
1  ->  3, 2  ->  1, 3  ->  2, 4  ->  4
Cluster, UMAP and Saving the Seurat dataset
Computing nearest neighbor graph
Computing SNN
Modularity Optimizer version 1.3.0 by Ludo Waltman and Nees Jan van Eck

Number of nodes: 1200
Number of edges: 55489

Running Louvain algorithm with multilevel refinement...
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
Maximum modularity in 10 random starts: 0.6846
Number of communities: 4
Elapsed time: 0 seconds
21:42:25 UMAP embedding parameters a = 0.9922 b = 1.112
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
21:42:25 Read 1200 rows and found 25 numeric columns
21:42:25 Using Annoy for neighbor search, n_neighbors = 30
Found more than one class "dist" in cache; using the first, from namespace 'BiocGenerics'
Also defined by 'spam'
21:42:25 Building Annoy index with metric = cosine, n_trees = 50
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:25 Writing NN index file to temp file /tmp/RtmpIcook5/file278ea31c3fbd8c
21:42:25 Searching Annoy index using 1 thread, search_k = 3000
21:42:25 Annoy recall = 100%
21:42:26 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
21:42:27 Initializing from normalized Laplacian + noise (using RSpectra)
21:42:28 Commencing optimization for 500 epochs, with 43428 positive edges
Using method 'umap'
0%   10   20   30   40   50   60   70   80   90   100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
21:42:30 Optimization finished
Creating cells' uniform clustering: DONE
Differential Expression Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE

Differential Expression Analysis - DONE
Applied reordering to clusterization is:
1  ->  1, 2  ->  2, 3  ->  3, 4  ->  4
Asked to drop 0 genes and 905 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [295] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: -0.054443359375 | max: 104 | % negative: 36.5
Only analysis time 0.0347416321436564
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.35504159733777
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.107257942358653
Total time 0.153525376319885
Checking uniformity for the cluster 'Cluster_2' with 295 cells
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
GDI plot
Removed 0 low GDI genes (such as the fully-expressed) in GDI plot
Cluster Cluster_2 is uniform
0% of the genes is above the given GDI threshold 1.5
GDI 99% quantile is at 1.3925
Differential Expression Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE
* analysis of cluster: '-1' - START
* analysis of cluster: '-1' - DONE

Differential Expression Analysis - DONE
Applied reordering to clusterization is:
1  ->  2, 2  ->  1, 3  ->  4, 4  ->  3, -1  ->  -1
Applied reordering to clusterization is:
1  ->  2, 2  ->  3, 3  ->  1, 4  ->  4, -1  ->  -1
findClustersMarkers - START
Differential Expression Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE

Differential Expression Analysis - DONE
clustersDeltaExpression - START
Handling cluster '1' with mean UDE 1.20998115359079
Handling cluster '2' with mean UDE 0.538244816501366
Handling cluster '3' with mean UDE 1.43530796540674
Handling cluster '4' with mean UDE 0.632684489354434
clustersDeltaExpression - DONE
Log Fold Change Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE

Log Fold Change Analysis - DONE
findClustersMarkers - DONE
findClustersMarkers - START
Differential Expression Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE

Differential Expression Analysis - DONE
clustersDeltaExpression - START
Handling cluster '1' with mean UDE 1.20998115359079
Handling cluster '2' with mean UDE 0.538244816501366
Handling cluster '3' with mean UDE 1.43530796540674
Handling cluster '4' with mean UDE 0.632684489354434
clustersDeltaExpression - DONE
Log Fold Change Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE

Log Fold Change Analysis - DONE
findClustersMarkers - DONE
findClustersMarkers - START
Differential Expression Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE

Differential Expression Analysis - DONE
clustersDeltaExpression - START
Handling cluster '1' with mean UDE 1.20998115359079
Handling cluster '2' with mean UDE 0.538244816501366
Handling cluster '3' with mean UDE 1.43530796540674
Handling cluster '4' with mean UDE 0.632684489354434
clustersDeltaExpression - DONE
Log Fold Change Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE

Log Fold Change Analysis - DONE
findClustersMarkers - DONE
[1] "1"
Asked to drop 0 genes and 879 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [321] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: -0.0501708984375 | max: 14.515625 | % negative: 8.5
Only analysis time 0.0357889294624329
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.00168053244592346
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.108792587121328
Total time 0.155420434474945
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
[1] "3"
Asked to drop 0 genes and 847 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [353] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: -0.038818359375 | max: 11.109375 | % negative: 5
Only analysis time 0.0362653970718384
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.000521353300055463
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.109517745176951
Total time 0.156725192070007
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [10] genes and [20] cells
Initializing `COTAN` meta-data
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [1200] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: 0.2197265625 | max: 6.08056640625 | % negative: 0
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [1200] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: 0.2197265625 | max: 6.08056640625 | % negative: 0
Only analysis time 0.0449586470921834
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.121827900409698
Total time 0.179443724950155
Calculating gene co-expression space - START
Using S
Calculating S: START
Calculating S: DONE
calculating PValues: START
Get p-values on a set of genes on columns and genome wide on rows
calculating PValues: DONE
Number of selected secondary markers: 109
Calculating S: START
Calculating S: DONE
Number of columns (V set - secondary markers): 109
Number of rows (U set): 60
Calculating gene co-expression space - DONE
Establishing gene clusters - START
Calculating gene co-expression space - START
Using S
Calculating S: START
Calculating S: DONE
calculating PValues: START
Get p-values on a set of genes on columns and genome wide on rows
calculating PValues: DONE
Number of selected secondary markers: 109
Calculating S: START
Calculating S: DONE
Number of columns (V set - secondary markers): 109
Number of rows (U set): 60
Calculating gene co-expression space - DONE
Establishing gene clusters - DONE
Initializing `COTAN` meta-data
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [1200] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: 0.2197265625 | max: 6.08056640625 | % negative: 0
Cotan genes' coex estimation not requested
Total time 0.0781545201937358
Differential Expression Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE

Differential Expression Analysis - DONE
Log Fold Change Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE

Log Fold Change Analysis - DONE
clustersDeltaExpression - START
Handling cluster '1' with mean UDE 1.43530796540674
Handling cluster '2' with mean UDE 0.640931107489519
Handling cluster '3' with mean UDE 0.546546914955575
Handling cluster '4' with mean UDE 1.22034802329657
clustersDeltaExpression - DONE
In group G1 there are 3 detected over 3 genes
In group G2 there are 2 detected over 2 genes
In group G3 there are 5 detected over 5 genes
Merging cells' uniform clustering: START
Start merging nearest clusters: iteration 1
Differential Expression Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE
* analysis of cluster: '3' - START
* analysis of cluster: '3' - DONE
* analysis of cluster: '4' - START
* analysis of cluster: '4' - DONE

Differential Expression Analysis - DONE
Clusters pairs for merging:
c("1", "2") c("3", "4") c("2", "3") c("2", "4") c("1", "3") c("1", "4")
*1_2-merge
Asked to drop 0 genes and 626 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [574] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: -0.0340576171875 | max: 10.4375 | % negative: 4.33333333333333
Only analysis time 0.0441163500150045
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.000105379922351636
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.121648613611857
Total time 0.178957529862722
Checking uniformity for the cluster '1_2-merge' with 574 cells
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
GDI plot
Removed 0 low GDI genes (such as the fully-expressed) in GDI plot
Cluster 1_2-merge is uniform
0.33% of the genes is above the given GDI threshold 1.5
GDI 99% quantile is at 1.4297
Clusters 1 and 2 can be merged
*3_4-merge
Asked to drop 0 genes and 574 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [626] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: -0.03839111328125 | max: 17.40625 | % negative: 6.66666666666667
Only analysis time 0.0435771425565084
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 5.54631170271769e-05
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.122154788176219
Total time 0.177643589178721
Checking uniformity for the cluster '3_4-merge' with 626 cells
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
GDI plot
Removed 0 low GDI genes (such as the fully-expressed) in GDI plot
Cluster 3_4-merge is uniform
0.83% of the genes is above the given GDI threshold 1.5
GDI 99% quantile is at 1.4544
Clusters 3 and 4 can be merged
*Clusters 2 or 3 is now missing due to previous merges: skip.
*Clusters 2 or 4 is now missing due to previous merges: skip.
*Clusters 1 or 3 is now missing due to previous merges: skip.
*Clusters 1 or 4 is now missing due to previous merges: skip.
Executed 2 merges out of 6
Start merging nearest clusters: iteration 2
Differential Expression Analysis - START
* analysis of cluster: '1_2-merge' - START
* analysis of cluster: '1_2-merge' - DONE
* analysis of cluster: '3_4-merge' - START
* analysis of cluster: '3_4-merge' - DONE

Differential Expression Analysis - DONE
Clusters pairs for merging:
c("1_2-merge", "3_4-merge")
*1_2-merge_3_4-merge-merge
Asked to drop no genes or cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [1200] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: 0.2197265625 | max: 6.08056640625 | % negative: 0
Only analysis time 0.0528698364893595
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.132630491256714
Total time 0.198834041754405
Checking uniformity for the cluster '1_2-merge_3_4-merge-merge' with 1200 cells
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
GDI plot
Removed 0 low GDI genes (such as the fully-expressed) in GDI plot
Cluster 1_2-merge_3_4-merge-merge is not uniform
15% of the genes is above the given GDI threshold 1.5
GDI 99% quantile is at 1.6405
Merging clusters 1_2-merge and 3_4-merge results in a too high GDI
None of the 1 nearest cluster pairs could be merged
The final merged clusterization contains [2] different clusters: 1_2-merge, 3_4-merge
Differential Expression Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE

Differential Expression Analysis - DONE
Applied reordering to clusterization is:
1  ->  1, 2  ->  2
Merging cells' uniform clustering: DONE
Applied reordering to clusterization is:
1  ->  1, 2  ->  2
Log Fold Change Analysis - START
* analysis of cluster: '1' - START
* analysis of cluster: '1' - DONE
* analysis of cluster: '2' - START
* analysis of cluster: '2' - DONE

Log Fold Change Analysis - DONE
Asked to drop 0 genes and 626 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [574] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: -0.0340576171875 | max: 10.4375 | % negative: 4.33333333333333
Only analysis time 0.0411014199256897
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 0.000105379922351636
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.112258470058441
Total time 0.166242182254791
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
Asked to drop 0 genes and 574 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [626] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: -0.03839111328125 | max: 17.40625 | % negative: 6.66666666666667
Only analysis time 0.0437010725339254
Cotan genes' coex estimation started
Calculate genes' coex: START
Retrieving expected genes' contingency table
calculating NN.. done
calculating NY..YN..YY..t().. done
Calculating genes' coex normalization factor
Fraction of genes with very low expected contingency tables: 5.54631170271769e-05
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.116025332609812
Total time 0.174782303969065
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 387 ]
> 
> proc.time()
   user  system elapsed 
538.172   3.853 538.837 

Example timings

COTAN.Rcheck/COTAN-Ex.timings

nameusersystemelapsed
COTAN0.0980.0050.102
COTANObjectCreation10.411 0.24710.329
CalculatingCOEX51.642 1.47751.826
ClustersList0.0070.0000.007
GenesCoexSpace11.762 0.24011.652
HandleMetaData0.0960.0040.100
HandlingClusterizations31.835 0.35532.172
HandlingConditions0.1030.0040.108
HeatmapPlots46.523 0.95746.504
LegacyFastSymmMatrix0.0030.0000.003
LoggingFunctions0.0030.0000.003
ParametersEstimations22.978 0.18223.161
RawDataCleaning6.6410.1296.701
RawDataGetters0.0660.0040.070
UniformClusters156.579 0.764155.855
getColorsVector0.0010.0000.001