| Back to Multiple platform build/check report for BioC 3.18: simplified long |
|
This page was generated on 2024-04-17 11:37:37 -0400 (Wed, 17 Apr 2024).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.3.3 (2024-02-29) -- "Angel Food Cake" | 4676 |
| palomino4 | Windows Server 2022 Datacenter | x64 | 4.3.3 (2024-02-29 ucrt) -- "Angel Food Cake" | 4414 |
| merida1 | macOS 12.7.1 Monterey | x86_64 | 4.3.3 (2024-02-29) -- "Angel Food Cake" | 4437 |
| Click on any hostname to see more info about the system (e.g. compilers) (*) as reported by 'uname -p', except on Windows and Mac OS X | ||||
| Package 445/2266 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| COTAN 2.2.4 (landing page) Galfrè Silvia Giulia
| nebbiolo2 | Linux (Ubuntu 22.04.3 LTS) / x86_64 | OK | OK | OK |  | ||||||||
| palomino4 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | | ||||||||
| merida1 | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | | ||||||||
| kjohnson1 | macOS 13.6.1 Ventura / arm64 | see weekly results here | ||||||||||||
|
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. |
| Package: COTAN |
| Version: 2.2.4 |
| Command: /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:COTAN.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings COTAN_2.2.4.tar.gz |
| StartedAt: 2024-04-16 01:16:41 -0400 (Tue, 16 Apr 2024) |
| EndedAt: 2024-04-16 01:43:08 -0400 (Tue, 16 Apr 2024) |
| EllapsedTime: 1587.2 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: COTAN.Rcheck |
| Warnings: 0 |
##############################################################################
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###
### Running command:
###
### /Library/Frameworks/R.framework/Resources/bin/R CMD check --install=check:COTAN.install-out.txt --library=/Library/Frameworks/R.framework/Resources/library --no-vignettes --timings COTAN_2.2.4.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/Users/biocbuild/bbs-3.18-bioc/meat/COTAN.Rcheck’
* using R version 4.3.3 (2024-02-29)
* using platform: x86_64-apple-darwin20 (64-bit)
* R was compiled by
Apple clang version 14.0.0 (clang-1400.0.29.202)
GNU Fortran (GCC) 12.2.0
* running under: macOS Monterey 12.7.1
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘COTAN/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘COTAN’ version ‘2.2.4’
* 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 R 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 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’
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’
clustersTreePlot: no visible binding for global variable ‘clusters’
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’
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’
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 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
* 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 181.256 1.405 186.339
HeatmapPlots 63.557 1.973 69.849
CalculatingCOEX 50.041 0.981 55.772
HandlingClusterizations 33.214 0.634 38.128
ParametersEstimations 29.308 0.790 31.708
GenesCoexSpace 14.383 0.216 15.674
COTANObjectCreation 12.437 0.260 14.099
RawDataCleaning 10.072 0.325 10.697
* 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 in ‘inst/doc’ ... OK
* checking running R code from vignettes ... SKIPPED
* checking re-building of vignette outputs ... SKIPPED
* checking PDF version of manual ... OK
* DONE
Status: 2 NOTEs
See
‘/Users/biocbuild/bbs-3.18-bioc/meat/COTAN.Rcheck/00check.log’
for details.
COTAN.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /Library/Frameworks/R.framework/Resources/bin/R CMD INSTALL COTAN ### ############################################################################## ############################################################################## * installing to library ‘/Library/Frameworks/R.framework/Versions/4.3-x86_64/Resources/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)
COTAN.Rcheck/tests/outputTestDatasetCreation.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
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))
+
+ 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
0.384 0.115 0.478
COTAN.Rcheck/tests/spelling.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
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
0.373 0.113 0.460
COTAN.Rcheck/tests/testthat.Rout
R version 4.3.3 (2024-02-29) -- "Angel Food Cake"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: x86_64-apple-darwin20 (64-bit)
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
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
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
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
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
S matrix sorted
Calculate GDI dataframe: 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
Calculate GDI dataframe: START
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
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 0.0635301828384399
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 0.133801118532817
Total time 0.210839017232259
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 0.0605359315872192
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 0.133087917168935
Total time 0.207516614596049
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
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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 0.170944567521413
Saving elaborated data locally at: /tmp/Rtmpjc0QNr/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-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
Negative: 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
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
01:36:20 UMAP embedding parameters a = 0.9922 b = 1.112
01:36:20 Read 1200 rows and found 50 numeric columns
01:36:20 Using Annoy for neighbor search, n_neighbors = 30
01:36:20 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:36:20 Writing NN index file to temp file /tmp/Rtmpjc0QNr/file5c41ab05cf
01:36:20 Searching Annoy index using 1 thread, search_k = 3000
01:36:20 Annoy recall = 100%
01:36:21 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
01:36:23 Initializing from normalized Laplacian + noise (using RSpectra)
01:36:23 Commencing optimization for 500 epochs, with 42270 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:36:27 Optimization finished
Creating PDF UMAP in file:/tmp/Rtmpjc0QNr/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 0.0468901515007019
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 0.130152865250905
Total time 0.191020449002584
Checking uniformity for the cluster '0' with 353 cells
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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 0.0481665333112081
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 0.129802751541138
Total time 0.192027219136556
Checking uniformity for the cluster '1' with 321 cells
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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 0.0447579860687256
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 0.127845696608226
Total time 0.185930613676707
Checking uniformity for the cluster '2' with 295 cells
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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 0.0449333985646566
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 0.128290915489197
Total time 0.187580215930939
Checking uniformity for the cluster '3' with 231 cells
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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
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
01:37:53 UMAP embedding parameters a = 0.9922 b = 1.112
01:37:53 Read 1200 rows and found 25 numeric columns
01:37:53 Using Annoy for neighbor search, n_neighbors = 30
01:37:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:37:53 Writing NN index file to temp file /tmp/Rtmpjc0QNr/file5c415346a062
01:37:53 Searching Annoy index using 1 thread, search_k = 3000
01:37:54 Annoy recall = 100%
01:37:54 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
01:37:56 Initializing from normalized Laplacian + noise (using RSpectra)
01:37:56 Commencing optimization for 500 epochs, with 43428 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:38:00 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 0.0457813660303752
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 0.127514282862345
Total time 0.186874763170878
Checking uniformity for the cluster 'Cluster_2' with 295 cells
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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 -> 3, 2 -> 4, 3 -> 1, 4 -> 2, -1 -> -1
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-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
Negative: 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
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
01:38:39 UMAP embedding parameters a = 0.9922 b = 1.112
01:38:39 Read 1200 rows and found 50 numeric columns
01:38:39 Using Annoy for neighbor search, n_neighbors = 30
01:38:39 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:38:39 Writing NN index file to temp file /tmp/Rtmpjc0QNr/file5c415e21f8b5
01:38:39 Searching Annoy index using 1 thread, search_k = 3000
01:38:39 Annoy recall = 100%
01:38:40 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
01:38:42 Initializing from normalized Laplacian + noise (using RSpectra)
01:38:42 Commencing optimization for 500 epochs, with 42270 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:38:46 Optimization finished
Creating PDF UMAP in file:/tmp/Rtmpjc0QNr/test/reclustering_0/pdf_umap.pdf
Creating Seurat object: DONE
Using passed in clusterization
* checking uniformity of cluster '1' of 2 clusters
Asked to drop 0 genes and 600 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [600] 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: -0.0386962890625 | max: 19.40625 | % negative: 6.5
Only analysis time 0.0517337163289388
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: 0.000110926234054354
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.129986385504405
Total time 0.19566658337911
Checking uniformity for the cluster '1' with 600 cells
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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
0.67% of the genes is above the given GDI threshold 1.5
GDI 99% quantile is at 1.4534
cluster 1 is uniform
* checking uniformity of cluster '2' of 2 clusters
Asked to drop 0 genes and 600 cells
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [600] 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: -0.032958984375 | max: 10.0859375 | % negative: 3.66666666666667
Only analysis time 0.0527212500572205
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: 6.10094287298946e-05
Retrieving observed genes' yes/yes contingency table
calculating YY.. done
Estimating genes' coex
Calculate genes' coex: DONE
Only genes' coex time 0.129938669999441
Total time 0.195376853148142
Checking uniformity for the cluster '2' with 600 cells
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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
0.33% of the genes is above the given GDI threshold 1.5
GDI 99% quantile is at 1.4378
cluster 2 is uniform
Found 2 uniform and 0 non-uniform clusters
NO new possible uniform clusters! Unclustered cell left: 0
The final raw clusterization contains [ 2 ] different clusters: 00_0001, 00_0002
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
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
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
01:39:31 UMAP embedding parameters a = 0.9922 b = 1.112
01:39:31 Read 1200 rows and found 25 numeric columns
01:39:31 Using Annoy for neighbor search, n_neighbors = 30
01:39:31 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:39:31 Writing NN index file to temp file /tmp/Rtmpjc0QNr/file5c4114dfcb9
01:39:31 Searching Annoy index using 1 thread, search_k = 3000
01:39:32 Annoy recall = 100%
01:39:32 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
01:39:34 Initializing from normalized Laplacian + noise (using RSpectra)
01:39:34 Commencing optimization for 500 epochs, with 43428 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
01:39:38 Optimization finished
Creating cells' uniform clustering: 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
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
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
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 0.0479015310605367
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 0.130172801017761
Total time 0.19245990117391
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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 0.0485140323638916
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 0.125167349974314
Total time 0.187372998396556
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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 0.06192973057429
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 0.137441901365916
Total time 0.213046316305796
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 0.092523733774821
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 1.43530796540674
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 0.0531931002934774
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 0.134948551654816
Total time 0.201811301708221
Checking uniformity for the cluster '1_2-merge' with 574 cells
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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 0.0525437513987223
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 0.136028599739075
Total time 0.203072301546733
Checking uniformity for the cluster '3_4-merge' with 626 cells
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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 0.0618014176686605
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 0.144202947616577
Total time 0.219700447718302
Checking uniformity for the cluster '1_2-merge_3_4-merge-merge' with 1200 cells
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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
Applied reordering to clusterization is:
1 -> 1, 2 -> 2
Merging cells' uniform clustering: DONE
Applied reordering to clusterization is:
1 -> 1, 2 -> 2
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 0.0515458305676778
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 0.134751935799917
Total time 0.200575951735179
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
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 0.052958349386851
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 0.133217716217041
Total time 0.201563266913096
Using S
Calculating S: START
Calculating S: DONE
Calculate GDI dataframe: START
S matrix sorted
Calculate GDI dataframe: DONE
[ FAIL 0 | WARN 0 | SKIP 0 | PASS 366 ]
>
> proc.time()
user system elapsed
639.604 6.923 686.400
COTAN.Rcheck/COTAN-Ex.timings
| name | user | system | elapsed | |
| COTAN | 0.302 | 0.017 | 0.343 | |
| COTANObjectCreation | 12.437 | 0.260 | 14.099 | |
| CalculatingCOEX | 50.041 | 0.981 | 55.772 | |
| ClustersList | 0.013 | 0.004 | 0.018 | |
| GenesCoexSpace | 14.383 | 0.216 | 15.674 | |
| HandleMetaData | 0.136 | 0.015 | 0.158 | |
| HandlingClusterizations | 33.214 | 0.634 | 38.128 | |
| HandlingConditions | 0.194 | 0.009 | 0.219 | |
| HeatmapPlots | 63.557 | 1.973 | 69.849 | |
| LegacyFastSymmMatrix | 0.003 | 0.002 | 0.005 | |
| LoggingFunctions | 0.006 | 0.003 | 0.009 | |
| ParametersEstimations | 29.308 | 0.790 | 31.708 | |
| RawDataCleaning | 10.072 | 0.325 | 10.697 | |
| RawDataGetters | 0.134 | 0.010 | 0.143 | |
| UniformClusters | 181.256 | 1.405 | 186.339 | |
| getColorsVector | 0.002 | 0.001 | 0.003 | |