| Back to Mac ARM64 build report for BioC 3.19 |
|
This page was generated on 2024-05-07 11:32:31 -0400 (Tue, 07 May 2024).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| kjohnson3 | macOS 13.6.5 Ventura | arm64 | 4.4.0 (2024-04-24) -- "Puppy Cup" | 4461 |
| 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 449/2300 | Hostname | OS / Arch | INSTALL | BUILD | CHECK | BUILD BIN | ||||||||
| COTAN 2.4.1 (landing page) Galfrè Silvia Giulia
| kjohnson3 | macOS 13.6.5 Ventura / arm64 | OK | OK | OK | OK |  | |||||||
|
To the developers/maintainers of the COTAN package: - 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: 2.4.1 |
| 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_2.4.1.tar.gz |
| StartedAt: 2024-05-06 20:41:03 -0400 (Mon, 06 May 2024) |
| EndedAt: 2024-05-06 20:49:09 -0400 (Mon, 06 May 2024) |
| EllapsedTime: 486.3 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: COTAN.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### 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_2.4.1.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/Users/biocbuild/bbs-3.19-bioc-mac-arm64/meat/COTAN.Rcheck’
* using R version 4.4.0 (2024-04-24)
* using platform: aarch64-apple-darwin20
* R was compiled by
Apple clang version 14.0.0 (clang-1400.0.29.202)
GNU Fortran (GCC) 12.2.0
* running under: macOS Ventura 13.6.5
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘COTAN/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘COTAN’ version ‘2.4.1’
* 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 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
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’
GDIPlot: no visible binding for global variable ‘sum.raw.norm’
GDIPlot: no visible binding for global variable ‘GDI’
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’
UMAPPlot: no visible binding for global variable ‘x’
UMAPPlot: no visible binding for global variable ‘y’
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 a as.dist CellNumber cl1 cl2 Cluster clusterData clusters
Condition conditions condName expectedN expectedNN expectedNY
expectedYN expectedYY ExpGenes g2 GCS GDI group hk keys lambda means
mit.percentage n nu nuPlot objSeurat observedNN observedNY observedY
observedYN observedYY PC1 PC2 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 71.177 0.531 71.668
CalculatingCOEX 23.794 0.339 24.134
HeatmapPlots 19.916 0.606 20.534
ParametersEstimations 10.467 0.341 10.813
HandlingClusterizations 7.842 0.214 8.052
GenesCoexSpace 5.583 0.076 5.658
* 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 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-3.19-bioc-mac-arm64/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/4.4-arm64/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 4.4.0 (2024-04-24) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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 <- 1.46
+ 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, cores = 12L)
+ 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
0.078 0.020 0.094
COTAN.Rcheck/tests/spelling.Rout
R version 4.4.0 (2024-04-24) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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
0.073 0.018 0.088
COTAN.Rcheck/tests/testthat.Rout
R version 4.4.0 (2024-04-24) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-apple-darwin20
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
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: 0.25535328937316 | median 0.0807577993228135 | mean: 0.101980750205762
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 0.00111524657842743 | 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: 2.99252342145451e-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: 0.25535328937316 | median 0.0807577993228135 | mean: 0.101980750205762
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 0.00111524657842743 | 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: 2.99252342145451e-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 0.0203678011894226
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 0.0579132517178853
Total time 0.0828925530115763
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 0.0214310526847839
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 0.0586144963900248
Total time 0.0853977998097738
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 0.0522034327189128
Saving elaborated data locally at: /var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T//RtmpRxqxMl/test.cotan.RDS
Creating cells' uniform clustering: START
In iteration 1 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-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
Negative: 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
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
20:46:03 UMAP embedding parameters a = 0.9922 b = 1.112
20:46:03 Read 1200 rows and found 50 numeric columns
20:46:03 Using Annoy for neighbor search, n_neighbors = 30
20:46:03 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:46:03 Writing NN index file to temp file /var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T//RtmpRxqxMl/file13a8725ccdda8
20:46:03 Searching Annoy index using 1 thread, search_k = 3000
20:46:03 Annoy recall = 100%
20:46:03 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
20:46:04 Initializing from normalized Laplacian + noise (using RSpectra)
20:46:04 Commencing optimization for 500 epochs, with 42274 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:46:05 Optimization finished
Creating PDF UMAP in file: /var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T//RtmpRxqxMl/test/reclustering/pdf_umap_1.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 0.0174884517987569
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 0.0523793816566467
Total time 0.0753634850184123
Checking uniformity for the cluster '01_0000' 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 01_0000 is uniform
0.83% of the genes is above the given GDI threshold 1.46
GDI 99% quantile is at 1.4439
cluster 01_0000 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 0.017279048760732
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 0.0494756658871969
Total time 0.0724399964014689
Checking uniformity for the cluster '01_0001' 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 01_0001 is uniform
0.5% of the genes is above the given GDI threshold 1.46
GDI 99% quantile is at 1.4305
cluster 01_0001 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 0.0171284516652425
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 0.0508932153383891
Total time 0.07352108558019
Checking uniformity for the cluster '01_0002' 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 01_0002 is uniform
0% of the genes is above the given GDI threshold 1.46
GDI 99% quantile is at 1.3925
cluster 01_0002 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 0.0169551809628805
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 0.0499240676561991
Total time 0.072345499197642
Checking uniformity for the cluster '01_0003' 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 01_0003 is uniform
0% of the genes is above the given GDI threshold 1.46
GDI 99% quantile is at 1.373
cluster 01_0003 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: 01_0000, 01_0001, 01_0002, 01_0003
Differential Expression Analysis - START
Effective number of cores used: 1
* DEA on cluster '1'
* DEA on cluster '2'
* DEA on cluster '3'
* DEA on cluster '4'
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
20:46:33 UMAP embedding parameters a = 0.9922 b = 1.112
20:46:33 Read 1200 rows and found 25 numeric columns
20:46:33 Using Annoy for neighbor search, n_neighbors = 30
20:46:33 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:46:33 Writing NN index file to temp file /var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T//RtmpRxqxMl/file13a872feb73c8
20:46:33 Searching Annoy index using 1 thread, search_k = 3000
20:46:33 Annoy recall = 100%
20:46:34 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
20:46:34 Initializing from normalized Laplacian + noise (using RSpectra)
20:46:34 Commencing optimization for 500 epochs, with 43428 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:46:36 Optimization finished
Creating cells' uniform clustering: 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 0.0177962859471639
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 0.0492548147837321
Total time 0.0724468509356181
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 1.46
GDI 99% quantile is at 1.3925
Differential Expression Analysis - START
Effective number of cores used: 1
* DEA on cluster '-1'
* DEA on cluster '1'
* DEA on cluster '2'
* DEA on cluster '3'
* DEA on cluster '4'
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
Creating cells' uniform clustering: START
In iteration 1 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-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
Negative: 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
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
20:46:44 UMAP embedding parameters a = 0.9922 b = 1.112
20:46:44 Read 1200 rows and found 50 numeric columns
20:46:44 Using Annoy for neighbor search, n_neighbors = 30
20:46:44 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:46:44 Writing NN index file to temp file /var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T//RtmpRxqxMl/file13a87350ba4f0
20:46:44 Searching Annoy index using 1 thread, search_k = 3000
20:46:44 Annoy recall = 100%
20:46:44 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
20:46:45 Initializing from normalized Laplacian + noise (using RSpectra)
20:46:45 Commencing optimization for 500 epochs, with 42274 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:46:46 Optimization finished
Creating PDF UMAP in file: /var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T//RtmpRxqxMl/test/reclustering/pdf_umap_1.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 0.0190093318621318
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 0.0578815857569377
Total time 0.0829906344413757
Checking uniformity for the cluster '01_0001' with 600 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 01_0001 is uniform
1% of the genes is above the given GDI threshold 1.46
GDI 99% quantile is at 1.4534
cluster 01_0001 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 0.019087282816569
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 0.0541008472442627
Total time 0.0785463809967041
Checking uniformity for the cluster '01_0002' with 600 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 01_0002 is uniform
0.5% of the genes is above the given GDI threshold 1.46
GDI 99% quantile is at 1.4378
cluster 01_0002 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: 01_0001, 01_0002
Differential Expression Analysis - START
Effective number of cores used: 1
* DEA on cluster '1'
* DEA on cluster '2'
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
20:47:02 UMAP embedding parameters a = 0.9922 b = 1.112
20:47:02 Read 1200 rows and found 25 numeric columns
20:47:02 Using Annoy for neighbor search, n_neighbors = 30
20:47:02 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:47:02 Writing NN index file to temp file /var/folders/r0/l4fjk6cj5xj0j3brt4bplpl40000gt/T//RtmpRxqxMl/file13a87592cd552
20:47:02 Searching Annoy index using 1 thread, search_k = 3000
20:47:02 Annoy recall = 100%
20:47:02 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
20:47:03 Initializing from normalized Laplacian + noise (using RSpectra)
20:47:03 Commencing optimization for 500 epochs, with 43428 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
20:47:04 Optimization finished
Creating cells' uniform clustering: DONE
findClustersMarkers - START
clustersDeltaExpression - START
Handling cluster '1' with mean UDE 1.20998115359079
Handling cluster '2' with mean UDE 0.538244816501365
Handling cluster '3' with mean UDE 1.43530796540673
Handling cluster '4' with mean UDE 0.632684489354433
clustersDeltaExpression - DONE
Log Fold Change Analysis - START
* Analysis of cluster: '1'
* Analysis of cluster: '2'
* Analysis of cluster: '3'
* Analysis of cluster: '4'
Log Fold Change Analysis - DONE
findClustersMarkers - DONE
findClustersMarkers - START
clustersDeltaExpression - START
Handling cluster '1' with mean UDE 1.20998115359079
Handling cluster '2' with mean UDE 0.538244816501365
Handling cluster '3' with mean UDE 1.43530796540673
Handling cluster '4' with mean UDE 0.632684489354433
clustersDeltaExpression - DONE
Log Fold Change Analysis - START
* Analysis of cluster: '1'
* Analysis of cluster: '2'
* Analysis of cluster: '3'
* Analysis of cluster: '4'
Log Fold Change Analysis - DONE
findClustersMarkers - DONE
findClustersMarkers - START
Differential Expression Analysis - START
Effective number of cores used: 1
* DEA on cluster '1'
* DEA on cluster '2'
* DEA on cluster '3'
* DEA on cluster '4'
Differential Expression Analysis - DONE
clustersDeltaExpression - START
Handling cluster '1' with mean UDE 1.20998115359079
Handling cluster '2' with mean UDE 0.538244816501365
Handling cluster '3' with mean UDE 1.43530796540673
Handling cluster '4' with mean UDE 0.632684489354433
clustersDeltaExpression - DONE
Log Fold Change Analysis - START
* Analysis of cluster: '1'
* Analysis of cluster: '2'
* Analysis of cluster: '3'
* Analysis of cluster: '4'
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 0.0179587523142497
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 0.0520575006802877
Total time 0.0750057180722555
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 0.018374514579773
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 0.0525254011154175
Total time 0.0763394832611084
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
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: 0.259872988828242 | 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 0.079429703709466 | mean: 0.0724140148396648
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: 0.0327747321002292 | median 0.0272104747849538 | mean: 0.0248963830312038
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 0.0094232649770607 | mean: 0.00863113610779571
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: 0.00387133150664631 | 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: 0.00122501723202006 | median 0.00102126435760308 | mean: 0.000943992659051318
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: 0.000364602956581805 | 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: 0.80321315986594 | median 0.677497553540581 | 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: 0.250724014302326 | median 0.206232152124436 | mean: 0.190425623677198
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: 0.0916983669060123 | median 0.0765266929824948 | mean: 0.0697593208689684
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 0.026270214227825 | mean: 0.0240886952086962
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: 0.0113693316356223 | median 0.00939669372836249 | mean: 0.00860715734056932
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: 0.00406746973787442 | median 0.00343393462175534 | 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: 0.00143433714371533 | median 0.00116636244706747 | mean: 0.00109289166947839
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: 0.000379524846207957 | median 0.000325685250686547 | mean: 0.000295532844332236
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
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 0.0211235165596008
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 0.0602168997128805
Total time 0.0875713666280111
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
S matrix sorted
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
S matrix sorted
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 0.0325520833333333
Differential Expression Analysis - START
Effective number of cores used: 1
* DEA on cluster '1'
* DEA on cluster '2'
* DEA on cluster '3'
* DEA on cluster '4'
Differential Expression Analysis - DONE
Log Fold Change Analysis - START
* Analysis of cluster: '1'
* Analysis of cluster: '2'
* Analysis of cluster: '3'
* Analysis of cluster: '4'
Log Fold Change Analysis - DONE
clustersDeltaExpression - START
Handling cluster '1' with mean UDE 1.43530796540673
Handling cluster '2' with mean UDE 0.640931107489518
Handling cluster '3' with mean UDE 0.546546914955574
Handling cluster '4' with mean UDE 1.22034802329656
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
Effective number of cores used: 1
* DEA on cluster '1'
* DEA on cluster '2'
* DEA on cluster '3'
* DEA on cluster '4'
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 0.0169353365898132
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 0.049264665444692
Total time 0.0708874185880025
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
0.67% of the genes is above the given GDI threshold 1.46
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 0.0186374346415202
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 0.0542826970418294
Total time 0.0779442310333252
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
1% of the genes is above the given GDI threshold 1.46
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
Effective number of cores used: 1
* DEA on cluster '1_2-merge'
* DEA on cluster '3_4-merge'
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 0.0196030815442403
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 0.0597062667210897
Total time 0.0849477489789327
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
22.17% of the genes is above the given GDI threshold 1.46
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
Effective number of cores used: 1
* DEA on cluster '1'
* DEA on cluster '2'
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'
* Analysis of cluster: '2'
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 0.0182791988054911
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 0.0536358674367269
Total time 0.0776063521703084
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 0.0175907015800476
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 0.0538992166519165
Total time 0.0772157986958822
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 402 ]
>
> proc.time()
user system elapsed
250.576 1.533 252.057
COTAN.Rcheck/COTAN-Ex.timings
| name | user | system | elapsed | |
| COTAN | 0.317 | 0.002 | 0.318 | |
| COTANObjectCreation | 4.652 | 0.050 | 4.706 | |
| CalculatingCOEX | 23.794 | 0.339 | 24.134 | |
| ClustersList | 0.002 | 0.001 | 0.003 | |
| GenesCoexSpace | 5.583 | 0.076 | 5.658 | |
| HandleMetaData | 0.023 | 0.004 | 0.027 | |
| HandlingClusterizations | 7.842 | 0.214 | 8.052 | |
| HandlingConditions | 0.036 | 0.004 | 0.040 | |
| HeatmapPlots | 19.916 | 0.606 | 20.534 | |
| LegacyFastSymmMatrix | 0.001 | 0.000 | 0.001 | |
| LoggingFunctions | 0.001 | 0.000 | 0.002 | |
| ParametersEstimations | 10.467 | 0.341 | 10.813 | |
| RawDataCleaning | 2.811 | 0.078 | 2.869 | |
| RawDataGetters | 0.021 | 0.004 | 0.027 | |
| UniformClusters | 71.177 | 0.531 | 71.668 | |
| getColorsVector | 0 | 0 | 0 | |