Galfrè Silvia Giulia
| Snapshot Date: 2024-04-28 14:00:16 -0400 (Sun, 28 Apr 2024) |
| git_url: https://git.bioconductor.org/packages/COTAN |
| git_branch: devel |
| git_last_commit: e8671db |
| git_last_commit_date: 2024-04-15 03:16:54 -0400 (Mon, 15 Apr 2024) |
| Back to Multiple platform build/check report for BioC 3.19: simplified long |
|
This page was generated on 2024-04-29 11:40:41 -0400 (Mon, 29 Apr 2024).
| Hostname | OS | Arch (*) | R version | Installed pkgs |
|---|---|---|---|---|
| nebbiolo1 | Linux (Ubuntu 22.04.3 LTS) | x86_64 | 4.4.0 beta (2024-04-15 r86425) -- "Puppy Cup" | 4752 |
| palomino3 | Windows Server 2022 Datacenter | x64 | 4.4.0 beta (2024-04-15 r86425 ucrt) -- "Puppy Cup" | 4486 |
| lconway | macOS 12.7.1 Monterey | x86_64 | 4.4.0 beta (2024-04-14 r86421) -- "Puppy Cup" | 4518 |
| kunpeng2 | Linux (openEuler 22.03 LTS-SP1) | aarch64 | 4.4.0 beta (2024-04-15 r86425) -- "Puppy Cup" | 4475 |
| 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 2.3.6 (landing page) Galfrè Silvia Giulia
| nebbiolo1 | Linux (Ubuntu 22.04.3 LTS) / x86_64 | OK | OK | OK | | ||||||||
| palomino3 | Windows Server 2022 Datacenter / x64 | OK | OK | OK | OK | | ||||||||
| lconway | macOS 12.7.1 Monterey / x86_64 | OK | OK | OK | OK | | ||||||||
| kunpeng2 | Linux (openEuler 22.03 LTS-SP1) / aarch64 | OK | OK | OK | ||||||||||
| kjohnson3 | macOS 13.6.5 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. - See Martin Grigorov's blog post for how to debug Linux ARM64 related issues on a x86_64 host. |
| Package: COTAN |
| Version: 2.3.6 |
| Command: /home/biocbuild/R/R-beta-2024-04-15_r86425/bin/R CMD check --install=check:COTAN.install-out.txt --library=/home/biocbuild/R/R-beta-2024-04-15_r86425/site-library --no-vignettes --timings COTAN_2.3.6.tar.gz |
| StartedAt: 2024-04-29 04:58:55 -0000 (Mon, 29 Apr 2024) |
| EndedAt: 2024-04-29 05:24:14 -0000 (Mon, 29 Apr 2024) |
| EllapsedTime: 1518.6 seconds |
| RetCode: 0 |
| Status: OK |
| CheckDir: COTAN.Rcheck |
| Warnings: 0 |
##############################################################################
##############################################################################
###
### Running command:
###
### /home/biocbuild/R/R-beta-2024-04-15_r86425/bin/R CMD check --install=check:COTAN.install-out.txt --library=/home/biocbuild/R/R-beta-2024-04-15_r86425/site-library --no-vignettes --timings COTAN_2.3.6.tar.gz
###
##############################################################################
##############################################################################
* using log directory ‘/home/biocbuild/bbs-3.19-bioc/meat/COTAN.Rcheck’
* using R version 4.4.0 beta (2024-04-15 r86425)
* using platform: aarch64-unknown-linux-gnu
* R was compiled by
gcc (GCC) 10.3.1
GNU Fortran (GCC) 10.3.1
* running under: openEuler 22.03 (LTS-SP1)
* using session charset: UTF-8
* using option ‘--no-vignettes’
* checking for file ‘COTAN/DESCRIPTION’ ... OK
* checking extension type ... Package
* this is package ‘COTAN’ version ‘2.3.6’
* package encoding: UTF-8
* checking package namespace information ... OK
* checking package dependencies ... OK
* checking if this is a source package ... OK
* checking if there is a namespace ... OK
* checking for hidden files and directories ... OK
* checking for portable file names ... OK
* checking for sufficient/correct file permissions ... OK
* checking whether package ‘COTAN’ can be installed ... OK
* checking installed package size ... OK
* checking package directory ... OK
* checking ‘build’ directory ... OK
* checking DESCRIPTION meta-information ... OK
* checking top-level files ... OK
* checking for left-over files ... OK
* checking index information ... OK
* checking package subdirectories ... OK
* checking code files for non-ASCII characters ... OK
* checking R files for syntax errors ... OK
* checking whether the package can be loaded ... OK
* checking whether the package can be loaded with stated dependencies ... OK
* checking whether the package can be unloaded cleanly ... OK
* checking whether the namespace can be loaded with stated dependencies ... OK
* checking whether the namespace can be unloaded cleanly ... OK
* checking loading without being on the library search path ... OK
* checking dependencies in R code ... NOTE
Unexported object imported by a ':::' call: ‘ggplot2:::ggname’
See the note in ?`:::` about the use of this operator.
* checking S3 generic/method consistency ... OK
* checking replacement functions ... OK
* checking foreign function calls ... OK
* checking R code for possible problems ... NOTE
GDIPlot: no visible binding for global variable ‘sum.raw.norm’
GDIPlot: no visible binding for global variable ‘GDI’
UMAPPlot: no visible binding for global variable ‘x’
UMAPPlot: no visible binding for global variable ‘y’
calculateG: no visible binding for global variable ‘observedNN’
calculateG: no visible binding for global variable ‘observedNY’
calculateG: no visible binding for global variable ‘observedYN’
calculateG: no visible binding for global variable ‘observedYY’
calculateG: no visible binding for global variable ‘expectedNN’
calculateG: no visible binding for global variable ‘expectedNY’
calculateG: no visible binding for global variable ‘expectedYN’
calculateG: no visible binding for global variable ‘expectedYY’
calculatePartialCoex: no visible binding for global variable
‘expectedNN’
calculatePartialCoex: no visible binding for global variable
‘expectedNY’
calculatePartialCoex: no visible binding for global variable
‘expectedYN’
calculatePartialCoex: no visible binding for global variable
‘expectedYY’
calculatePartialCoex: no visible binding for global variable
‘observedYY’
calculatePartialCoex: no visible binding for global variable ‘.’
cellsUniformClustering: no visible binding for global variable
‘objSeurat’
cellsUniformClustering: no visible binding for global variable
‘usedMaxResolution’
checkClusterUniformity: ... may be used in an incorrect context:
‘c(..., nuPlot, zoomedNuPlot)’
checkClusterUniformity: no visible binding for global variable ‘nuPlot’
checkClusterUniformity: no visible binding for global variable
‘zoomedNuPlot’
cleanPlots: no visible binding for global variable ‘PC1’
cleanPlots: no visible binding for global variable ‘PC2’
cleanPlots: no visible binding for global variable ‘n’
cleanPlots: no visible binding for global variable ‘means’
cleanPlots: no visible binding for global variable ‘nu’
clustersMarkersHeatmapPlot: no visible binding for global variable
‘condName’
clustersMarkersHeatmapPlot: no visible binding for global variable
‘conditions’
clustersSummaryPlot: no visible binding for global variable ‘keys’
clustersSummaryPlot: no visible binding for global variable ‘values’
clustersSummaryPlot: no visible binding for global variable
‘CellNumber’
clustersSummaryPlot: no visible binding for global variable ‘ExpGenes’
clustersSummaryPlot: no visible binding for global variable ‘Cluster’
clustersSummaryPlot: no visible binding for global variable ‘Condition’
establishGenesClusters: no visible binding for global variable
‘secondaryMarkers’
establishGenesClusters: no visible binding for global variable ‘GCS’
establishGenesClusters: no visible binding for global variable
‘rankGenes’
expectedContingencyTables: no visible binding for global variable
‘expectedN’
expectedPartialContingencyTables: no visible binding for global
variable ‘expectedNN’
expectedPartialContingencyTables: no visible binding for global
variable ‘expectedN’
geom_flat_violin : <anonymous>: no visible binding for global variable
‘group’
geom_flat_violin : <anonymous>: no visible binding for global variable
‘y’
geom_flat_violin : <anonymous>: no visible binding for global variable
‘x’
geom_flat_violin : <anonymous>: no visible binding for global variable
‘width’
geom_flat_violin : <anonymous>: no visible binding for global variable
‘violinwidth’
geom_flat_violin : <anonymous>: no visible binding for global variable
‘xmax’
geom_flat_violin : <anonymous>: no visible binding for global variable
‘xminv’
geom_flat_violin : <anonymous>: no visible binding for global variable
‘xmaxv’
heatmapPlot: no visible binding for global variable ‘g2’
mergeUniformCellsClusters : testPairListMerge: no visible binding for
global variable ‘cl1’
mergeUniformCellsClusters : testPairListMerge: no visible binding for
global variable ‘cl2’
mitochondrialPercentagePlot: no visible binding for global variable
‘mit.percentage’
observedContingencyTables: no visible binding for global variable
‘observedY’
observedPartialContingencyTables: no visible binding for global
variable ‘observedYY’
observedPartialContingencyTables: no visible binding for global
variable ‘observedY’
reorderClusterization: no visible global function definition for
‘as.dist’
scatterPlot: no visible binding for global variable ‘.x’
calculateCoex,COTAN: no visible binding for global variable
‘expectedNN’
calculateCoex,COTAN: no visible binding for global variable
‘expectedNY’
calculateCoex,COTAN: no visible binding for global variable
‘expectedYN’
calculateCoex,COTAN: no visible binding for global variable
‘expectedYY’
calculateCoex,COTAN: no visible binding for global variable
‘observedYY’
calculateCoex,COTAN: no visible binding for global variable ‘.’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘rawNorm’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘nu’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘lambda’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘a’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘hk’
coerce,COTAN-scCOTAN: no visible binding for global variable ‘clusters’
coerce,COTAN-scCOTAN: no visible binding for global variable
‘clusterData’
Undefined global functions or variables:
. .x CellNumber Cluster Condition ExpGenes GCS GDI PC1 PC2 a as.dist
cl1 cl2 clusterData clusters condName conditions expectedN expectedNN
expectedNY expectedYN expectedYY g2 group hk keys lambda means
mit.percentage n nu nuPlot objSeurat observedNN observedNY observedY
observedYN observedYY rankGenes rawNorm secondaryMarkers sum.raw.norm
usedMaxResolution values violinwidth width x xmax xmaxv xminv y
zoomedNuPlot
Consider adding
importFrom("stats", "as.dist")
to your NAMESPACE file.
* checking Rd files ... OK
* checking Rd metadata ... OK
* checking Rd cross-references ... OK
* checking for missing documentation entries ... OK
* checking for code/documentation mismatches ... OK
* checking Rd \usage sections ... OK
* checking Rd contents ... OK
* checking for unstated dependencies in examples ... OK
* checking contents of ‘data’ directory ... OK
* checking data for non-ASCII characters ... OK
* checking data for ASCII and uncompressed saves ... OK
* checking files in ‘vignettes’ ... OK
* checking examples ... OK
Examples with CPU (user + system) or elapsed time > 5s
user system elapsed
UniformClusters 209.905 5.709 215.099
CalculatingCOEX 74.770 3.203 77.325
HeatmapPlots 64.169 2.340 66.068
ParametersEstimations 35.712 1.122 36.911
HandlingClusterizations 26.971 1.148 28.173
GenesCoexSpace 15.679 0.374 15.810
COTANObjectCreation 14.544 0.120 14.500
RawDataCleaning 7.893 0.358 8.194
* 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
‘/home/biocbuild/bbs-3.19-bioc/meat/COTAN.Rcheck/00check.log’
for details.
COTAN.Rcheck/00install.out
############################################################################## ############################################################################## ### ### Running command: ### ### /home/biocbuild/R/R-beta-2024-04-15_r86425/bin/R CMD INSTALL COTAN ### ############################################################################## ############################################################################## * installing to library ‘/home/biocbuild/R/R-beta-2024-04-15_r86425/site-library’ * installing *source* package ‘COTAN’ ... ** using staged installation ** R ** data ** inst ** byte-compile and prepare package for lazy loading Note: ... may be used in an incorrect context ** help *** installing help indices ** building package indices ** installing vignettes ** testing if installed package can be loaded from temporary location ** testing if installed package can be loaded from final location ** testing if installed package keeps a record of temporary installation path * DONE (COTAN)
COTAN.Rcheck/tests/outputTestDatasetCreation.Rout
R version 4.4.0 beta (2024-04-15 r86425) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
>
> # 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
0.189 0.050 0.222
COTAN.Rcheck/tests/spelling.Rout
R version 4.4.0 beta (2024-04-15 r86425) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> if (requireNamespace('spelling', quietly = TRUE))
+ spelling::spell_check_test(vignettes = TRUE, error = FALSE,
+ skip_on_cran = TRUE)
NULL
>
> proc.time()
user system elapsed
0.223 0.028 0.237
COTAN.Rcheck/tests/testthat.Rout
R version 4.4.0 beta (2024-04-15 r86425) -- "Puppy Cup"
Copyright (C) 2024 The R Foundation for Statistical Computing
Platform: aarch64-unknown-linux-gnu
R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.
R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.
Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.
> 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: 0.259872988828242 | 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: 0.619375430892821
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 0.0262702142278251 | 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
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 0.074643615881602
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 0.148180441061656
Total time 0.238194298744202
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 0.071340004603068
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 0.142471138636271
Total time 0.229112573464712
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 0.171182147661845
Saving elaborated data locally at: /home/biocbuild/tmp/Rtmp1EPgyy/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-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
Negative: 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
PC_ 2
Positive: g-000039, g-000050, g-000175, g-000078, g-000116, g-000189, g-000135, g-000047, g-000072, g-000087
g-000063, g-000235, g-000066, g-000109, g-000018, g-000074, g-000231, g-000136, g-000034, g-000207
g-000128, g-000167, g-000171, g-000049, g-000182, g-000013, g-000054, g-000062, g-000240, g-000158
Negative: g-000584, g-000583, g-000544, g-000519, g-000575, g-000516, g-000585, g-000486, g-000489, g-000539
g-000484, g-000502, g-000523, g-000595, g-000305, g-000574, g-000599, g-000589, g-000509, g-000538
g-000526, g-000551, g-000579, g-000590, g-000445, g-000556, g-000543, g-000501, g-000504, g-000570
PC_ 3
Positive: g-000015, g-000575, g-000483, g-000316, g-000025, g-000364, g-000050, g-000278, g-000443, g-000360
g-000332, g-000124, g-000212, g-000387, g-000536, g-000252, g-000251, g-000321, g-000501, g-000470
g-000582, g-000106, g-000455, g-000368, g-000081, g-000104, g-000437, g-000288, g-000386, g-000317
Negative: g-000211, g-000337, g-000129, g-000185, g-000397, g-000403, g-000253, g-000098, g-000390, g-000303
g-000052, g-000088, g-000463, g-000468, g-000236, g-000209, g-000005, g-000375, g-000342, g-000262
g-000388, g-000091, g-000413, g-000285, g-000003, g-000095, g-000142, g-000205, g-000432, g-000241
PC_ 4
Positive: g-000334, g-000398, g-000292, g-000095, g-000097, g-000202, g-000382, g-000195, g-000007, g-000079
g-000086, g-000240, g-000263, g-000317, g-000576, g-000557, g-000160, g-000154, g-000214, g-000228
g-000313, g-000053, g-000524, g-000374, g-000568, g-000188, g-000358, g-000528, g-000362, g-000150
Negative: g-000379, g-000193, g-000212, g-000434, g-000593, g-000513, g-000177, g-000223, g-000069, g-000131
g-000162, g-000345, g-000462, g-000484, g-000448, g-000229, g-000365, g-000302, g-000010, g-000366
g-000051, g-000535, g-000269, g-000270, g-000155, g-000529, g-000373, g-000008, g-000393, g-000306
PC_ 5
Positive: g-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
05:17:53 UMAP embedding parameters a = 0.9922 b = 1.112
05:17:53 Read 1200 rows and found 50 numeric columns
05:17:53 Using Annoy for neighbor search, n_neighbors = 30
05:17:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:17:54 Writing NN index file to temp file /home/biocbuild/tmp/Rtmp1EPgyy/file22eb523da215ed
05:17:54 Searching Annoy index using 1 thread, search_k = 3000
05:17:54 Annoy recall = 100%
05:17:55 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
05:17:57 Initializing from normalized Laplacian + noise (using RSpectra)
05:17:57 Commencing optimization for 500 epochs, with 42268 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:18:00 Optimization finished
Creating PDF UMAP in file: /home/biocbuild/tmp/Rtmp1EPgyy/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 0.0513760884602865
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 0.146148065725962
Total time 0.212666058540344
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 0.0502851525942485
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 0.142979069550832
Total time 0.20873228708903
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 0.0546048243840535
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 0.143506379922231
Total time 0.21340583562851
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 0.0538979411125183
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 0.147570848464966
Total time 0.217173024018606
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
05:19:21 UMAP embedding parameters a = 0.9922 b = 1.112
05:19:21 Read 1200 rows and found 25 numeric columns
05:19:21 Using Annoy for neighbor search, n_neighbors = 30
05:19:21 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:19:21 Writing NN index file to temp file /home/biocbuild/tmp/Rtmp1EPgyy/file22eb52624153aa
05:19:21 Searching Annoy index using 1 thread, search_k = 3000
05:19:22 Annoy recall = 100%
05:19:23 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
05:19:25 Initializing from normalized Laplacian + noise (using RSpectra)
05:19:25 Commencing optimization for 500 epochs, with 43428 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:19:28 Optimization finished
Creating cells' uniform clustering: DONE
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 -> 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 0.0531038761138916
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 0.158035095532735
Total time 0.226781129837036
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 '2'
* DEA on cluster '3'
* DEA on cluster '4'
* DEA on cluster '-1'
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-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
Negative: 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
PC_ 2
Positive: g-000039, g-000050, g-000175, g-000078, g-000116, g-000189, g-000135, g-000047, g-000072, g-000087
g-000063, g-000235, g-000066, g-000109, g-000018, g-000074, g-000231, g-000136, g-000034, g-000207
g-000128, g-000167, g-000171, g-000049, g-000182, g-000013, g-000054, g-000062, g-000240, g-000158
Negative: g-000584, g-000583, g-000544, g-000519, g-000575, g-000516, g-000585, g-000486, g-000489, g-000539
g-000484, g-000502, g-000523, g-000595, g-000305, g-000574, g-000599, g-000589, g-000509, g-000538
g-000526, g-000551, g-000579, g-000590, g-000445, g-000556, g-000543, g-000501, g-000504, g-000570
PC_ 3
Positive: g-000015, g-000575, g-000483, g-000316, g-000025, g-000364, g-000050, g-000278, g-000443, g-000360
g-000332, g-000124, g-000212, g-000387, g-000536, g-000252, g-000251, g-000321, g-000501, g-000470
g-000582, g-000106, g-000455, g-000368, g-000081, g-000104, g-000437, g-000288, g-000386, g-000317
Negative: g-000211, g-000337, g-000129, g-000185, g-000397, g-000403, g-000253, g-000098, g-000390, g-000303
g-000052, g-000088, g-000463, g-000468, g-000236, g-000209, g-000005, g-000375, g-000342, g-000262
g-000388, g-000091, g-000413, g-000285, g-000003, g-000095, g-000142, g-000205, g-000432, g-000241
PC_ 4
Positive: g-000334, g-000398, g-000292, g-000095, g-000097, g-000202, g-000382, g-000195, g-000007, g-000079
g-000086, g-000240, g-000263, g-000317, g-000576, g-000557, g-000160, g-000154, g-000214, g-000228
g-000313, g-000053, g-000524, g-000374, g-000568, g-000188, g-000358, g-000528, g-000362, g-000150
Negative: g-000379, g-000193, g-000212, g-000434, g-000593, g-000513, g-000177, g-000223, g-000069, g-000131
g-000162, g-000345, g-000462, g-000484, g-000448, g-000229, g-000365, g-000302, g-000010, g-000366
g-000051, g-000535, g-000269, g-000270, g-000155, g-000529, g-000373, g-000008, g-000393, g-000306
PC_ 5
Positive: g-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
05:19:53 UMAP embedding parameters a = 0.9922 b = 1.112
05:19:53 Read 1200 rows and found 50 numeric columns
05:19:53 Using Annoy for neighbor search, n_neighbors = 30
05:19:53 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:19:54 Writing NN index file to temp file /home/biocbuild/tmp/Rtmp1EPgyy/file22eb5248bea4b4
05:19:54 Searching Annoy index using 1 thread, search_k = 3000
05:19:54 Annoy recall = 100%
05:19:55 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
05:19:57 Initializing from normalized Laplacian + noise (using RSpectra)
05:19:57 Commencing optimization for 500 epochs, with 42268 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:20:00 Optimization finished
Creating PDF UMAP in file: /home/biocbuild/tmp/Rtmp1EPgyy/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 0.0628596901893616
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 0.155892280737559
Total time 0.235837503274282
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 0.062599774201711
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 0.152743883927663
Total time 0.233019642035166
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
05:20:46 UMAP embedding parameters a = 0.9922 b = 1.112
05:20:46 Read 1200 rows and found 25 numeric columns
05:20:46 Using Annoy for neighbor search, n_neighbors = 30
05:20:46 Building Annoy index with metric = cosine, n_trees = 50
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:20:46 Writing NN index file to temp file /home/biocbuild/tmp/Rtmp1EPgyy/file22eb525cca9cf
05:20:46 Searching Annoy index using 1 thread, search_k = 3000
05:20:46 Annoy recall = 100%
05:20:48 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
05:20:50 Initializing from normalized Laplacian + noise (using RSpectra)
05:20:50 Commencing optimization for 500 epochs, with 43428 positive edges
Using method 'umap'
0% 10 20 30 40 50 60 70 80 90 100%
[----|----|----|----|----|----|----|----|----|----|
**************************************************|
05:20:53 Optimization finished
Creating cells' uniform clustering: 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 0.538244816501366
Handling cluster '3' with mean UDE 1.43530796540674
Handling cluster '4' with mean UDE 0.632684489354434
clustersDeltaExpression - DONE
Log Fold Change Analysis - START
* Analysis of cluster: '1'
* 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 0.538244816501366
Handling cluster '3' with mean UDE 1.43530796540674
Handling cluster '4' with mean UDE 0.632684489354434
clustersDeltaExpression - DONE
Log Fold Change Analysis - START
* Analysis of cluster: '1'
* 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 0.538244816501366
Handling cluster '3' with mean UDE 1.43530796540674
Handling cluster '4' with mean UDE 0.632684489354434
clustersDeltaExpression - DONE
Log Fold Change Analysis - START
* Analysis of cluster: '1'
* 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 0.0573517719904582
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 0.155353812376658
Total time 0.22971225976944
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 0.059291406472524
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 0.157329718271891
Total time 0.234124799569448
Calculate GDI dataframe: START
Using S
Calculating S: START
Calculating S: DONE
S matrix sorted
Calculate GDI dataframe: DONE
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [10] genes and [20] cells
Initializing `COTAN` meta-data
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [1200] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: 0.2197265625 | max: 6.08056640625 | % negative: 0
Cotan analysis functions started
Genes/cells selection done: dropped [0] genes and [0] cells
Working on [600] genes and [1200] cells
Estimate dispersion: START
Effective number of cores used: 1
Executing 1 genes batches from [1:600] to [1:600]
Estimate dispersion: DONE
dispersion | min: 0.2197265625 | max: 6.08056640625 | % negative: 0
Only analysis time 0.0788511673609416
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 0.161387940247854
Total time 0.257302530606588
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 0.117625761032104
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 1.43530796540674
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
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 0.0645900805791219
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 0.159070201714834
Total time 0.241722814242045
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 0.0660975615183512
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 0.159176166852315
Total time 0.24235452413559
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 0.0818788607915242
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 0.164143033822378
Total time 0.263254217306773
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 0.0671446720759074
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 0.157723208268483
Total time 0.242993978659312
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 0.0684991558392843
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 0.163581375281016
Total time 0.24922780195872
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 401 ]
>
> proc.time()
user system elapsed
734.017 23.458 757.058
COTAN.Rcheck/COTAN-Ex.timings
| name | user | system | elapsed | |
| COTAN | 0.254 | 0.008 | 0.262 | |
| COTANObjectCreation | 14.544 | 0.120 | 14.500 | |
| CalculatingCOEX | 74.770 | 3.203 | 77.325 | |
| ClustersList | 0.009 | 0.001 | 0.009 | |
| GenesCoexSpace | 15.679 | 0.374 | 15.810 | |
| HandleMetaData | 0.108 | 0.008 | 0.116 | |
| HandlingClusterizations | 26.971 | 1.148 | 28.173 | |
| HandlingConditions | 0.154 | 0.008 | 0.162 | |
| HeatmapPlots | 64.169 | 2.340 | 66.068 | |
| LegacyFastSymmMatrix | 0.002 | 0.000 | 0.002 | |
| LoggingFunctions | 0.003 | 0.000 | 0.003 | |
| ParametersEstimations | 35.712 | 1.122 | 36.911 | |
| RawDataCleaning | 7.893 | 0.358 | 8.194 | |
| RawDataGetters | 0.117 | 0.004 | 0.121 | |
| UniformClusters | 209.905 | 5.709 | 215.099 | |
| getColorsVector | 0.001 | 0.000 | 0.001 | |