Guided tutorial to Uniform Transcript cells’ clustering using COTAN
Preamble
Introduction
This tutorial shows the available tools in COTAN to help with genes’ clustering
Setup
Define a directory where the data and the output will be stored
dataDir <- file.path(tempdir(), "COTAN_vignette_data")
dir.create(dataDir, recursive = TRUE, showWarnings = FALSE)
print(dataDir)
#> [1] "/tmp/RtmpdrRmYh/COTAN_vignette_data"
outDir <- dataDir
# Log-level 2 was chosen to showcase better how the package works
# In normal usage a level of 0 or 1 is more appropriate
setLoggingLevel(2L)
#> Setting new log level to 2
# This file will contain all the logs produced by the package
# as if at the highest logging level
setLoggingFile(file.path(outDir, "vignette_uniform_clustering.log"))
#> Setting log file to be: /tmp/RtmpdrRmYh/COTAN_vignette_data/vignette_uniform_clustering.logRetrieving the data-set
Download the data-set for mouse cortex E17.5 from Yuzwa et al. (2017)
GEO <- "GSM2861514"
dir.create(file.path(dataDir, GEO), showWarnings = FALSE)
datasetFileName <-
file.path(dataDir, GEO, "GSM2861514_E175_All_Cells_DGE.txt.gz")
# retries up to 5 times to get the dataset
attempts <- 0L
maxAttempts <- 5L
ok <- FALSE
while (attempts < maxAttempts && !ok) {
attempts <- attempts + 1L
if (!file.exists(datasetFileName)) {
res <- try(
GEOquery::getGEOSuppFiles(
GEO = GEO,
makeDirectory = TRUE,
baseDir = dataDir,
filter_regex = base::basename(datasetFileName),
fetch_files = TRUE
),
silent = TRUE
)
}
ok <- file.exists(datasetFileName)
if (!ok && attempts < maxAttempts) {
Sys.sleep(1)
}
}
assertthat::assert_that(
ok,
msg = paste0(
"Failed to retrieve file '", datasetFileName,
"' after ", maxAttempts, " attempts."
)
)
#> [1] TRUE
rawDataset <- read.csv(datasetFileName, sep = "\t", row.names = 1L)
print(dim(rawDataset))
#> [1] 17085 2000Create the COTAN object
Initialize the COTAN object with the row count table and
the metadata from the experiment.
cond <- "mouse_cortex_E17.5"
obj <- COTAN(raw = rawDataset)
obj <-
initializeMetaDataset(
obj,
GEO = GEO,
sequencingMethod = "Drop_seq",
sampleCondition = cond
)
#> Initializing `COTAN` meta-data
logThis(paste0("Condition ", getMetadataElement(obj, datasetTags()[["cond"]])),
logLevel = 1L)
#> Condition mouse_cortex_E17.5Assign to each cell its origin
# mark cells origin
data(vignette.cells.origin)
head(vignette.cells.origin, 18)
#> GCGAATTGTGAA ACCGATAACTGA CCCCGGGTGCGA CTGTAGATGTTA TCATCGAAGCGC TTCTACCGAGTC
#> Other Other Other Other Other Other
#> CTGTTCCCGGCG GCGTGTTAGTTC CTCGCGCGTTTA GATGTATAACTT GCGCTATGATTT CGTTTAGTTTAC
#> Other Other Other Other Other Cortical
#> GTGGAGGCCCAT TGTCACTACATC TCTAGAACAACG ACCTTTGTTCGT TTGTCTTCTTCG TAAAATATCGCC
#> Other Other Cortical Cortical Cortical Cortical
#> Levels: Cortical Other
obj <-
addCondition(obj, condName = "origin", conditions = vignette.cells.origin)
rm(vignette.cells.origin)Drop low quality cells
# use previously established results to determine
# which cells were dropped in the cleaning stage
data(vignette.split.clusters)
cellsToDrop <-
getCells(obj)[!(getCells(obj) %in% names(vignette.split.clusters))]
obj <- dropGenesCells(obj, cells = cellsToDrop)
# Log the remaining number of cells
logThis(paste("n cells", getNumCells(obj)), logLevel = 1L)
#> n cells 1783
table(getCondition(obj, condName = "origin"))
#>
#> Cortical Other
#> 844 939
rm(vignette.split.clusters)Establish Uniform Transcript cells’ clusters
COTAN analysis
In this part, the COTAN model is calibrated: all the
contingency tables are computed and used to get the statistics necessary
to COEX evaluation and storing
The COEX coefficient between the two genes, measures the
deviation between the 2×2 contingency table of the joint occurrence of
the genes in the cells, and their expected co-expression assuming
independence.
obj <-
proceedToCoex(
obj,
calcCoex = FALSE,
optimizeForSpeed = TRUE,
cores = 3L,
deviceStr = "cuda"
)
#> COTAN dataset analysis: START
#> Genes/cells selection done: dropped [4330] genes and [0] cells
#> Working on [12755] genes and [1783] cells
#> Estimate `dispersion`: START
#> Total calculations elapsed time: 9.63260936737061
#> Estimate `dispersion`: DONE
#> Estimate `dispersion`: DONE
#> `dispersion` | min: -0.0349260648055935 | max: 760.574661197634 | % negative: 12.5362602900823
#> COTAN genes' COEX estimation not requested
#> COTAN dataset analysis: DONEUniform Clustering
It is possible to obtain a cells’ clusterization based on
the concept of uniformity of expression of the genes across the
cells in each cluster. That is the clusters satisfy the null hypothesis
of the COTAN model: the genes expression is not dependent
on the cell in consideration.
First UT clusterization
There are two functions involved into obtaining a proper
clusterization: the first is
cellsUniformClustering. This function uses standard tools
clusterization methods via a COTAN specific data
input and reduction. The function then discards and re-clusters any
non-uniform cluster.
Please note that the most important parameters for the users are the
GDIThresholds inside the Uniform
Transcript checkers: they determine how strict is the check.
Default constructed advance checks give a pretty strong guarantee of
uniformity for the cluster.
# This code is a too computationally heavy to be used in a vignette [~20 min.]
# So the result was stored and will be re-loaded in the next chunk
initialClusters <- NULL
if (TRUE) {
# read from the last file among those named all_check_results_XX.csv
mergeDir <- file.path(outDir, cond, "reclustering")
resFiles <-
list.files(
path = mergeDir,
pattern = "partial_clusterization_.*csv",
full.names = TRUE
)
if (length(resFiles) != 0L) {
prevClustRes <-
read.csv(resFiles[length(resFiles)], header = TRUE, row.names = 1L)
initialClusters <- getColumnFromDF(prevClustRes, 1L)
initialClusters[is.na(initialClusters)] <- "-1"
initialClusters <- niceFactorLevels(initialClusters)
}
}
# default constructed checker is OK
advChecker <- new("AdvancedGDIUniformityCheck")
c(splitClusters, splitCoexDF) %<-%
cellsUniformClustering(
obj,
initialResolution = 0.8,
checker = advChecker,
dataMethod = "LogLikelihood", # of observed against expected data
useCoexEigen = TRUE, # project on `COEX` eigenvectors instead doing a `PCA`
numReducedComp = 50L,
minimumUTClusterSize = 50L, # too large values imply many un-clustered cells
initialClusters = initialClusters,
initialIteration = 1L,
optimizeForSpeed = TRUE,
deviceStr = "cuda",
cores = 3L,
saveObj = TRUE, # save intermediate data for log and restart
outDir = outDir
)
# add the newly found clusterization to the `COTAN` object
obj <-
addClusterization(
obj,
clName = "split",
clusters = splitClusters,
coexDF = splitCoexDF
)
table(splitClusters)Loading pre-calculated clusterization from data
data("vignette.split.clusters", package = "COTAN")
splitClusters <- vignette.split.clusters
# explicitly calculate the DEA of the clusterization to store it
splitCoexDF <- DEAOnClusters(obj, clusters = splitClusters)
#> Differential Expression Analysis - START
#> ****************
#> Total calculations elapsed time: 0.819079637527466
#> Differential Expression Analysis - DONE
obj <-
addClusterization(
obj,
clName = "split",
clusters = splitClusters,
coexDF = splitCoexDF,
override = FALSE
)Extract split clusters’ statistics
It is possible to extract some statistics about a stored clusterization:
c(summaryData, summaryPlot) %<-%
clustersSummaryPlot(
obj,
clName = "split",
plotTitle = "split summary",
condName = "origin"
)
head(summaryData, n = 10L)
#> split origin CellNumber CellPercentage MeanUDE MedianUDE ExpGenes25
#> 1 -1 Cortical 48 2.7 1.14 0.63 1342
#> 2 01 Cortical 134 7.5 0.90 0.79 1155
#> 3 02 Cortical 85 4.8 0.87 0.82 1136
#> 4 03 Cortical 56 3.1 1.32 1.29 1755
#> 5 04 Cortical 124 7.0 1.75 1.76 2348
#> 6 05 Cortical 158 8.9 1.30 1.28 1745
#> 7 06 Cortical 152 8.5 0.80 0.77 1037
#> 8 07 Cortical 64 3.6 1.14 0.99 1417
#> 9 08 Cortical 1 0.1 1.16 1.16 1495
#> 10 09 Cortical 8 0.4 1.27 1.15 1583
#> ExpGenes
#> 1 10349
#> 2 10890
#> 3 10237
#> 4 10063
#> 5 11601
#> 6 11375
#> 7 10640
#> 8 10654
#> 9 1495
#> 10 5689The ExpGenes column contains the number of genes that
are expressed in any cell of the relevant cluster, while the
ExpGenes25 column contains the number of genes expressed in
at the least 25% of the cells of the relevant cluster
plot(summaryPlot)
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_bar()`).
#> Warning: Removed 4 rows containing missing values or values outside the scale range
#> (`geom_text()`).
Relevance of marker genes for the split clusters
It is possible to visualize how relevant are some marker genes for the clusters comprising a given clusterization
# these are some genes associated to each cortical layer
layersGenes <- list(
"L1" = c("Reln", "Lhx5"),
"L2/3" = c("Cux1", "Satb2"),
"L4" = c("Rorb", "Sox5"),
"L5/6" = c("Bcl11b", "Fezf2"),
"Prog" = c("Hes1", "Vim")
)c(splitHeatmap, splitScoreDF, splitPValueDF) %<-%
clustersMarkersHeatmapPlot(
obj,
groupMarkers = layersGenes,
clName = "split",
kCuts = 2L,
adjustmentMethod = "holm"
)
ComplexHeatmap::draw(splitHeatmap)
In the above graph, it is possible to see that the found clusters align well to the expression of the layers’ genes.
UMAP of the split clusterization
It is also possible to check how the clusters are spread in
a UMAP plot that uses the same data reduction methods as
the one used in the creation of the clusterization
c(umapPlot, cellsRDM) %<-%
cellsUMAPPlot(
obj,
clName = "split",
clusters = NULL,
useCoexEigen = TRUE, # Aligning these to `split` generation gives best res.
dataMethod = "LogLikelihood", # "AdjBinarized" is also a good choice
numComp = 50L, # number of components of the data reduced matrix
colors = NULL, # a list of user defined colors for the various clusters
numNeighbors = 30L, # UMAP related parameters
minPointsDist = 0.3 # UMAP related parameters
)
plot(umapPlot)Unfortunately, due to build-timeout limits, the above plot cannot be
displayed, as no COEX matrix was calculated in the
COTAN object, but we can show that the
clusterization is good even changing the data reduction method,
in this case the one from Seurat.
c(umapPlot, cellsRDM) %<-%
cellsUMAPPlot(
obj,
clName = "split",
clusters = NULL,
useCoexEigen = FALSE,
dataMethod = "LogNormalized",
numComp = 50L, # number of components of the data reduced matrix
genesSel = "HVG_Seurat",
numGenes = 500L,
colors = NULL, # a list of user defined colors for the various clusters
numNeighbors = 30L, # UMAP related parameters
minPointsDist = 0.3 # UMAP related parameters
)
#> Elaborating Reduced dimensionality Data Matrix - START
#> Running genes' selection: START
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes
#> ('-')
#> Normalizing layer: counts
#> Finding variable features for layer counts
#> Running genes' selection: DONE
#> Total calculations elapsed time: 3.08464932441711
#> Elaborating Reduced dimensionality Data Matrix - DONE
#> UMAP plot: START
#> 20:52:14 UMAP embedding parameters a = 0.9922 b = 1.112
#> 20:52:14 Read 1783 rows and found 50 numeric columns
#> 20:52:14 Using Annoy for neighbor search, n_neighbors = 30
#> 20:52:14 Building Annoy index with metric = cosine, n_trees = 50
#> 0% 10 20 30 40 50 60 70 80 90 100%
#> [----|----|----|----|----|----|----|----|----|----|
#> **************************************************|
#> 20:52:14 Writing NN index file to temp file /tmp/RtmpdrRmYh/file28cd53e02f03
#> 20:52:14 Searching Annoy index using 1 thread, search_k = 3000
#> 20:52:15 Annoy recall = 100%
#> 20:52:15 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
#> 20:52:17 Initializing from normalized Laplacian + noise (using RSpectra)
#> 20:52:17 Commencing optimization for 500 epochs, with 76514 positive edges
#> 20:52:17 Using rng type: pcg
#> 20:52:19 Optimization finished
#> Total calculations elapsed time: 5.32929825782776
#> UMAP plot: DONE
plot(umapPlot)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
Merged UT clusterization
Since the algorithm that creates the clusters is not directly geared to achieve cluster uniformity, there might be some clusters that can be merged back together and still be Uniform Transcript.
This is the purpose of the function
mergeUniformCellsClusters() that takes a existing
clusterization and tries to merge pairs of cluster. If a pair
forms a uniform cluster (via the uniformity check) the pair is actually
merged.
Since the number of possible pairs grows quadratically with the number of clusters, in order to avoid running the totality of the possible checks, the function relies on a related cluster distance to select the a-priori most likely to be merged.
Again, here, the most important parameters for the users are the
GDIThresholds inside the Uniform
Transcript checkers: they define how strict is the check.
Default constructed advance check gives a pretty strong guarantee of
uniformity for the cluster.
If one wants to achieve a more relaxed merge, and thus less clusters overall, it is possible to define a sequence of checkers, each less stringent than the previous, that will be applied one after the other: this allows to execute first the merges that result in the most uniform clusters and only later merge those that satisfy only the relaxed checks.
checkersList <-
list(
advChecker,
shiftCheckerThresholds(advChecker, 0.01), # Sensible threshold shifts really
shiftCheckerThresholds(advChecker, 0.03) # depend on the dataset
)
prevCheckRes <- data.frame()
# The following code shows the case when one wants to re-use the already
# calculated checks from the saved intermediate outputs.
# This can apply in cases one wants to add a new checker to the list above or
# in the cases the execution is interrupted prematurely.
#
# Note that a similar facility exists for the `cellsUniformClustering()`
# function above.
#
if (TRUE) {
# read from the last file among those named all_check_results_XX.csv
mergeDir <- file.path(outDir, cond, "leafs_merge")
resFiles <-
list.files(
path = mergeDir,
pattern = "all_check_results_.*csv",
full.names = TRUE
)
if (length(resFiles) != 0L) {
prevCheckRes <-
read.csv(resFiles[length(resFiles)], header = TRUE, row.names = 1L)
}
}
c(mergedClusters, mergedCoexDF) %<-%
mergeUniformCellsClusters(
obj,
clusters = splitClusters,
checkers = checkersList,
allCheckResults = prevCheckRes,
optimizeForSpeed = TRUE,
deviceStr = "cuda",
cores = 6L,
useDEA = TRUE,
saveObj = TRUE,
outDir = outDir
)
# add the newly found clusterization to the `COTAN` object
obj <-
addClusterization(
obj,
clName = "merge",
override = TRUE,
clusters = mergedClusters,
coexDF = mergedCoexDF
)
table(mergedClusters)Loading pre-calculated clusterization from data
data("vignette.merge.clusters", package = "COTAN")
mergedClusters <- vignette.merge.clusters[getCells(obj)]
# explicitly calculate the DEA of the clusterization to store it
mergedCoexDF <- DEAOnClusters(obj, clusters = mergedClusters)
#> Differential Expression Analysis - START
#> *********
#> Total calculations elapsed time: 0.657291889190674
#> Differential Expression Analysis - DONE
obj <-
addClusterization(
obj,
clName = "merge",
clusters = mergedClusters,
coexDF = mergedCoexDF,
override = FALSE
)
# this gives immediate visual information about which clusters were merged
table(splitClusters, mergedClusters)
#> mergedClusters
#> splitClusters -1 2 3 4 5 6 7 8 9
#> -1 76 0 0 0 0 0 0 0 0
#> 01 0 140 0 0 0 0 0 0 0
#> 02 0 86 0 0 0 0 0 0 0
#> 03 0 0 57 0 0 0 0 0 0
#> 04 0 0 0 126 0 0 0 0 0
#> 05 0 0 159 0 0 0 0 0 0
#> 06 0 152 0 0 0 0 0 0 0
#> 07 0 0 0 0 64 0 0 0 0
#> 08 0 0 0 0 0 75 0 0 0
#> 09 0 0 0 0 0 61 0 0 0
#> 10 0 0 0 0 0 0 197 0 0
#> 11 0 0 50 0 0 0 0 0 0
#> 12 0 0 0 0 0 0 137 0 0
#> 13 0 0 0 0 0 0 134 0 0
#> 14 0 0 0 0 0 0 0 145 0
#> 15 0 0 0 0 0 0 0 0 124Extract merge clusters’ statistics
It is possible to extract some statistics about a stored clusterization:
c(summaryData, summaryPlot) %<-%
clustersSummaryPlot(
obj,
clName = "merge",
plotTitle = "merge summary",
condName = "origin"
)
head(summaryData, n = 10L)
#> merge origin CellNumber CellPercentage MeanUDE MedianUDE ExpGenes25
#> 1 -1 Cortical 48 2.7 1.14 0.63 1342
#> 2 2 Cortical 371 20.8 0.85 0.79 1101
#> 3 3 Cortical 215 12.1 1.30 1.28 1747
#> 4 4 Cortical 124 7.0 1.75 1.76 2348
#> 5 5 Cortical 64 3.6 1.14 0.99 1417
#> 6 6 Cortical 9 0.5 1.26 1.15 1797
#> 7 7 Cortical 7 0.4 0.96 0.87 2049
#> 8 8 Cortical 3 0.2 1.11 0.61 3037
#> 9 9 Cortical 3 0.2 1.46 1.66 3524
#> 10 -1 Other 28 1.6 1.26 1.06 1329
#> ExpGenes
#> 1 10349
#> 2 12016
#> 3 11804
#> 4 11601
#> 5 10654
#> 6 6035
#> 7 4694
#> 8 3037
#> 9 3524
#> 10 9330
plot(summaryPlot)
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_bar()`).
#> Warning: Removed 2 rows containing missing values or values outside the scale range
#> (`geom_text()`).
Relevance of marker genes for the merge clusters
Again, it is possible to visualize how relevant are the marker genes from above:
c(mergeHeatmap, mergeScoresDF, mergePValuesDF) %<-%
clustersMarkersHeatmapPlot(
obj,
groupMarkers = layersGenes,
clName = "merge",
condNameList = list("origin")
)
ComplexHeatmap::draw(mergeHeatmap)
It is also possible to use any clusterization as if a condition:
c(mergeVsSplitHeatmap, ..) %<-%
clustersMarkersHeatmapPlot(
obj,
clName = "merge",
condNameList = list("split"),
conditionsList = list(splitClusters)
)
ComplexHeatmap::draw(mergeVsSplitHeatmap)
UMAP of the merge clusterization
It is also possible to check how the clusters are spread in
a UMAP plot that uses the same (or other) data reduction
methods as the one used in the creation of the
clusterization
Again it is possible to plot the clusterization on a
UMAP plot
c(umapPlot, .) %<-%
cellsUMAPPlot(
obj,
clName = "merge",
clusters = NULL,
useCoexEigen = TRUE,
dataMethod = "LogLikelihood", # "AdjBinarized" is also a good choice
numComp = 50L, # number of components of the data reduced matrix
colors = NULL, # a list of user defined colors for the various clusters
numNeighbors = 30L, # UMAP related parameters
minPointsDist = 0.3 # UMAP related parameters
)
plot(umapPlot)Again, due to build-timeout limits, the above plot cannot be
displayed, as no COEX matrix was calculated in the
COTAN object, but we can show that the
clusterization is good even changing the data reduction method,
in this case the one from scanpy.
c(umapPlot, .) %<-%
cellsUMAPPlot(
obj,
clName = "merge",
clusters = NULL,
useCoexEigen = FALSE,
dataMethod = "LogNormalized", # "AdjBinarized" is also a good choice
numComp = 50L, # number of components of the data reduced matrix
genesSel = "HVG_Scanpy",
numGenes = 500L,
colors = NULL, # a list of user defined colors for the various clusters
numNeighbors = 30L, # UMAP related parameters
minPointsDist = 0.3 # UMAP related parameters
)
#> Elaborating Reduced dimensionality Data Matrix - START
#> Running genes' selection: START
#> Warning: Feature names cannot have underscores ('_'), replacing with dashes
#> ('-')
#> Normalizing layer: counts
#> Finding variable features for layer data
#> Running genes' selection: DONE
#> Total calculations elapsed time: 2.95388340950012
#> Elaborating Reduced dimensionality Data Matrix - DONE
#> UMAP plot: START
#> 20:52:27 UMAP embedding parameters a = 0.9922 b = 1.112
#> 20:52:27 Read 1783 rows and found 50 numeric columns
#> 20:52:27 Using Annoy for neighbor search, n_neighbors = 30
#> 20:52:27 Building Annoy index with metric = cosine, n_trees = 50
#> 0% 10 20 30 40 50 60 70 80 90 100%
#> [----|----|----|----|----|----|----|----|----|----|
#> **************************************************|
#> 20:52:27 Writing NN index file to temp file /tmp/RtmpdrRmYh/file28cd4d337a0a
#> 20:52:27 Searching Annoy index using 1 thread, search_k = 3000
#> 20:52:27 Annoy recall = 100%
#> 20:52:28 Commencing smooth kNN distance calibration using 1 thread with target n_neighbors = 30
#> 20:52:30 Initializing from normalized Laplacian + noise (using RSpectra)
#> 20:52:30 Commencing optimization for 500 epochs, with 73286 positive edges
#> 20:52:30 Using rng type: pcg
#> 20:52:32 Optimization finished
#> Total calculations elapsed time: 5.35580229759216
#> UMAP plot: DONE
plot(umapPlot)
#> Warning: No shared levels found between `names(values)` of the manual scale and the
#> data's fill values.
Vignette clean-up stage
The next few lines are just to clean.
if (file.exists(file.path(outDir, paste0(cond, ".cotan.RDS")))) {
# delete file if it exists
file.remove(file.path(outDir, paste0(cond, ".cotan.RDS")))
}
if (file.exists(file.path(outDir, paste0(cond, "_times.csv")))) {
# delete file if it exists
file.remove(file.path(outDir, paste0(cond, "_times.csv")))
}
if (dir.exists(file.path(outDir, cond))) {
unlink(file.path(outDir, cond), recursive = TRUE)
}
# if (dir.exists(file.path(outDir, GEO))) {
# unlink(file.path(outDir, GEO), recursive = TRUE)
# }
# stop logging to file
setLoggingFile("")
#> Closing previous log file - Setting log file to be:
file.remove(file.path(outDir, "vignette_uniform_clustering.log"))
#> [1] TRUE
options(prevOptState)Sys.time()
#> [1] "2026-04-29 20:52:33 UTC"
sessionInfo()
#> R version 4.6.0 (2026-04-24)
#> Platform: x86_64-pc-linux-gnu
#> Running under: Ubuntu 24.04.4 LTS
#>
#> Matrix products: default
#> BLAS: /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
#> LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so; LAPACK version 3.12.0
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> time zone: Etc/UTC
#> tzcode source: system (glibc)
#>
#> attached base packages:
#> [1] stats graphics grDevices utils datasets methods base
#>
#> other attached packages:
#> [1] zeallot_0.2.0 COTAN_2.12.0 BiocStyle_2.40.0
#>
#> loaded via a namespace (and not attached):
#> [1] matrixStats_1.5.0 spatstat.sparse_3.1-0
#> [3] httr_1.4.8 RColorBrewer_1.1-3
#> [5] doParallel_1.0.17 tools_4.6.0
#> [7] sctransform_0.4.3 R6_2.6.1
#> [9] lazyeval_0.2.3 uwot_0.2.4
#> [11] ggdist_3.3.3 GetoptLong_1.1.1
#> [13] withr_3.0.2 sp_2.2-1
#> [15] gridExtra_2.3 parallelDist_0.2.7
#> [17] progressr_0.19.0 cli_3.6.6
#> [19] Biobase_2.72.0 textshaping_1.0.5
#> [21] Cairo_1.7-0 spatstat.explore_3.8-0
#> [23] fastDummies_1.7.6 labeling_0.4.3
#> [25] sass_0.4.10 Seurat_5.5.0
#> [27] S7_0.2.2 spatstat.data_3.1-9
#> [29] readr_2.2.0 proxy_0.4-29
#> [31] ggridges_0.5.7 pbapply_1.7-4
#> [33] systemfonts_1.3.2 rentrez_1.2.4
#> [35] parallelly_1.47.0 limma_3.68.0
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