ExperimentSubset: Manages Subsets of Data with Bioconductor Experiment Objects
26 October 2026
Package
ExperimentSubset 1.23.14
Contents
- 1 Installation
- 2 Motivation
- 3 A Brief Description
- 4 Overview of the
ExperimentSubsetclass - 5 Core methods of
ExperimentSubsetclass - 6 Using the
ExperimentSubsetobject: A toy example - 7 Using the
ExperimentSubsetobject: an example with real scRNA-seq data - 8 Supported Input Object Classes
- 9 Methods for
ExperimentSubset - 10 Implementation Details
- 11 Session Information
1 Installation
if (!requireNamespace("BiocManager", quietly=TRUE)){
install.packages("BiocManager")}
BiocManager::install("ExperimentSubset")To install the latest version from GitHub, use the following code:
BiocManager::install("campbio/ExperimentSubset")Loading the package:
library(ExperimentSubset)2 Motivation
Experiment objects such as the SummarizedExperiment or SingleCellExperiment
are data containers for one or more matrix-like assays along with the associated
row and column data. Often only a subset of the original data is needed for
down-stream analysis. For example, filtering out poor quality samples will
require excluding some columns before analysis. The ExperimentSubset object
is a container to efficiently manage different subsets of the same data without
having to make separate objects for each new subset and can be used as a
drop-in replacement for other experiment classes.
3 A Brief Description
ExperimentSubset package enables users to perform flexible subsetting of
Single-Cell data that comes from the same experiment as well as the consequent
storage of these subsets back into the same object. In general, it offers the
same interface to the users as the SingleCellExperiment container which is
one the most widely used containers for Single-Cell data. However, in addition
to the features offered by SingleCellExperiment container, ExperimentSubset
offers subsetting features while hiding the implementation details from the
users. It does so by creating references to the subset rows and columns
instead of storing a new assay whenever possible instead of actually copying
the redundant data. Functions from SingleCellExperiment such as assay,
rowData and colData can be used for regular assays as one would normally do,
as well as with newly created subsets of the data. Most code written for the
input object also works on the ExperimentSubset object, and subsets are
reached through an extra subsetName argument and the subset methods described
below. Subsetting an object that contains subsets with [ is not supported
yet.
4 Overview of the ExperimentSubset class
The ExperimentSubset package is composed of multiple classes that support
subsets management capability depending upon the class of the input experiment
object. The currently supported experiment classes which can be used with
ExperimentSubset are SummarizedExperiment, RangedSummarizedExperiment,
SingleCellExperiment, TreeSummarizedExperiment and SpatialExperiment.
The ExperimentSubset package adds an additional slot subsets to the objects
from these classes which enables support for the creation and management of
subsets of data.
Each subset inside the ExperimentSubset object (more specifically inside the
subsets slot of the object) is stored as an AssaySubset instance. This
AssaySubset instance creates reference to the row and column indices for this
particular subset against a parent (which can be the inherited parent object or
another subset). In case a new assay is to be stored against a subset, it is
stored as a separate experiment object (same class as the inherited object)
inside the subset.
5 Core methods of ExperimentSubset class
Many methods of the SummarizedExperiment and SingleCellExperiment classes
accept an additional subsetName argument to work on a subset, as described in
the section on overridden methods below. In addition, some core methods for the
creation and manipulation of subsets are provided with the ExperimentSubset
class.
5.1 ExperimentSubset constructor
The constructor method allows the creation of an ExperimentSubset object from
an input experiment object of one of the five supported classes listed above.
Additionally, if needed, a subset can be directly
created from within the constructor by providing input a named list to the
subset parameter.
#set a seed so the random counts below are reproducible
set.seed(1)
counts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10)
sce <- SingleCellExperiment(list(counts = counts))
es <- ExperimentSubset(sce)
es## class: SubsetSingleCellExperiment
## dim: 10 10
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames: NULL
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## subsets(0):
## subsetAssays(0):Additionally, an ExperimentSubset object can also be created directly from
generally loaded data such as counts matrices, which can be passed to the
constructor function in a list.
counts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10)
ExperimentSubset(list(counts = counts))## class: SubsetSingleCellExperiment
## dim: 10 10
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames: NULL
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## subsets(0):
## subsetAssays(0):5.2 createSubset
The createSubset method as evident from the name, creates a subset from an
already available assay in the object. The subsetName (a character
string), rows (a numeric or character vector), cols (a numeric or
character vector) and parentAssay (a character string) are the standard
parameters of the createSubset method. If rows or cols are missing or
NULL, all of the rows or columns are selected from the specified
parentAssay. If parentAssay is missing or NULL, the first available
assay from the parent object is linked as the parent of this subset. The
parentAssay can be an assay in the parent object, a subset or an assay
within a subset.
es <- createSubset(es,
subsetName = "subset1",
rows = c(1:2),
cols = c(1:5),
parentAssay = "counts")
es## class: SubsetSingleCellExperiment
## dim: 10 10
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames: NULL
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## subsets(1): subset1
## subsetAssays(1): subset15.3 setSubsetAssay and getSubsetAssay
The setSubsetAssay method should be used when a subset assay needs to be
stored in a previously created subset. This is specifically
different from the createSubset method which only creates a subset by
referencing to a defined parentAssay where the internalAssay of the subset
has no assays stored. The setSubsetAssay method however, is used to store an
assay in this internalAssay slot of the subset which in fact is a subset
experiment object of the same class as the parent object.
subset1Assay <- assay(es, "subset1")
subset1Assay[,] <- subset1Assay[,] + 1
es <- setSubsetAssay(es,
subsetName = "subset1",
inputMatrix = subset1Assay,
subsetAssayName = "subset1Assay")
es## class: SubsetSingleCellExperiment
## dim: 10 10
## metadata(0):
## assays(1): counts
## rownames: NULL
## rowData names(0):
## colnames: NULL
## colData names(0):
## reducedDimNames(0):
## mainExpName: NULL
## altExpNames(0):
## subsets(1): subset1
## subsetAssays(2): subset1 subset1AssayThe parameters of interest against this method are subsetName which specifies
the name of the subset inside which the an input assay should be stored,
inputMatrix which is a matrix-type object to be stored as an assay inside a
subset specified by the subsetName parameter and lastly the subsetAssayName
parameter which represents the name of the new assay.
To get a subset assay, getSubsetAssay method can be used:
#get assay from 'subset1'
getSubsetAssay(es, "subset1")## [,1] [,2] [,3] [,4] [,5]
## [1,] 8 8 3 5 5
## [2,] 10 2 11 13 12#get internal 'subset1Assay'
getSubsetAssay(es, "subset1Assay")## [,1] [,2] [,3] [,4] [,5]
## [1,] 9 9 4 6 6
## [2,] 11 3 12 14 13Apart from setSubsetAssay and getSubsetAssay methods, assay and assay<-
methods can also be used for the same purpose. Their usage has been described
in the overridden methods section below.
5.4 subsetSummary
The subsetSummary method displays an overall summary of the
ExperimentSubset object including the assays in the parent object, the list
of subsets along with the stored assays, reduced dimensions, alt experiments
and other supplementary information that may help the users understand the
current condition of the object. The most important piece of information
displayed by this method is the hierarchical ‘parent-subset’ link against each
subset in the object.
subsetSummary(es)## Main assay(s):
## counts
##
## Subset(s):
## Name Dim Parent Assays
## 1 subset1 2, 5 counts subset1Assay5.5 Additional helper methods
Helper methods have been provided for use by the users during specific circumstances while manipulating subsets of data. These helper methods and their short descriptions are given below:
subsetNamesReturns the names of all available subsets (excluding internal subset assays)subsetAssayNamesReturns the names of all available subsets (including internal subset assays)subsetCountReturns the total count of the subsets (excluding internal subset assays)subsetAssayCountReturns the total count of the subsets (including internal subset assays)subsetDimReturns the dimensions of a specified subsetsubsetColDataGets or sets colData from/to a subsetsubsetRowDataGets or sets rowData from/to a subsetsubsetColnamesGets or sets colnames from/to a subsetsubsetRownamesGets or sets rownames from/to a subsetsubsetParentReturns the ‘subset-parent’ link of a specified subsetsetSubsetAssaySets an assay to a subsetgetSubsetAssayGets an assay from a subset
5.5.1 subsetColData & subsetRowData
Both subsetColData and subsetRowData getter methods take in an additional
logical parameter parentColData or parentRowData that specifies if the
returned ‘colData’ or ‘rowData’ should include the ‘colData’ and ‘rowData’
from the parent object as well. By default, parentColData and parentRowData
parameters are set to FALSE. Same applies to the usage of inherited rowData
and colData methods.
#store colData to parent object
colData(es) <- cbind(colData(es), sampleID = seq_len(dim(es)[2]))
#store colData to 'subset1' using option 1
colData(es, subsetName = "subset1") <- cbind(
colData(es, subsetName = "subset1"),
subsetSampleID1 = seq_len(subsetDim(es, "subset1")[2]))
#store colData to 'subset1' using option 2
subsetColData(es, "subset1") <- cbind(
subsetColData(es, "subset1"),
subsetSampleID2 = seq_len(subsetDim(es, "subset1")[2]))
#get colData from 'subset1' without parent colData
subsetColData(es, "subset1", parentColData = FALSE)## DataFrame with 5 rows and 2 columns
## subsetSampleID1 subsetSampleID2
## <integer> <integer>
## 1 1 1
## 2 2 2
## 3 3 3
## 4 4 4
## 5 5 5#get colData from 'subset1' with parent colData
subsetColData(es, "subset1", parentColData = TRUE)## DataFrame with 5 rows and 3 columns
## sampleID subsetSampleID1 subsetSampleID2
## <integer> <integer> <integer>
## 1 1 1 1
## 2 2 2 2
## 3 3 3 3
## 4 4 4 4
## 5 5 5 5#the same works with the `colData` method (and with `rowData` and the
#`parentRowData` parameter for row data)
colData(es, subsetName = "subset1", parentColData = FALSE) #without parent data## DataFrame with 5 rows and 2 columns
## subsetSampleID1 subsetSampleID2
## <integer> <integer>
## 1 1 1
## 2 2 2
## 3 3 3
## 4 4 4
## 5 5 5colData(es, subsetName = "subset1", parentColData = TRUE) #with parent data## DataFrame with 5 rows and 3 columns
## sampleID subsetSampleID1 subsetSampleID2
## <integer> <integer> <integer>
## 1 1 1 1
## 2 2 2 2
## 3 3 3 3
## 4 4 4 4
## 5 5 5 55.6 Overridden methods for ExperimentSubset class
These are the methods that have been overridden from other classes to support
the subset feature of the ExperimentSubset objects by introducing an
additional parameter subsetName to these methods. These methods can simply
be called on any ExperimentSubset object to get or set from the parent object
or from any subset by passing the optional subsetName parameter.
The methods include rowData, rowData<-, colData,
colData<-, metadata, metadata<-, reducedDim, reducedDim<-,
reducedDims, reducedDims<-, reducedDimNames, reducedDimNames<-,
altExp, altExp<-, altExps, altExps<-, altExpNames and
altExpNames<-, and for SpatialExperiment objects spatialCoords and
spatialData. All of these methods can be used with the subsets by providing
the optional subsetName parameter.
5.6.1 assay-get & assay-set inherited methods
An exception to the above approach is the use of assay and assay<- methods,
both of which have a slightly different usage as described below:
Because the assay<- setter method in the case of a subset needs to store the
assay inside the subset, we need to specify the subset name inside which the
assay should be stored as i parameter and define the new name of the subset
assay as the additional subsetAssayName parameter.
#creating a dummy ES object
counts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10)
sce <- SingleCellExperiment(list(counts = counts))
es <- ExperimentSubset(sce)
#create a subset
es <- createSubset(es, subsetName = "subset1", rows = c(1:2), cols = c(1:4))
#store an assay inside the newly created 'subset1'
#note that 'assay<-' setter has two important parameters 'x' and 'i' where
#'x' is the object and 'i' is the assay name, but in the case of storing to a
#subset we use 'x' as the object, 'i' as the subset name inside which the assay
#should be stored and an additional 'subsetAssayName' parameter which defines
#the name of the new assay
assay(
x = es,
i = "subset1",
subsetAssayName = "subset1InternalAssay") <- matrix(rpois(8, lambda = 10),
ncol=4, nrow=2)Using assay getter method is simple, as no additional parameter is required
unlike in the setter method.
#assay getter has parameters 'x' which is the input object, 'i' which can either
#be a assay name in the parent object, a subset name or a subset assay name
#getting 'counts' from parent es object
assay(
x = es,
i = "counts"
)## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8] [,9] [,10]
## [1,] 11 8 2 6 4 8 6 7 14 12
## [2,] 15 9 7 8 11 11 16 14 13 10
## [3,] 9 9 8 10 8 10 9 8 9 8
## [4,] 7 13 13 15 14 5 6 12 5 8
## [5,] 9 10 8 8 7 11 10 8 12 12
## [6,] 12 11 9 6 15 7 11 5 8 6
## [7,] 13 9 12 10 9 13 6 14 5 10
## [8,] 10 9 15 13 5 9 5 14 10 9
## [9,] 7 12 10 2 11 9 10 12 7 14
## [10,] 6 13 8 9 13 12 8 4 12 10#getting just the 'subset1' from es object
assay(
x = es,
i = "subset1"
)## [,1] [,2] [,3] [,4]
## [1,] 11 8 2 6
## [2,] 15 9 7 8#getting the 'subset1InternalAssay' from inside the 'subset1'
assay(
x = es,
i = "subset1InternalAssay"
)## [,1] [,2] [,3] [,4]
## [1,] 10 6 12 9
## [2,] 10 14 11 146 Using the ExperimentSubset object: A toy example
Creating the ExperimentSubset object is as simple as passing an experiment
object to the ExperimentSubset constructor:
counts <- matrix(rpois(100, lambda = 10), ncol=10, nrow=10)
sce <- SingleCellExperiment(list(counts = counts))
es <- ExperimentSubset(sce)
subsetSummary(es)## Main assay(s):
## counts
##
## Subset(s):
## NULLCreate a subset that includes the first 5 rows and columns only:
es <- createSubset(es,
subsetName = "subset1",
rows = c(1:5),
cols = c(1:5),
parentAssay = "counts")
subsetSummary(es)## Main assay(s):
## counts
##
## Subset(s):
## Name Dim Parent
## 1 subset1 5, 5 countsCreate another subset from subset1 by only keeping the first two rows:
es <- createSubset(es,
subsetName = "subset2",
rows = c(1:2),
cols = c(1:5),
parentAssay = "subset1")
subsetSummary(es)## Main assay(s):
## counts
##
## Subset(s):
## Name Dim Parent
## 1 subset1 5, 5 counts
## 2 subset2 2, 5 subset1 -> countsGet assay from subset2 and update values:
subset2Assay <- assay(es, "subset2")
subset2Assay[,] <- subset2Assay[,] + 1Store the updated assay back to subset2 using one of the two approaches:
#approach 1
es <- setSubsetAssay(es,
subsetName = "subset2",
inputMatrix = subset2Assay,
subsetAssayName = "subset2Assay_a1")
#approach 2
assay(es, "subset2", subsetAssayName = "subset2Assay_a2") <- subset2Assay
subsetSummary(es)## Main assay(s):
## counts
##
## Subset(s):
## Name Dim Parent Assays
## 1 subset1 5, 5 counts
## 2 subset2 2, 5 subset1 -> counts subset2Assay_a1, subset2Assay_a2Store an experiment object in the altExp slot of subset2:
altExp(x = es,
e = "subset2_alt1",
subsetName = "subset2") <- SingleCellExperiment(assays = list(
counts = assay(es, "subset2")
))Show the current condition of ExperimentSubset object:
subsetSummary(es)## Main assay(s):
## counts
##
## Subset(s):
## Name Dim Parent Assays
## 1 subset1 5, 5 counts
## 2 subset2 2, 5 subset1 -> counts subset2Assay_a1, subset2Assay_a2
## AltExperiments
## 1
## 2 subset2_alt17 Using the ExperimentSubset object: an example with real scRNA-seq data
Installing and loading required packages:
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(c("TENxPBMCData", "scrapper"))library(ExperimentSubset)
library(TENxPBMCData)
library(scrapper)Load PBMC4K dataset and create ExperimentSubset object:
tenx_pbmc4k <- TENxPBMCData(dataset = "pbmc4k")
es <- ExperimentSubset(tenx_pbmc4k)
subsetSummary(es)Compute per-cell QC metrics on the counts matrix using the scrapper
library and store the total count (sum) for each cell:
qcMetrics <- computeRnaQcMetrics(assay(es, "counts"), subsets = list())
colData(es)$sum <- qcMetrics$sumFilter cells with low column sum and create a new subset called ‘filteredCells’:
filteredCellsIndices <- which(colData(es)$sum > 1500)
es <- createSubset(es, "filteredCells", cols = filteredCellsIndices,
parentAssay = "counts")
subsetSummary(es)Normalize ‘filteredCells’ subset using scrapper library and store it back.
Size factors are computed from each cell’s total count and centered at one:
filteredCounts <- assay(es, "filteredCells")
filteredSizeFactors <- centerSizeFactors(colSums(filteredCounts))
normalizedCounts <- normalizeCounts(filteredCounts,
size.factors = filteredSizeFactors)
assay(es, "filteredCells",
subsetAssayName = "filteredCellsNormalized") <- normalizedCounts
subsetSummary(es)Find highly variable genes from the normalized assay in the previous step
using scrapper library against the ‘filteredCells’ subset only:
geneVariances <- modelGeneVariances(assay(es, "filteredCellsNormalized"))
residualVariances <- geneVariances$statistics$residuals
topHVG1000 <- chooseHighlyVariableGenes(residualVariances, top = 1000)
es <- createSubset(es, "hvg1000", rows = topHVG1000,
parentAssay = "filteredCellsNormalized")
subsetSummary(es)Run ‘PCA’ on the highly variable genes computed in the last step using
scrapper library against the ‘hvg1000’ subset only. runPca returns the PCs
in rows, so transpose them to put cells in rows:
pcaResult <- runPca(assay(es, "hvg1000"))
pcaScores <- t(pcaResult$components)
reducedDim(es, type = "PCA", subsetName = "hvg1000") <- pcaScoresShow the current condition of the ExperimentSubset object:
subsetSummary(es)8 Supported Input Object Classes
- SummarizedExperiment
- RangedSummarizedExperiment
- SingleCellExperiment
- TreeSummarizedExperiment
- SpatialExperiment
9 Methods for ExperimentSubset
ExperimentSubsetconstructorcreateSubsetsetSubsetAssaygetSubsetAssaysubsetSummarysubsetParentsubsetCountsubsetAssayCountsubsetNamessubsetAssayNamessubsetDimsubsetRowDatasubsetColDatasubsetColnamessubsetRownamessubsetRowData<-subsetColData<-subsetColnames<-subsetRownames<-showassayassay<-rowDatarowData<-colDatacolData<-metadatametadata<-reducedDimreducedDim<-reducedDimsreducedDims<-reducedDimNamesreducedDimNames<-altExpaltExp<-altExpsaltExps<-altExpNamesaltExpNames<-subsetSpatialCoordssubsetSpatialDatasubsetSpatialData<-subsetRowLinkssubsetColLinksspatialCoordsspatialDataspatialData<-rowLinkscolLinks
10 Implementation Details
The internal structure of an ExperimentSubset class is described
below:
10.1 Inherited parent object
The ExperimentSubset object during creation is assigned one of the classes
SubsetSummarizedExperiment, SubsetRangedSummarizedExperiment,
SubsetSingleCellExperiment, SubsetTreeSummarizedExperiment or
SubsetSpatialExperiment, each of which inherits from the class of the input
object.
This ensures that ExperimentSubset object can be manipulated in a fashion
similar to the input object class and so can be used as a drop-in replacement
for these classes. Most methods that work on the input object class work on
the ExperimentSubset objects as well; subsetting with [ is an exception
for objects that contain subsets.
10.2 Additional subsets slot
The subsets slot of the ExperimentSubset object is a SimpleList, where
each element in this list is an object of an internal AssaySubset class.
The slot itself is not directly accessible to the users and should be accessed
through the provided methods of the ExperimentSubset package. Each element
represents one subset linked to the experiment object in the parent object.
The structure of each subset is described below:
10.2.1 subsetName
A character string that represents a user-defined name of the subset.
10.2.2 rowIndices
A numeric vector that stores the indices of the selected rows in the linked
parent assay within for this subset.
10.2.3 colIndices
A numeric vector that stores the indices of the selected columns in the
linked parent assay for this subset.
10.2.4 parentAssay
A character string that stores the name of the immediate parent to which the
subset is linked. The parentAssay can be an assay in the parent
ExperimentSubset object or any subset or any internalAssay of a subset.
10.2.5 internalAssay
The internalAssay slot stores an experiment object of same type as the input
object but with the dimensions of the subset. The internalAssay is initially
an empty experiment object with only dimensions set to enable manipulation, but
can be used to store additional data against a subset such as assay,
rowData, colData, reducedDims, altExps and metadata.
11 Session Information
sessionInfo()## R version 4.6.1 (2026-06-24)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.24-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0 LAPACK version 3.12.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB LC_COLLATE=C
## [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: America/New_York
## tzcode source: system (glibc)
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] ExperimentSubset_1.23.14 TreeSummarizedExperiment_2.21.0
## [3] Biostrings_2.81.9 XVector_0.53.0
## [5] SpatialExperiment_1.23.0 SingleCellExperiment_1.35.2
## [7] SummarizedExperiment_1.43.0 Biobase_2.73.2
## [9] GenomicRanges_1.65.4 Seqinfo_1.3.2
## [11] IRanges_2.47.5 S4Vectors_0.51.10
## [13] BiocGenerics_0.59.12 generics_0.1.4
## [15] MatrixGenerics_1.25.0 matrixStats_1.5.0
## [17] BiocStyle_2.41.0
##
## loaded via a namespace (and not attached):
## [1] rjson_0.2.23 xfun_0.62 bslib_0.12.0
## [4] lattice_0.23-1 vctrs_0.7.3 tools_4.6.1
## [7] yulab.utils_0.2.5 parallel_4.6.1 tibble_3.3.1
## [10] pkgconfig_2.0.3 Matrix_1.7-6 lifecycle_1.0.5
## [13] compiler_4.6.1 treeio_1.37.1 codetools_0.2-20
## [16] htmltools_0.5.9 sass_0.4.10 yaml_2.3.12
## [19] lazyeval_0.2.3 pillar_1.11.1 crayon_1.5.3
## [22] jquerylib_0.1.4 tidyr_1.3.2 BiocParallel_1.47.0
## [25] DelayedArray_0.39.8 cachem_1.1.0 magick_2.9.1
## [28] abind_1.4-8 nlme_3.1-171 tidyselect_1.2.1
## [31] digest_0.6.39 dplyr_1.2.1 purrr_1.2.2
## [34] bookdown_0.48 fastmap_1.2.0 grid_4.6.1
## [37] cli_3.6.6 SparseArray_1.13.4 magrittr_2.0.5
## [40] S4Arrays_1.13.2 ape_5.8-1 rappdirs_0.3.4
## [43] rmarkdown_2.32 otel_0.2.0 evaluate_1.0.5
## [46] knitr_1.52 rlang_1.3.0 Rcpp_1.1.2
## [49] glue_1.8.1 tidytree_0.4.8 BiocManager_1.30.27
## [52] jsonlite_2.0.0 R6_2.6.1 fs_2.1.0