Contents

1 Installation

if (!requireNamespace("BiocManager", quietly=TRUE)){
    install.packages("BiocManager")}
BiocManager::install("ExperimentSubset")

To install the latest version from Github, use the following code:

library(devtools)
install_github("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. This allows the users to use the ExperimentSubset container simply as if they were using SingleCellExperiment container with no change required to the existing code.

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 include SummarizedExperiment, RangedSummarizedExperiment and SingleCellExperiment.

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

While all methods available with SummarizedExperiment and SingleCellExperiment classes have been overridden to support the ExperimentSubset class with additional support for subsets, some core methods for the creation and manipulation of subsets have been provided with the ExperimentSubset class.

5.1 ExperimentSubset constructor

The constructor method allows the creation of an ExperimentSubset object from an input experiment object as long as it is inherited from SummarizedExperiment class. Additionally, if needed, a subset can be directly created from within the constructor by providing input a named list to the subset parameter.

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), rowIndices (a numeric or character vector), colIndices (a numeric or character vector) and parentAssay (a character string) are the standard parameters of the createSubset method. If rowIndices or colIndices 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): subset1

5.3 setSubsetAssay and getSubsetAssay

The setSubsetAssay method should be used when a subset assay needs to be stored either 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 subset1Assay

The 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   14   11   13   10
## [2,]   13   12   12   12    5
#get internal 'subset1Assay'
getSubsetAssay(es, "subset1Assay")
##      [,1] [,2] [,3] [,4] [,5]
## [1,]    9   15   12   14   11
## [2,]   14   13   13   13    6

Apart 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 subset1Assay

5.5 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:

  1. subsetNames Returns the names of all available subsets (excluding internal subset assays)
  2. subsetAssayNames Returns the names of all available subsets (including internal subset assays)
  3. subsetCount Returns the total count of the subsets (excluding internal subset assays)
  4. subsetAssayCount Returns the total count of the subsets (including internal subset assays)
  5. subsetDim Returns the dimensions of a specified subset
  6. subsetColData Gets or sets colData from/to a subset
  7. subsetRowData Gets or sets rowData from/to a subset
  8. subsetColnames Gets or sets colnames from/to a subset
  9. subsetRownames Gets or sets rownames from/to a subset
  10. subsetParent Returns the ’subset-parent` link of a specified subset
  11. setSubsetAssay Sets an assay to a subset
  12. getSubsetAssay Gets 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(1:dim(es)[2]))

#store colData to 'subset1' using option 1
colData(es, subsetName = "subset1") <- cbind(
  colData(es, subsetName = "subset1"), 
  subsetSampleID1 = seq(1:subsetDim(es, "subset1")[2]))

#store colData to 'subset1' using option 2
subsetColData(es, "subset1") <- cbind(
  subsetColData(es, "subset1"), 
  subsetSampleID2 = seq(1: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
#same applies to `colData` and `rowData` methods when using with subsets
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               5
colData(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               5

5.6 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<-. All of the 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(100, 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,]    8   13   10    9    8    9   11   13   16     6
##  [2,]   14    9   14    9    7    7    8   13   13     7
##  [3,]   17   11    7    7   10   12   11    8    9     6
##  [4,]   12    8    5   16   15    8    6   11    9     7
##  [5,]   10    7    4    9    7   14   11   19   11    14
##  [6,]    7   12    7    3    8   13   10    6   12     9
##  [7,]    8   11    4    9    9   11   12   12   10    14
##  [8,]    9    8    6   14   13    5   11    9    7    11
##  [9,]   11   11    3    9    8   14   11   11   11     9
## [10,]   10   10    6    8    9   11   10   10   13     8
#getting just the 'subset1' from es object
assay(
  x = es,
  i = "subset1"
)
##      [,1] [,2] [,3] [,4]
## [1,]    8   13   10    9
## [2,]   14    9   14    9
#getting the 'subset1InternalAssay' from inside the 'subset1'
assay(
  x = es,
  i = "subset1InternalAssay"
)
##      [,1] [,2] [,3] [,4]
## [1,]   12    9   12   10
## [2,]   11   19   11   12

6 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):
## NULL

Create 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 counts

Create 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 -> counts

Get assay from subset2 and update values:

subset2Assay <- assay(es, "subset2")
subset2Assay[,] <- subset2Assay[,] + 1

Store 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_a2

Store an experiment object in the altExp slot of subset2:

altExp(x = es,
       e = "subset2_alt1",
       subsetName = "subset2") <- SingleCellExperiment(assay = 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_alt1

7 Using the ExperimentSubset object: An example with real single cell RNA-seq data

Installing and loading required packages:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")
BiocManager::install(version = "3.11", ask = FALSE)
BiocManager::install(c("TENxPBMCData", "scater", "scran"))
library(ExperimentSubset)
library(TENxPBMCData)
library(scater)
library(scran)

Load PBMC4K dataset and create ExperimentSubset object:

tenx_pbmc4k <- TENxPBMCData(dataset = "pbmc4k")
es <- ExperimentSubset(tenx_pbmc4k)
subsetSummary(es)

Compute perCellQCMetrics on counts matrix:

perCellQCMetrics <- perCellQCMetrics(assay(es, "counts"))
colData(es) <- cbind(colData(es), perCellQCMetrics)

Filter 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 scater library and store it back:

assay(es, "filteredCells", subsetAssayName = "filteredCellsNormalized") <- normalizeCounts(assay(es, "filteredCells"))
subsetSummary(es)

Find highly variable genes from the normalized assay in the previous step using scran library against the ‘filteredCells’ subset only:

topHVG1000 <- getTopHVGs(modelGeneVar(assay(es, "filteredCellsNormalized")), n = 1000)
es <- createSubset(es, "hvg1000", rows = topHVG1000, parentAssay = "filteredCellsNormalized")
subsetSummary(es)

Run ‘PCA’ on the highly variable genes computed in the last step using scater library against the ‘filteredCells’ subset only:

reducedDim(es, type = "PCA", subsetName = "hvg1000") <- calculatePCA(assay(es, "hvg1000"))

Show the current condition of the ExperimentSubset object:

subsetSummary(es)

8 Supported Input Object Classes

  1. SummarizedExperiment
  2. RangedSummarizedExperiment
  3. SingleCellExperiment
  4. TreeSummarizedExperiment
  5. SpatialExperiment

9 Methods for ExperimentSubset

  1. ExperimentSubset constructor
  2. createSubset
  3. setSubsetAssay
  4. getSubsetAssay
  5. subsetSummary
  6. subsetParent
  7. subsetCount
  8. subsetAssayCount
  9. subsetNames
  10. subsetAssayNames
  11. subsetDim
  12. subsetRowData
  13. subsetColData
  14. subsetColnames
  15. subsetRownames
  16. subsetRowData<-
  17. subsetColData<-
  18. subsetColnames<-
  19. subsetRownames<-
  20. show
  21. assay
  22. assay<-
  23. rowData
  24. rowData<-
  25. colData
  26. colData<-
  27. metadata
  28. metadata<-
  29. reducedDim
  30. reducedDim<-
  31. reducedDims
  32. reducedDims<-
  33. reducedDimNames
  34. reducedDimNames<-
  35. altExp
  36. altExp<-
  37. altExps
  38. altExps<-
  39. altExpNames
  40. altExpNames<-
  41. subsetSpatialCoords
  42. subsetSpatialData
  43. subsetSpatialData<-
  44. subsetRowLinks
  45. subsetColLinks
  46. spatialCoords
  47. spatialData
  48. spatialData<-
  49. rowLinks
  50. colLinks

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 from SubsetSummarizedExperiment, SubsetRangedSummarizedExperiment or SubsetSingleCellExperiment which inherit 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. All methods that are compatible with the input object class are compatible with the ExperimentSubset objects as well.

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 4.1.0 beta (2021-05-03 r80259)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.14-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.14-bioc/R/lib/libRlapack.so
## 
## 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       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] ExperimentSubset_1.3.0         TreeSummarizedExperiment_2.1.0
##  [3] Biostrings_2.61.0              XVector_0.33.0                
##  [5] SpatialExperiment_1.3.0        SingleCellExperiment_1.15.0   
##  [7] SummarizedExperiment_1.23.0    Biobase_2.53.0                
##  [9] GenomicRanges_1.45.0           GenomeInfoDb_1.29.0           
## [11] IRanges_2.27.0                 S4Vectors_0.31.0              
## [13] BiocGenerics_0.39.0            MatrixGenerics_1.5.0          
## [15] matrixStats_0.58.0             BiocStyle_2.21.0              
## 
## loaded via a namespace (and not attached):
##  [1] sass_0.4.0                edgeR_3.35.0             
##  [3] jsonlite_1.7.2            DelayedMatrixStats_1.15.0
##  [5] scuttle_1.3.0             R.utils_2.10.1           
##  [7] bslib_0.2.5.1             assertthat_0.2.1         
##  [9] BiocManager_1.30.15       dqrng_0.3.0              
## [11] GenomeInfoDbData_1.2.6    yaml_2.2.1               
## [13] pillar_1.6.1              lattice_0.20-44          
## [15] glue_1.4.2                limma_3.49.0             
## [17] beachmat_2.9.0            digest_0.6.27            
## [19] htmltools_0.5.1.1         Matrix_1.3-3             
## [21] R.oo_1.24.0               pkgconfig_2.0.3          
## [23] magick_2.7.2              bookdown_0.22            
## [25] zlibbioc_1.39.0           purrr_0.3.4              
## [27] HDF5Array_1.21.0          BiocParallel_1.27.0      
## [29] tibble_3.1.2              generics_0.1.0           
## [31] ellipsis_0.3.2            magrittr_2.0.1           
## [33] crayon_1.4.1              evaluate_0.14            
## [35] R.methodsS3_1.8.1         fansi_0.4.2              
## [37] nlme_3.1-152              tools_4.1.0              
## [39] lifecycle_1.0.0           stringr_1.4.0            
## [41] Rhdf5lib_1.15.0           DropletUtils_1.13.0      
## [43] locfit_1.5-9.4            DelayedArray_0.19.0      
## [45] compiler_4.1.0            jquerylib_0.1.4          
## [47] rlang_0.4.11              rhdf5_2.37.0             
## [49] grid_4.1.0                RCurl_1.98-1.3           
## [51] rhdf5filters_1.5.0        rjson_0.2.20             
## [53] bitops_1.0-7              rmarkdown_2.8            
## [55] DBI_1.1.1                 R6_2.5.0                 
## [57] knitr_1.33                dplyr_1.0.6              
## [59] utf8_1.2.1                ape_5.5                  
## [61] stringi_1.6.2             Rcpp_1.0.6               
## [63] vctrs_0.3.8               tidyselect_1.1.1         
## [65] xfun_0.23                 sparseMatrixStats_1.5.0