SparseArray 1.5.25
SparseArray is an infrastructure package that enables high-performance sparse data representation and manipulation in R. The workhorse of the package is an array-like container that allows efficient in-memory representation of multidimensional sparse data in R.
Use BiocManager::install() to install the SparseArray
package:
if (!require("BiocManager", quietly=TRUE))
install.packages("BiocManager")
BiocManager::install("SparseArray")Load the package:
library(SparseArray)The package defines the SparseArray virtual class and two concrete subclasses: COO_SparseArray and SVT_SparseArray.
Each subclass uses its own internal representation of the nonzero multidimensional data: the “COO layout” and the “SVT layout”, respectively.
Note that the SparseArray virtual class makes no assumption about the internal representation of the nonzero data, so it could easily be extended by other S4 classes that use a different layout for the nonzero data.
This vignette focuses on the SVT_SparseArray container, which is the most memory-efficient and feature-complete of the two SparseArray subclasses. The COO_SparseArray class is only provided to support some rare use-cases. In other words, using SVT_SparseArray objects is almost always preferred over using COO_SparseArray objects.
The SVT_SparseArray container provides an efficient representation of the nonzero multidimensional data via a novel layout called the “SVT layout”.
Note that SVT_SparseArray objects mimic as much as possible the behavior of ordinary matrix or array objects in base R. In particular, they suppport most of the “standard matrix and array API” defined in base R and in the matrixStats package from CRAN.
SVT_SparseArray objects can be constructed in many ways. A common way is to start with an empty object and to subassign nonzero values to it:
svt1 <- SVT_SparseArray(dim=c(6, 4))
svt1[c(1:2, 8, 10, 15:17, 24)] <- (1:8)*10L
svt1## <6 x 4 SparseMatrix> of type "integer" [nzcount=8 (33%)]:
## [,1] [,2] [,3] [,4]
## [1,] 10 0 0 0
## [2,] 20 30 0 0
## [3,] 0 0 50 0
## [4,] 0 40 60 0
## [5,] 0 0 70 0
## [6,] 0 0 0 80svt2 <- SVT_SparseArray(dim=5:3)
svt2[c(1:2, 8, 10, 15:17, 20, 24, 40, 56:60)] <- (1:15)*10L
svt2## <5 x 4 x 3 SparseArray> of type "integer" [nzcount=15 (25%)]:
## ,,1
## [,1] [,2] [,3] [,4]
## [1,] 10 0 0 60
## [2,] 20 0 0 70
## [3,] 0 30 0 0
## [4,] 0 0 0 0
## [5,] 0 40 50 80
##
## ,,2
## [,1] [,2] [,3] [,4]
## [1,] 0 0 0 0
## [2,] 0 0 0 0
## [3,] 0 0 0 0
## [4,] 90 0 0 0
## [5,] 0 0 0 100
##
## ,,3
## [,1] [,2] [,3] [,4]
## [1,] 0 0 0 110
## [2,] 0 0 0 120
## [3,] 0 0 0 130
## [4,] 0 0 0 140
## [5,] 0 0 0 150Another way is to coerce a matrix- or array-like object to SVT_SparseArray:
# Coerce a dgCMatrix object to SVT_SparseArray:
dgcm <- Matrix::rsparsematrix(12, 5, density=0.15)
svt3 <- as(dgcm, "SVT_SparseArray")
# Coerce a TENxMatrix object to SVT_SparseArray:
suppressMessages(library(HDF5Array))
M <- writeTENxMatrix(svt3)
svt3b <- as(M, "SVT_SparseArray")
# Sanity check:
stopifnot(identical(svt3, svt3b))Alternatively, these coercions can be done by simply passing the object
to coerce to the SVT_SparseArray() constructor function:
svt3 <- SVT_SparseArray(dgcm) # same as as(dgcm, "SVT_SparseArray")
svt3b <- SVT_SparseArray(M) # same as as(M, "SVT_SparseArray")See ?SVT_SparseArray for more information about the SVT_SparseArray()
constructor function and additional examples.
As mentioned earlier, SVT_SparseArray objects are almost always preferred
over using COO_SparseArray objects. Coercing to SparseArray or using
the SparseArray() constructor function reflects this preference i.e.
in both cases the actual class of the returned SparseArray derivative
will almost always be SVT_SparseArray (or SVT_SparseMatrix). Except
in the rare situation where returning a COO_SparseArray object is a more
natural fit for the input object.
For example coercing the following objects to SparseArray will always produce an SVT_SparseArray object:
# Coerce an ordinary matrix to SparseArray:
a <- array(rpois(80, lambda=0.35), dim=c(5, 8, 2))
class(as(a, "SparseArray")) # SVT_SparseArray## [1] "SVT_SparseArray"
## attr(,"package")
## [1] "SparseArray"# Coerce a dgCMatrix object to SparseArray:
svt3 <- as(dgcm, "SparseArray")
class(svt3) # SVT_SparseArray## [1] "SVT_SparseMatrix"
## attr(,"package")
## [1] "SparseArray"# Coerce a TENxMatrix object to SparseArray:
svt3b <- as(M, "SparseArray")
class(svt3) # SVT_SparseArray## [1] "SVT_SparseMatrix"
## attr(,"package")
## [1] "SparseArray"Also using the SparseArray() constructor function on these objects will
always produce an SVT_SparseArray object:
SparseArray(a) # same as as(a, "SparseArray")## <5 x 8 x 2 SparseArray> of type "integer" [nzcount=20 (25%)]:
## ,,1
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 1 0 1 1 0 0 0 0
## [2,] 0 0 0 0 0 0 0 0
## [3,] 1 1 0 0 2 0 0 1
## [4,] 0 1 0 0 1 0 0 0
## [5,] 0 0 0 0 0 2 0 1
##
## ,,2
## [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
## [1,] 0 1 0 0 0 0 0 0
## [2,] 0 1 1 0 1 0 0 1
## [3,] 1 0 1 0 0 0 0 0
## [4,] 0 0 0 1 0 0 0 0
## [5,] 0 0 0 1 0 0 0 0svt3 <- SparseArray(dgcm) # same as as(dgcm, "SparseArray")
svt3b <- SparseArray(M) # same as as(M, "SparseArray")This is actually the most convenient way to turn an ordinary matrix or array, or a dgCMatrix object, or a TENxMatrix object, into an SVT_SparseArray object.
One situation where as(x, "SparseArray") or SparseArray(x) will return
a COO_SparseArray object is when the input object x is a sparseMatrix
derivative that uses a compressed row-oriented representation ("R"
representation) instead of the more widely used compressed column-oriented
representation ("C" representation):
ngrm <- sparseMatrix(i=c(1, 5, 5, 6), j=c(4, 2, 3, 2), repr="R")
class(ngrm) # ngRMatrix## [1] "ngRMatrix"
## attr(,"package")
## [1] "Matrix"class(SparseArray(ngrm)) # COO_SparseMatrix## [1] "COO_SparseMatrix"
## attr(,"package")
## [1] "SparseArray"One way to enforce the SVT_SparseArray representation is to coerce the result to SVT_SparseArray:
svt <- as(SparseArray(ngrm), "SVT_SparseArray")
class(svt) # SVT_SparseMatrix## [1] "SVT_SparseMatrix"
## attr(,"package")
## [1] "SparseArray"Finally, note that coercing back to ordinary matrix or array (dense representation) is supported, although obviously not a good idea if the SparseArray object is big:
as.array(svt1) # same as as.matrix(svt1)## [,1] [,2] [,3] [,4]
## [1,] 10 0 0 0
## [2,] 20 30 0 0
## [3,] 0 0 50 0
## [4,] 0 40 60 0
## [5,] 0 0 70 0
## [6,] 0 0 0 80as.array(svt2)## , , 1
##
## [,1] [,2] [,3] [,4]
## [1,] 10 0 0 60
## [2,] 20 0 0 70
## [3,] 0 30 0 0
## [4,] 0 0 0 0
## [5,] 0 40 50 80
##
## , , 2
##
## [,1] [,2] [,3] [,4]
## [1,] 0 0 0 0
## [2,] 0 0 0 0
## [3,] 0 0 0 0
## [4,] 90 0 0 0
## [5,] 0 0 0 100
##
## , , 3
##
## [,1] [,2] [,3] [,4]
## [1,] 0 0 0 110
## [2,] 0 0 0 120
## [3,] 0 0 0 130
## [4,] 0 0 0 140
## [5,] 0 0 0 150The standard array accessors are supported:
dim(svt2)## [1] 5 4 3length(svt2)## [1] 60dimnames(svt2) <- list(NULL, letters[1:4], LETTERS[1:3])
svt2## <5 x 4 x 3 SparseArray> of type "integer" [nzcount=15 (25%)]:
## ,,A
## a b c d
## [1,] 10 0 0 60
## [2,] 20 0 0 70
## [3,] 0 30 0 0
## [4,] 0 0 0 0
## [5,] 0 40 50 80
##
## ,,B
## a b c d
## [1,] 0 0 0 0
## [2,] 0 0 0 0
## [3,] 0 0 0 0
## [4,] 90 0 0 0
## [5,] 0 0 0 100
##
## ,,C
## a b c d
## [1,] 0 0 0 110
## [2,] 0 0 0 120
## [3,] 0 0 0 130
## [4,] 0 0 0 140
## [5,] 0 0 0 150Some additional accessors defined in the S4Arrays / SparseArray framework:
type(svt1)## [1] "integer"type(svt1) <- "double"
svt1## <6 x 4 SparseMatrix> of type "double" [nzcount=8 (33%)]:
## [,1] [,2] [,3] [,4]
## [1,] 10 0 0 0
## [2,] 20 30 0 0
## [3,] 0 0 50 0
## [4,] 0 40 60 0
## [5,] 0 0 70 0
## [6,] 0 0 0 80is_sparse(svt1)## [1] TRUEOther accessors/extractors specific to sparse arrays:
## Get the number of nonzero array elements in 'svt1':
nzcount(svt1)## [1] 8## Extract the "linear indices" of the nonzero array elements in 'svt1':
nzwhich(svt1)## [1] 1 2 8 10 15 16 17 24## Extract the "array indices" (a.k.a. "array coordinates") of the
## nonzero array elements in 'svt1':
nzwhich(svt1, arr.ind=TRUE)## [,1] [,2]
## [1,] 1 1
## [2,] 2 1
## [3,] 2 2
## [4,] 4 2
## [5,] 3 3
## [6,] 4 3
## [7,] 5 3
## [8,] 6 4## Extract the values of the nonzero array elements in 'svt1' and return
## them in a vector "parallel" to 'nzwhich(svt1)':
nzvals(svt1)## [1] 10 20 30 40 50 60 70 80sparsity(svt1)## [1] 0.6666667See ?SparseArray for more information and additional examples.
svt2[5:3, , "C"]## <3 x 4 SparseMatrix> of type "integer" [nzcount=3 (25%)]:
## a b c d
## [1,] 0 0 0 150
## [2,] 0 0 0 140
## [3,] 0 0 0 130Like with ordinary arrays in base R, assigning values of type "double" to
an SVT_SparseArray object of type "integer" will automatically change the
type of the object to "double":
type(svt2)## [1] "integer"svt2[5, 1, 3] <- NaN
type(svt2)## [1] "double"See ?SparseArray_subsetting for more information and additional examples.
The following summarization methods are provided at the moment: anyNA(),
any, all, min, max, range, sum, prod, mean, var, sd.
anyNA(svt2)## [1] TRUErange(svt2, na.rm=TRUE)## [1] 0 150mean(svt2, na.rm=TRUE)## [1] 20.33898var(svt2, na.rm=TRUE)## [1] 1717.124See ?SparseArray_summarization for more information and additional examples.
SVT_SparseArray objects support operations from the ‘Ops’, ‘Math’, Math2,
and ‘Complex’ groups, with some restrictions.
See ?S4groupGeneric in the methods package for more
information about these group generics.
signif((svt1^1.5 + svt1) %% 100 - 0.6 * svt1, digits=2)## <6 x 4 SparseMatrix> of type "double" [nzcount=8 (33%)]:
## [,1] [,2] [,3] [,4]
## [1,] 36.0 0.0 0.0 0.0
## [2,] -2.6 76.0 0.0 0.0
## [3,] 0.0 0.0 -26.0 0.0
## [4,] 0.0 69.0 -11.0 0.0
## [5,] 0.0 0.0 14.0 0.0
## [6,] 0.0 0.0 0.0 48.0See ?SparseArray_Ops, ?SparseArray_Math, and ?SparseArray_Complex,
for more information and additional examples.
Two convenience functions are provided for this:
randomSparseArray(c(5, 6, 2), density=0.5)## <5 x 6 x 2 SparseArray> of type "double" [nzcount=30 (50%)]:
## ,,1
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.00 1.20 0.00 0.40 -1.60 1.40
## [2,] 0.00 0.00 -1.20 1.00 -2.00 0.00
## [3,] -0.14 0.00 0.00 0.73 -0.11 0.00
## [4,] 0.00 0.00 0.00 0.00 0.70 0.00
## [5,] 0.00 -1.00 -1.60 0.00 1.60 0.00
##
## ,,2
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0.000 0.000 -1.100 1.700 0.000 0.000
## [2,] 0.046 -0.880 0.000 -0.200 -0.041 0.000
## [3,] 0.230 0.000 0.400 0.000 0.000 0.000
## [4,] -0.650 1.500 0.000 -0.180 0.170 0.000
## [5,] -0.240 -0.790 0.000 -1.900 0.000 -1.600poissonSparseArray(c(5, 6, 2), density=0.5)## <5 x 6 x 2 SparseArray> of type "integer" [nzcount=30 (50%)]:
## ,,1
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 2 1 0 0 0 2
## [2,] 1 0 2 2 1 1
## [3,] 1 0 1 2 0 1
## [4,] 0 0 0 0 1 1
## [5,] 0 2 1 2 0 0
##
## ,,2
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 3 2 1 3 1 0
## [2,] 0 1 1 0 1 0
## [3,] 0 1 0 0 0 1
## [4,] 0 1 0 2 1 0
## [5,] 0 0 0 0 0 0See ?randomSparseArray for more information and additional examples.
SVT_SparseMatrix objects are just two-dimensional SVT_SparseArray objects.
See ?SparseArray for a diagram of the SparseArray class hierarchy.
t(svt1)## <4 x 6 SparseMatrix> of type "double" [nzcount=8 (33%)]:
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 10 20 0 0 0 0
## [2,] 0 30 0 40 0 0
## [3,] 0 0 50 60 70 0
## [4,] 0 0 0 0 0 80Note that multidimensional transposition is supported via aperm():
aperm(svt2)## <3 x 4 x 5 SparseArray> of type "double" [nzcount=16 (27%)]:
## ,,1
## a b c d
## A 10 0 0 60
## B 0 0 0 0
## C 0 0 0 110
##
## ,,2
## a b c d
## A 20 0 0 70
## B 0 0 0 0
## C 0 0 0 120
##
## ,,3
## a b c d
## A 0 30 0 0
## B 0 0 0 0
## C 0 0 0 130
##
## ,,4
## a b c d
## A 0 0 0 0
## B 90 0 0 0
## C 0 0 0 140
##
## ,,5
## a b c d
## A 0 40 50 80
## B 0 0 0 100
## C NaN 0 0 150See ?SparseArray_aperm for more information and additional examples.
Like ordinary matrices in base R, SVT_SparseMatrix objects can be
combined by rows or columns, with rbind() or cbind():
svt4 <- poissonSparseMatrix(6, 2, density=0.5)
cbind(svt1, svt4)## <6 x 6 SparseMatrix> of type "double" [nzcount=13 (36%)]:
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 10 0 0 0 2 0
## [2,] 20 30 0 0 1 0
## [3,] 0 0 50 0 1 0
## [4,] 0 40 60 0 0 1
## [5,] 0 0 70 0 2 0
## [6,] 0 0 0 80 0 0Note that multidimensional objects can be combined along any dimension
with abind():
svt5a <- poissonSparseArray(c(5, 6, 2), density=0.4)
svt5b <- poissonSparseArray(c(5, 6, 5), density=0.2)
svt5c <- poissonSparseArray(c(5, 6, 4), density=0.2)
abind(svt5a, svt5b, svt5c)## <5 x 6 x 11 SparseArray> of type "integer" [nzcount=78 (24%)]:
## ,,1
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 1 1 1 3 1
## [2,] 0 0 0 0 1 0
## [3,] 1 0 0 2 0 0
## [4,] 0 0 1 0 2 0
## [5,] 0 0 0 1 0 1
##
## ...
##
## ,,11
## [,1] [,2] [,3] [,4] [,5] [,6]
## [1,] 0 0 0 1 0 0
## [2,] 1 0 0 0 0 0
## [3,] 0 0 0 0 1 0
## [4,] 0 0 0 1 0 0
## [5,] 1 0 0 0 0 0svt6a <- aperm(svt5a, c(1, 3:2))
svt6b <- aperm(svt5b, c(1, 3:2))
svt6c <- aperm(svt5c, c(1, 3:2))
abind(svt6a, svt6b, svt6c, along=2)## <5 x 11 x 6 SparseArray> of type "integer" [nzcount=78 (24%)]:
## ,,1
## [,1] [,2] [,3] [,4] ... [,8] [,9] [,10] [,11]
## [1,] 0 0 1 0 . 0 0 0 0
## [2,] 0 1 0 0 . 0 0 0 1
## [3,] 1 1 0 0 . 0 0 0 0
## [4,] 0 0 1 0 . 0 0 0 0
## [5,] 0 0 0 0 . 1 1 0 1
##
## ...
##
## ,,6
## [,1] [,2] [,3] [,4] ... [,8] [,9] [,10] [,11]
## [1,] 1 1 0 0 . 0 0 0 0
## [2,] 0 0 0 0 . 0 1 0 0
## [3,] 0 0 2 0 . 0 0 1 0
## [4,] 0 2 0 0 . 1 0 0 0
## [5,] 1 1 0 0 . 0 0 0 0See ?SparseArray_abind for more information and additional examples.
Like ordinary matrices in base R, SVT_SparseMatrix objects can be
multiplied with the %*% operator:
svt7 <- SVT_SparseArray(dim=5:6, dimnames=list(letters[1:5], LETTERS[1:6]))
svt7[c(2, 6, 12:17, 22:30)] <- 101:117
svt7 %*% svt4## [,1] [,2]
## a 102 107
## b 523 108
## c 324 0
## d 327 0
## e 330 0They also support crossprod() and tcrossprod():
crossprod(svt4)## [,1] [,2]
## [1,] 10 0
## [2,] 0 1See ?SparseMatrix_mult for more information and additional examples.
The SparseArray package provides memory-efficient col/row summarization methods for SVT_SparseMatrix objects:
colVars(svt7)## A B C D E F
## 2040.2 2080.8 2185.3 3467.0 2443.3 2.5Note that multidimensional objects are supported:
colVars(svt2)## A B C
## a 80 1620 NaN
## b 380 0 0
## c 500 0 0
## d 1520 2000 250colVars(svt2, dims=2)## A B C
## 732.6316 857.6316 NaNcolAnyNAs(svt2)## A B C
## a FALSE FALSE TRUE
## b FALSE FALSE FALSE
## c FALSE FALSE FALSE
## d FALSE FALSE FALSEcolAnyNAs(svt2, dims=2)## A B C
## FALSE FALSE TRUESee ?matrixStats_methods for more information and additional examples.
rowsum() and colsum()rowsum() and colsum() are supported:
rowsum(svt7, group=c(1:3, 2:1))## A B C D E F
## 1 0 102 106 107 112 230
## 2 101 0 208 108 220 230
## 3 0 0 104 0 110 115colsum(svt7, group=c("A", "B", "A", "B", "B", "A"))## A B
## a 113 209
## b 318 217
## c 219 110
## d 221 111
## e 223 112See ?rowsum_methods for more information and additional examples.
Use writeSparseCSV() to write a sparse matrix to a CSV file:
csv_file <- tempfile()
writeSparseCSV(svt7, csv_file)Use readSparseCSV() to read the file. This will import the data as
an SVT_SparseMatrix object:
readSparseCSV(csv_file)## <5 x 6 SparseMatrix> of type "integer" [nzcount=17 (57%)]:
## A B C D E F
## a 0 102 0 107 0 113
## b 101 0 103 108 109 114
## c 0 0 104 0 110 115
## d 0 0 105 0 111 116
## e 0 0 106 0 112 117See ?readSparseCSV for more information and additional examples.
The nonzero data of a SVT_SparseArray object is stored in a Sparse Vector Tree. This internal data representation is referred to as the “SVT layout”. It is similar to the “CSC layout” (compressed, sparse, column-oriented format) used by CsparseMatrix derivatives from the Matrix package, like dgCMatrix or lgCMatrix objects, but with the following improvements:
The “SVT layout” supports sparse arrays of arbitrary dimensions.
With the “SVT layout”, the sparse data can be of any type.
Whereas CsparseMatrix derivatives only support sparse data of
type "double" or "logical".
The “SVT layout” imposes no limit on the number of nonzero array elements that can be stored. With dgCMatrix/lgCMatrix objects, this number must be < 2^31.
Overall, the “SVT layout” allows more efficient operations on SVT_SparseArray objects.
See ?SVT_SparseArray for more information about the “SVT layout”.
The “1.3 Million Brain Cell Dataset” from 10x Genomics is a sparse 2D
dataset with more than 2^31 nonzero values. The dataset is stored in an
HDF5 file that is available via ExperimentHub (resource id EH1039):
suppressMessages(library(HDF5Array))
suppressMessages(library(ExperimentHub))
hub <- ExperimentHub()
oneM <- TENxMatrix(hub[["EH1039"]], group="mm10")## see ?TENxBrainData and browseVignettes('TENxBrainData') for documentation## loading from cacheoneM## <27998 x 1306127> sparse TENxMatrix object of type "integer":
## AAACCTGAGATAGGAG-1 ... TTTGTCATCTGAAAGA-133
## ENSMUSG00000051951 0 . 0
## ENSMUSG00000089699 0 . 0
## ENSMUSG00000102343 0 . 0
## ENSMUSG00000025900 0 . 0
## ENSMUSG00000109048 0 . 0
## ... . . .
## ENSMUSG00000079808 0 . 0
## ENSMUSG00000095041 1 . 0
## ENSMUSG00000063897 0 . 0
## ENSMUSG00000096730 0 . 0
## ENSMUSG00000095742 0 . 0oneM is a TENxMatrix object. This is a particular kind of sparse
DelayedArray object where the data is on disk in an HDF5 file.
See ?TENxMatrix in the HDF5Array package for more
information about TENxMatrix objects.
Note that the object has more than 2^31 nonzero values:
nzcount(oneM)## [1] 2624828308The standard way to load the data of a TENxMatrix object (or any DelayedArray derivative) from disk to memory is to simply coerce the object to the desired in-memory representation.
For example, to load the data in an SVT_SparseArray object:
# WARNING: This takes a couple of minutes on a modern laptop, and will
# consume about 25Gb of RAM!
svt <- as(oneM, "SVT_SparseArray")To load the data in a dgCMatrix object:
# This will fail because 'oneM' has more than 2^31 nonzero values!
as(oneM, "dgCMatrix")Please consult the individual man pages in the SparseArray
package to learn more about SVT_SparseArray objects and about the
package. A good starting point is the man page for SparseArray
objects: ?SparseArray
sessionInfo()## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.20-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
##
## 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] TENxBrainData_1.25.0 SingleCellExperiment_1.27.2
## [3] SummarizedExperiment_1.35.1 Biobase_2.65.0
## [5] GenomicRanges_1.57.1 GenomeInfoDb_1.41.1
## [7] ExperimentHub_2.13.0 AnnotationHub_3.13.0
## [9] BiocFileCache_2.13.0 dbplyr_2.5.0
## [11] HDF5Array_1.33.5 rhdf5_2.49.0
## [13] DelayedArray_0.31.9 SparseArray_1.5.25
## [15] S4Arrays_1.5.5 IRanges_2.39.2
## [17] abind_1.4-5 S4Vectors_0.43.2
## [19] MatrixGenerics_1.17.0 matrixStats_1.3.0
## [21] BiocGenerics_0.51.0 Matrix_1.7-0
## [23] BiocStyle_2.33.1
##
## loaded via a namespace (and not attached):
## [1] KEGGREST_1.45.1 xfun_0.46 bslib_0.7.0
## [4] lattice_0.22-6 rhdf5filters_1.17.0 vctrs_0.6.5
## [7] tools_4.4.1 generics_0.1.3 curl_5.2.1
## [10] tibble_3.2.1 fansi_1.0.6 AnnotationDbi_1.67.0
## [13] RSQLite_2.3.7 blob_1.2.4 pkgconfig_2.0.3
## [16] GenomeInfoDbData_1.2.12 lifecycle_1.0.4 compiler_4.4.1
## [19] Biostrings_2.73.1 htmltools_0.5.8.1 sass_0.4.9
## [22] yaml_2.3.9 pillar_1.9.0 crayon_1.5.3
## [25] jquerylib_0.1.4 cachem_1.1.0 mime_0.12
## [28] tidyselect_1.2.1 digest_0.6.36 purrr_1.0.2
## [31] dplyr_1.1.4 bookdown_0.40 BiocVersion_3.20.0
## [34] fastmap_1.2.0 grid_4.4.1 cli_3.6.3
## [37] magrittr_2.0.3 utf8_1.2.4 withr_3.0.0
## [40] UCSC.utils_1.1.0 filelock_1.0.3 rappdirs_0.3.3
## [43] bit64_4.0.5 rmarkdown_2.27 XVector_0.45.0
## [46] httr_1.4.7 bit_4.0.5 png_0.1-8
## [49] memoise_2.0.1 evaluate_0.24.0 knitr_1.48
## [52] rlang_1.1.4 glue_1.7.0 DBI_1.2.3
## [55] BiocManager_1.30.23 jsonlite_1.8.8 R6_2.5.1
## [58] Rhdf5lib_1.27.0 zlibbioc_1.51.1