# SparseArray objects

# Introduction

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.

# Install and load the package

Like any other Bioconductor package, SparseArray should always be installed with `BiocManager::install()`:

    if (!require("BiocManager", quietly=TRUE))
        install.packages("BiocManager")
    BiocManager::install("SparseArray")

Load the package:

    library(SparseArray)

# The SparseArray virtual class and its two concrete subclasses

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.

# SVT_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.

## Construction

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   80

    svt2 <- 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  150

Another 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.

## SVT_SparseArray vs COO_SparseArray

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=25 (31%)]:
    ## ,,1
    ##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
    ## [1,]    0    0    0    0    0    0    0    0
    ## [2,]    1    0    0    0    1    0    0    0
    ## [3,]    1    0    1    0    0    0    1    1
    ## [4,]    0    0    0    0    1    0    0    0
    ## [5,]    1    2    0    0    0    1    3    0
    ##
    ## ,,2
    ##      [,1] [,2] [,3] [,4] [,5] [,6] [,7] [,8]
    ## [1,]    1    0    1    1    0    0    1    0
    ## [2,]    0    0    0    1    0    0    0    2
    ## [3,]    0    0    1    1    1    1    1    0
    ## [4,]    0    0    0    0    0    0    0    0
    ## [5,]    0    0    0    0    1    1    1    0

    svt3  <- 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   80

    as.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  150

# The SparseArray API

## The core array API

SVT_SparseArray objects support the “core array API” defined in base R:

    dim(svt2)

    ## [1] 5 4 3

    length(svt2)

    ## [1] 60

    dimnames(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   0 150

## type() and is_sparse()

`type()` and `is_sparse()` are generic functions defined in BiocGenerics and S4Arrays, respectively. They extend the “core array API” defined in base R:

    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   80

    is_sparse(svt1)

    ## [1] TRUE

See `?SparseArray` for more information and additional examples.

## is_nonzero() and the nz*() functions

A set of functions is provided for direct manipulation of the nonzero array elements:

    is_nonzero(svt1)

    ## <6 x 4 SparseMatrix> of type "logical" [nzcount=8 (33%)]:
    ##       [,1]  [,2]  [,3]  [,4]
    ## [1,]  TRUE FALSE FALSE FALSE
    ## [2,]  TRUE  TRUE FALSE FALSE
    ## [3,] FALSE FALSE  TRUE FALSE
    ## [4,] FALSE  TRUE  TRUE FALSE
    ## [5,] FALSE FALSE  TRUE FALSE
    ## [6,] FALSE FALSE FALSE  TRUE

    ## 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':
    nzvals(svt1)

    ## [1] 10 20 30 40 50 60 70 80

Note that the vectors produced by `nzwhich()` and `nzvals()` are parallel, that is, they have the same length and the i-th element in one vector corresponds to the i-th element in the other vector.

See `?is_nonzero` for more information and additional examples.

## Subsetting and subassignment

    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   0 130

Like 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.

## Summarization methods (whole array)

The following summarization methods are provided at the moment: `anyNA()`, `any`, `all`, `min`, `max`, `range`, `sum`, `prod`, `mean`, `var`, `sd`.

    anyNA(svt2)

    ## [1] TRUE

    range(svt2, na.rm=TRUE)

    ## [1]   0 150

    mean(svt2, na.rm=TRUE)

    ## [1] 20.33898

    var(svt2, na.rm=TRUE)

    ## [1] 1717.124

See `?SparseArray_summarization` for more information and additional examples.

## Operations from the Arith, Compare, Logic, Math, Math2, and Complex groups

SVT_SparseArray objects support operations from the `Arith`, `Compare`, `Logic`, `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  48.0

See `?SparseArray_Arith`, `?SparseArray_Compare`, `?SparseArray_Logic`, `?SparseArray_Math`, and `?SparseArray_Complex` for more information and additional examples.

# The 2D API

## SVT_SparseMatrix objects

SVT_SparseMatrix objects are just two-dimensional SVT_SparseArray objects. See `?SparseArray` for a diagram of the SparseArray class hierarchy.

## Transposition

    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   80

Note 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   0 150

See `?SparseArray_aperm` for more information and additional examples.

## Combine multidimensional objects along a given dimension

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=15 (42%)]:
    ##      [,1] [,2] [,3] [,4] [,5] [,6]
    ## [1,]   10    0    0    0    1    0
    ## [2,]   20   30    0    0    1    0
    ## [3,]    0    0   50    0    0    1
    ## [4,]    0   40   60    0    1    0
    ## [5,]    0    0   70    0    0    1
    ## [6,]    0    0    0   80    1    1

Note 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=84 (25%)]:
    ## ,,1
    ##      [,1] [,2] [,3] [,4] [,5] [,6]
    ## [1,]    1    0    0    0    1    0
    ## [2,]    0    0    0    0    1    0
    ## [3,]    0    0    1    1    0    1
    ## [4,]    0    0    2    0    1    0
    ## [5,]    0    3    2    0    0    3
    ##
    ## ...
    ##
    ## ,,11
    ##      [,1] [,2] [,3] [,4] [,5] [,6]
    ## [1,]    1    0    0    1    0    0
    ## [2,]    1    1    1    0    0    0
    ## [3,]    2    0    0    0    0    0
    ## [4,]    0    0    0    1    0    0
    ## [5,]    0    1    0    0    0    0

    svt6a <- 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=84 (25%)]:
    ## ,,1
    ##       [,1]  [,2]  [,3]  [,4] ...  [,8]  [,9] [,10] [,11]
    ## [1,]     1     0     0     0   .     1     0     0     1
    ## [2,]     0     0     0     0   .     0     0     1     1
    ## [3,]     0     0     1     1   .     0     0     0     2
    ## [4,]     0     0     0     0   .     0     0     0     0
    ## [5,]     0     0     0     0   .     0     1     0     0
    ##
    ## ...
    ##
    ## ,,6
    ##       [,1]  [,2]  [,3]  [,4] ...  [,8]  [,9] [,10] [,11]
    ## [1,]     0     0     0     1   .     0     0     0     0
    ## [2,]     0     2     0     0   .     0     0     0     0
    ## [3,]     1     0     0     0   .     0     0     0     0
    ## [4,]     0     0     0     1   .     0     0     0     0
    ## [5,]     3     0     0     0   .     1     0     0     0

See `?SparseArray_abind` for more information and additional examples.

## matrixStats methods

The SparseArray package provides memory-efficient col/row summarization methods for SVT_SparseMatrix objects:

    svt7 <- SVT_SparseArray(dim=5:6, dimnames=list(letters[1:5], LETTERS[1:6]))
    svt7[c(2, 6, 12:17, 22:30)] <- 101:117

    colVars(svt7)

    ##      A      B      C      D      E      F
    ## 2040.2 2080.8 2185.3 3467.0 2443.3    2.5

Note 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 250

    colVars(svt2, dims=2)

    ##        A        B        C
    ## 732.6316 857.6316      NaN

    colAnyNAs(svt2)

    ##       A     B     C
    ## a FALSE FALSE  TRUE
    ## b FALSE FALSE FALSE
    ## c FALSE FALSE FALSE
    ## d FALSE FALSE FALSE

    colAnyNAs(svt2, dims=2)

    ##     A     B     C
    ## FALSE FALSE  TRUE

See `?SparseArray_matrixStats` 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   0 110 115

    colsum(svt7, group=c("A", "B", "A", "B", "B", "A"))

    ##     A   B
    ## a 113 209
    ## b 318 217
    ## c 219 110
    ## d 221 111
    ## e 223 112

See `?rowsum_methods` for more information and additional examples.

## Matrix multiplication and cross-product

SVT_SparseMatrix objects support matrix multiplication:

    svt7 %*% svt4

    ##   [,1] [,2]
    ## a  322  113
    ## b  323  326
    ## c  115  329
    ## d  116  332
    ## e  117  335

as well as `crossprod()` and `tcrossprod()`:

    crossprod(svt4)

    ##      [,1] [,2]
    ## [1,]    4    1
    ## [2,]    1    3

See `?SparseMatrix_mult` for more information and additional examples.

# Other operations

## Generate a random SVT_SparseArray object

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,] -1.700  0.000  0.140 -0.420 -1.200  0.000
    ## [2,]  0.000 -0.300 -0.097  0.000  0.000  0.750
    ## [3,] -1.000  0.000 -0.550  0.120  1.500  0.000
    ## [4,]  0.000  0.000  0.000  0.000  0.000 -1.200
    ## [5,]  0.000  0.000 -0.140  0.000  0.000  0.000
    ##
    ## ,,2
    ##        [,1]   [,2]   [,3]   [,4]   [,5]   [,6]
    ## [1,]  1.100  0.610  0.480  0.000  1.000  0.000
    ## [2,]  1.500  0.000  2.100  0.000  0.000  1.700
    ## [3,]  0.000  2.600  0.000  0.000  0.000 -0.600
    ## [4,] -0.190 -1.500  0.680  0.000  0.000 -0.290
    ## [5,]  0.900  0.000  0.000  1.200 -0.390  0.087

    poissonSparseArray(c(5, 6, 2), density=0.5)

    ## <5 x 6 x 2 SparseArray> of type "integer" [nzcount=33 (55%)]:
    ## ,,1
    ##      [,1] [,2] [,3] [,4] [,5] [,6]
    ## [1,]    0    2    0    0    2    0
    ## [2,]    1    0    0    0    0    0
    ## [3,]    1    0    3    2    1    0
    ## [4,]    1    0    1    0    1    1
    ## [5,]    0    0    1    1    1    0
    ##
    ## ,,2
    ##      [,1] [,2] [,3] [,4] [,5] [,6]
    ## [1,]    3    0    1    0    1    1
    ## [2,]    1    2    1    3    0    0
    ## [3,]    0    2    1    2    4    1
    ## [4,]    0    0    1    1    0    1
    ## [5,]    3    0    0    1    0    3

See `?randomSparseArray` for more information and additional examples.

## Read/write a sparse matrix from/to a CSV file

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   0 112 117

See `?readSparseCSV` for more information and additional examples.

# Comparison with dgCMatrix objects

## “SVT layout” vs “CSC layout”

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”.

## Working with a big sparse dataset

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")

    ## TENxBrainData not installed.
    ##   Full functionality, documentation, and loading of data might not be possible without installing

    ## downloading 1 resources

    ## retrieving 1 resource

    ## loading from cache

    oneM

    ## <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   .                    0

`oneM` 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] 2624828308

The 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")

# Learn more

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`

# Session information

    sessionInfo()

    ## R version 4.5.2 (2025-10-31)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 24.04.3 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=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: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] ExperimentHub_3.0.0   AnnotationHub_4.0.0   BiocFileCache_3.0.0
    ##  [4] dbplyr_2.5.1          HDF5Array_1.38.0      h5mread_1.2.1
    ##  [7] rhdf5_2.54.1          DelayedArray_0.36.0   SparseArray_1.10.8
    ## [10] S4Arrays_1.10.1       IRanges_2.44.0        abind_1.4-8
    ## [13] S4Vectors_0.48.0      MatrixGenerics_1.22.0 matrixStats_1.5.0
    ## [16] BiocGenerics_0.56.0   generics_0.1.4        Matrix_1.7-4
    ## [19] BiocStyle_2.38.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] KEGGREST_1.50.0      xfun_0.56            bslib_0.10.0
    ##  [4] httr2_1.2.2          Biobase_2.70.0       lattice_0.22-9
    ##  [7] rhdf5filters_1.22.0  vctrs_0.7.1          tools_4.5.2
    ## [10] curl_7.0.0           tibble_3.3.1         AnnotationDbi_1.72.0
    ## [13] RSQLite_2.4.6        blob_1.3.0           pkgconfig_2.0.3
    ## [16] lifecycle_1.0.5      compiler_4.5.2       Biostrings_2.78.0
    ## [19] Seqinfo_1.0.0        htmltools_0.5.9      sys_3.4.3
    ## [22] buildtools_1.0.0     sass_0.4.10          yaml_2.3.12
    ## [25] pillar_1.11.1        crayon_1.5.3         jquerylib_0.1.4
    ## [28] cachem_1.1.0         tidyselect_1.2.1     digest_0.6.39
    ## [31] purrr_1.2.1          dplyr_1.2.0          BiocVersion_3.22.0
    ## [34] maketools_1.3.2      fastmap_1.2.0        grid_4.5.2
    ## [37] cli_3.6.5            magrittr_2.0.4       withr_3.0.2
    ## [40] filelock_1.0.3       rappdirs_0.3.4       bit64_4.6.0-1
    ## [43] rmarkdown_2.30       XVector_0.50.0       httr_1.4.7
    ## [46] bit_4.6.0            png_0.1-8            memoise_2.0.1
    ## [49] evaluate_1.0.5       knitr_1.51           rlang_1.1.7
    ## [52] glue_1.8.0           DBI_1.2.3            BiocManager_1.30.27
    ## [55] jsonlite_2.0.0       R6_2.6.1             Rhdf5lib_1.32.0