# An Overview of the S4Vectors package

# Introduction

The S4Vectors package provides a framework for representing vector-like and list-like objects as S4 objects. It defines two central virtual classes, Vector and List, and a set of generic functions that extend the semantic of ordinary vectors and lists in R. Package developers can easily implement vector-like or list-like objects as Vector and/or List derivatives. A few low-level Vector and List derivatives are implemented in the S4Vectors package itself e.g. Hits, Rle, and DataFrame). Many more are implemented in the IRanges and GenomicRanges infrastructure packages, and in many other Bioconductor packages.

In this vignette, we will rely on simple, illustrative example datasets, rather than large, real-world data, so that each data structure and algorithm can be explained in an intuitive, graphical manner. We expect that packages that apply S4Vectors to a particular problem domain will provide vignettes with relevant, realistic examples.

The S4Vectors package is available at bioconductor.org and can be downloaded via `BiocManager::install`:

    if (!require("BiocManager"))
      install.packages("BiocManager")
    BiocManager::install("S4Vectors")

    library(S4Vectors)

# Vector-like and list-like objects

In the context of the S4Vectors package, a vector-like object is an ordered finite collection of elements. All vector-like objects have three main properties: (1) a notion of length or number of elements, (2) the ability to extract elements to create new vector-like objects, and (3) the ability to be concatenated with one or more vector-like objects to form larger vector-like objects. The main functions for these three operations are `length`, `[`, and `c`. Supporting these operations provide a great deal of power and many vector-like object manipulations can be constructed using them.

Some vector-like objects can also have a list-like semantic, which means that individual elements can be extracted with `[[`.

In S4Vectors and many other Bioconductor packages, vector-like and list-like objects derive from the Vector and List virtual classes, respectively. Note that List is a subclass of Vector. The following subsections describe each in turn.

## Vector-like objects

As a first example of vector-like objects, we’ll look at Rle objects. In R, atomic sequences are typically stored in atomic vectors. But there are times when these object become too large to manage in memory. When there are lots of consecutive repeats in the sequence, the data can be compressed and managed in memory through a run-length encoding where a data value is paired with a run length. For example, the sequence {1, 1, 1, 2, 3, 3} can be represented as values = {1, 2, 3}, run lengths = {3, 1, 2}.

The Rle class defined in the S4Vectors package is used to represent a run-length encoded (compressed) sequence of logical, integer, numeric, complex, character, raw, or factor values. Note that the Rle class extends the Vector virtual class:

    showClass("Rle")

    ## Class "Rle" [package "S4Vectors"]
    ##
    ## Slots:
    ##
    ## Name:             values           lengths
    ## Class:  vector_OR_factor  integer_OR_LLint
    ##
    ## Name:    elementMetadata          metadata
    ## Class: DataFrame_OR_NULL              list
    ##
    ## Extends:
    ## Class "Vector", directly
    ## Class "Annotated", by class "Vector", distance 2
    ## Class "vector_OR_Vector", by class "Vector", distance 2

One way to construct Rle objects is through the Rle constructor function:

    set.seed(0)
    lambda <- c(rep(0.001, 4500), seq(0.001, 10, length=500),
                seq(10, 0.001, length=500))
    xVector <- rpois(1e7, lambda)
    yVector <- rpois(1e7, lambda[c(251:length(lambda), 1:250)])
    xRle <- Rle(xVector)
    yRle <- Rle(yVector)

Rle objects are vector-like objects:

    length(xRle)

    ## [1] 10000000

    xRle[1]

    ## integer-Rle of length 1 with 1 run
    ##   Lengths: 1
    ##   Values : 0

    zRle <- c(xRle, yRle)

### Subsetting a vector-like object

As with ordinary R atomic vectors, it is often necessary to subset one sequence from another. When this subsetting does not duplicate or reorder the elements being extracted, the result is called a subsequence. In general, the `[` function can be used to construct a new sequence or extract a subsequence, but its interface is often inconvenient and not amenable to optimization. To compensate for this, the S4Vectors package supports seven additional functions for sequence extraction:

1.`window` - Extracts a subsequence over a specified region.

2.`subset` - Extracts the subsequence specified by a logical vector.

3.`head` - Extracts a consecutive subsequence containing the first n elements.

4.`tail` - Extracts a consecutive subsequence containing the last n elements.

5.`rev` - Creates a new sequence with the elements in the reverse order.

6.`rep` - Creates a new sequence by repeating sequence elements.

The following code illustrates how these functions are used on an Rle vector:

    xSnippet <- window(xRle, 4751, 4760)
    xSnippet

    ## integer-Rle of length 10 with 9 runs
    ##   Lengths: 1 1 1 1 1 1 1 1 2
    ##   Values : 4 6 5 4 6 2 6 7 5

    head(xSnippet)

    ## integer-Rle of length 6 with 6 runs
    ##   Lengths: 1 1 1 1 1 1
    ##   Values : 4 6 5 4 6 2

    tail(xSnippet)

    ## integer-Rle of length 6 with 5 runs
    ##   Lengths: 1 1 1 1 2
    ##   Values : 6 2 6 7 5

    rev(xSnippet)

    ## integer-Rle of length 10 with 9 runs
    ##   Lengths: 2 1 1 1 1 1 1 1 1
    ##   Values : 5 7 6 2 6 4 5 6 4

    rep(xSnippet, 2)

    ## integer-Rle of length 20 with 18 runs
    ##   Lengths: 1 1 1 1 1 1 1 1 2 1 1 1 1 1 1 1 1 2
    ##   Values : 4 6 5 4 6 2 6 7 5 4 6 5 4 6 2 6 7 5

    subset(xSnippet, xSnippet >= 5L)

    ## integer-Rle of length 7 with 5 runs
    ##   Lengths: 1 1 2 1 2
    ##   Values : 6 5 6 7 5

### Concatenating vector-like objects

The S4Vectors package uses two generic functions, `c` and `append`, for concatenating two Vector derivatives. The methods for Vector objects follow the definition that these two functions are given the base package.

    c(xSnippet, rev(xSnippet))

    ## integer-Rle of length 20 with 17 runs
    ##   Lengths: 1 1 1 1 1 1 1 1 4 1 1 1 1 1 1 1 1
    ##   Values : 4 6 5 4 6 2 6 7 5 7 6 2 6 4 5 6 4

    append(xSnippet, xSnippet, after=3)

    ## integer-Rle of length 20 with 18 runs
    ##   Lengths: 1 1 1 1 1 1 1 1 1 1 1 2 1 1 1 1 1 2
    ##   Values : 4 6 5 4 6 5 4 6 2 6 7 5 4 6 2 6 7 5

### Looping over subsequences of vector-like objects

In R, `for` looping can be an expensive operation. To compensate for this, the S4Vectors package provides `aggregate` and `shiftApply` methods (`shiftApply` is a new generic function defined in S4Vectors to perform calculations over subsequences of vector-like objects.

The `aggregate` function combines sequence extraction functionality of the `window` function with looping capabilities of the `sapply` function. For example, here is some code to compute medians across a moving window of width 3 using the function `aggregate`:

    xSnippet

    ## integer-Rle of length 10 with 9 runs
    ##   Lengths: 1 1 1 1 1 1 1 1 2
    ##   Values : 4 6 5 4 6 2 6 7 5

    aggregate(xSnippet, start=1:8, width=3, FUN=median)

    ## [1] 5 5 5 4 6 6 6 5

The `shiftApply` function is a looping operation involving two vector-like objects whose elements are lined up via a positional shift operation. For example, the elements of `xRle` and `yRle` were simulated from Poisson distributions with the mean of element i from `yRle` being equivalent to the mean of element i + 250 from `xRle`. If we did not know the size of the shift, we could estimate it by finding the shift that maximizes the correlation between `xRle` and `yRle`.

    cor(xRle, yRle)

    ## [1] 0.5739224

    shifts <- seq(235, 265, by=3)
    corrs  <- shiftApply(shifts, yRle, xRle, FUN=cor)

    plot(shifts, corrs)

Correlation between `xRle` and `yRle` for various shifts

The result is shown in Fig.@ref(fig:figshiftcorrs)

### More on Rle objects

When there are lots of consecutive repeats, the memory savings through an RLE can be quite dramatic. For example, the `xRle` object occupies less than one third of the space of the original `xVector` object, while storing the same information:

    as.vector(object.size(xRle) / object.size(xVector))

    ## [1] 0.3020726

    identical(as.vector(xRle), xVector)

    ## [1] TRUE

The functions `runValue` and `runLength` extract the run values and run lengths from an Rle object respectively:

    head(runValue(xRle))

    ## [1] 0 1 0 1 0 1

    head(runLength(xRle))

    ## [1]  780    1  208    1 1599    1

The Rle class supports many of the basic methods associated with R atomic vectors including the Ops, Math, Math2, Summary, and Complex group generics. Here is a example of manipulating Rle objects using methods from the Ops group:

    xRle > 0

    ## logical-Rle of length 10000000 with 197127 runs
    ##   Lengths:   780     1   208     1 ...    91     1   927
    ##   Values : FALSE  TRUE FALSE  TRUE ... FALSE  TRUE FALSE

    xRle + yRle

    ## integer-Rle of length 10000000 with 1957707 runs
    ##   Lengths: 780   1 208   1  13   1 ...  91   1 507   1 419
    ##   Values :   0   1   0   1   0   1 ...   0   1   0   1   0

    xRle > 0 | yRle > 0

    ## logical-Rle of length 10000000 with 210711 runs
    ##   Lengths:   780     1   208     1 ...   507     1   419
    ##   Values : FALSE  TRUE FALSE  TRUE ... FALSE  TRUE FALSE

Here are some from the Summary group:

    range(xRle)

    ## [1]  0 26

    sum(xRle > 0 | yRle > 0)

    ## [1] 2105185

And here is one from the Math group:

    log1p(xRle)

    ## numeric-Rle of length 10000000 with 1510219 runs
    ##   Lengths:      780        1      208 ...        1      927
    ##   Values : 0.000000 0.693147 0.000000 ... 0.693147 0.000000

As with atomic vectors, the `cor` and `shiftApply` functions operate on Rle objects:

    cor(xRle, yRle)

    ## [1] 0.5739224

    shiftApply(249:251, yRle, xRle,
               FUN=function(x, y) {var(x, y) / (sd(x) * sd(y))})

    ## [1] 0.8519138 0.8517324 0.8517725

For more information on the methods supported by the Rle class, consult the `Rle` man page.

## List-like objects

Just as with ordinary R List objects, List-derived objects support `[[` for element extraction, `c` for concatenating, and `lapply`/`sapply` for looping. `lapply` and `sapply` are familiar to many R users since they are the standard functions for looping over the elements of an R list object.

In addition, the S4Vectors package introduces the `endoapply` function to perform an endomorphism equivalent to `lapply`, i.e. it returns a List derivative of the same class as the input rather than a list object.

An example of List derivative is the DataFrame class:

    showClass("DataFrame")

    ## Virtual Class "DataFrame" [package "S4Vectors"]
    ##
    ## Slots:
    ##
    ## Name:        elementType   elementMetadata
    ## Class:         character DataFrame_OR_NULL
    ##
    ## Name:           metadata
    ## Class:              list
    ##
    ## Extends:
    ## Class "RectangularData", directly
    ## Class "List", directly
    ## Class "DataFrame_OR_NULL", directly
    ## Class "Vector", by class "List", distance 2
    ## Class "list_OR_List", by class "List", distance 2
    ## Class "Annotated", by class "List", distance 3
    ## Class "vector_OR_Vector", by class "List", distance 3
    ##
    ## Known Subclasses: "DFrame"

One way to construct DataFrame objects is through the DataFrame constructor function:

    df <- DataFrame(x=xRle, y=yRle)
    sapply(df, class)

    ##     x     y
    ## "Rle" "Rle"

    sapply(df, summary)

    ##                  x          y
    ## Min.     0.0000000  0.0000000
    ## 1st Qu.  0.0000000  0.0000000
    ## Median   0.0000000  0.0000000
    ## Mean     0.9090338  0.9096009
    ## 3rd Qu.  0.0000000  0.0000000
    ## Max.    26.0000000 27.0000000

    sapply(as.data.frame(df), summary)

    ##                  x          y
    ## Min.     0.0000000  0.0000000
    ## 1st Qu.  0.0000000  0.0000000
    ## Median   0.0000000  0.0000000
    ## Mean     0.9090338  0.9096009
    ## 3rd Qu.  0.0000000  0.0000000
    ## Max.    26.0000000 27.0000000

    endoapply(df, `+`, 0.5)

    ## DataFrame with 10000000 rows and 2 columns
    ##              x     y
    ##          <Rle> <Rle>
    ## 1          0.5   0.5
    ## 2          0.5   0.5
    ## 3          0.5   0.5
    ## 4          0.5   0.5
    ## 5          0.5   0.5
    ## ...        ...   ...
    ## 9999996    0.5   0.5
    ## 9999997    0.5   0.5
    ## 9999998    0.5   0.5
    ## 9999999    0.5   0.5
    ## 10000000   0.5   0.5

For more information on DataFrame objects, consult the DataFrame man page.

See the “An Overview of the IRanges package’’ vignette in the IRanges package for many more examples of List derivatives.

# Vector Annotations

Often when one has a collection of objects, there is a need to attach metadata that describes the collection in some way. Two kinds of metadata can be attached to a Vector object:

1. Metadata about the object as a whole: this metadata is accessed via the `metadata` accessor and is represented as an ordinary list;
2. Metadata about the individual elements of the object: this metadata is accessed via the `mcols` accessor (`mcols` stands for metadata columns) and is represented as a DataFrame object.This DataFrame object can be thought of as the result of binding together one or several vector-like objects (the metadata columns) of the same length as the Vector object. Each row of the DataFrame object annotates the corresponding element of the Vector object.

# Session Information

Here is the output of `sessionInfo()` on the system on which this document was compiled:

    ## R version 4.6.1 (2026-06-24)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 26.04 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-r0.3.32.so;  LAPACK version 3.12.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: Etc/UTC
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils
    ## [6] datasets  methods   base
    ##
    ## other attached packages:
    ##  [1] graph_1.91.0                ShortRead_1.71.1
    ##  [3] GenomicAlignments_1.49.0    SummarizedExperiment_1.43.0
    ##  [5] Biobase_2.73.1              MatrixGenerics_1.25.0
    ##  [7] matrixStats_1.5.0           Rsamtools_2.29.0
    ##  [9] GenomicRanges_1.65.0        Biostrings_2.81.5
    ## [11] Seqinfo_1.3.0               XVector_0.53.0
    ## [13] BiocParallel_1.47.0         IRanges_2.47.2
    ## [15] S4Vectors_0.51.5            BiocGenerics_0.59.9
    ## [17] generics_0.1.4              Matrix_1.7-5
    ## [19] BiocStyle_2.41.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] sass_0.4.10         SparseArray_1.13.2
    ##  [3] bitops_1.0-9        jpeg_0.1-11
    ##  [5] lattice_0.22-9      digest_0.6.39
    ##  [7] evaluate_1.0.5      grid_4.6.1
    ##  [9] RColorBrewer_1.1-3  fastmap_1.2.0
    ## [11] jsonlite_2.0.0      cigarillo_1.3.0
    ## [13] BiocManager_1.30.27 codetools_0.2-20
    ## [15] jquerylib_0.1.4     abind_1.4-8
    ## [17] cli_3.6.6           rlang_1.3.0
    ## [19] crayon_1.5.3        cachem_1.1.0
    ## [21] DelayedArray_0.39.3 yaml_2.3.12
    ## [23] S4Arrays_1.13.0     tools_4.6.1
    ## [25] parallel_4.6.1      deldir_2.0-4
    ## [27] interp_1.1-6        hwriter_1.3.2.1
    ## [29] buildtools_1.0.0    R6_2.6.1
    ## [31] png_0.1-9           lifecycle_1.0.5
    ## [33] pwalign_1.9.1       bslib_0.11.0
    ## [35] Rcpp_1.1.2          xfun_0.59
    ## [37] sys_3.4.3           knitr_1.51
    ## [39] latticeExtra_0.6-31 htmltools_0.5.9
    ## [41] rmarkdown_2.31      maketools_1.3.2
    ## [43] compiler_4.6.1