ContactMatrix class {InteractionSet} | R Documentation

## ContactMatrix class and constructors

### Description

The ContactMatrix class contains a matrix where rows and columns represent genomic loci. Each entry of the matrix contains information about the interaction between the loci represented by the corresponding row/column, e.g., contact frequencies. Coordinates of the loci are also contained within this class.

### Usage

    ## S4 method for signature 'ANY,numeric,numeric,GRanges'
    ContactMatrix(matrix, anchor1, anchor2, regions, metadata=list())

    ## S4 method for signature 'ANY,GRanges,GRanges,GenomicRanges_OR_missing'
    ContactMatrix(matrix, anchor1, anchor2, regions, metadata=list())

    ## S4 method for signature 'missing,missing,missing,GenomicRanges_OR_missing'
    ContactMatrix(matrix, anchor1, anchor2, regions, metadata=list())

### Arguments

`matrix` | Any matrix-like object containing interaction data.
`anchor1, anchor2` | Either a pair of numeric vectors containing indices to `regions` or a pair of GRanges objects. In both cases, `anchor1` and `anchor2` should have lengths equal to the number of rows and columns in `matrix`, respectively.
`regions` | A GRanges object containing the coordinates of the interacting regions. This argument is optional for `InteractionSet,ANY,GRanges,GRanges-method`.
`metadata` | A list containing experiment-wide metadata - see `?Annotated` for more details.

### Details

The ContactMatrix class inherits from the `Annotated` class, with several additional slots:

`matrix`:

  A matrix-like object.

`anchor1`:

  An integer vector specifying the index of the first interacting region.

`anchor2`:

  An integer vector specifying the index of the second interacting region.

`regions`:

  A sorted GRanges object containing the coordinates of all interacting regions.

Each entry of `anchor1` corresponds to a row in `matrix`, while each entry of `anchor2` corresponds to a column. Each entry of `matrix` represents an interaction between the corresponding entries in `anchor1` and `anchor2`, i which point to the relevant coordinates in `regions` for each locus.

ContactMatrix objects can be constructed by specifying numeric vectors as `anchor1` and `anchor2` in the `ContactMatrix` function. These vectors will define the regions corresponding to the rows and columns of the matrix. Specifically, each value of the vector acts as an index to specify the relevant coordinates from `regions`. This means that the range of entries must lie within `[1, length(regions)]`.

Alternatively, ContactMatrix objects can be constructed by directly supplying the GRanges of the interacting loci in `ContactMatrix`. If `regions` is not specified, this will be constructed automatically from the two sets of supplied GRanges. If `regions` is supplied, exact matching will be performed to identify the indices in `regions` corresponding to the regions in the supplied GRanges. Missing values are not tolerated and will cause an error to be raised.

Both methods will return an ContactMatrix object containing all of the specified information. Sorting of `regions` is also performed automatically, with re-indexing of all anchor indices to preserve the correct pairings between regions.

### Value

For the constructors, a ContactMatrix object is returned.

### Choosing between matrix classes

The ContactMatrix class provides support for any matrix-like object that implements `dim`, `rbind`, `cbind`, `[` and `t` methods. The choice of class depends on the type of data and the intended application. Some of the common choices are described in more detail here:

- Base matrices are simple to generate and are most efficient for dense data. This is often sufficient for most use cases where small regions of the interaction space are being examined.

- Sparse matrices from the Matrix package are useful for large areas of the interaction space where most entries are zero. This reduces memory usage compared to a dense representation (though conversely, is less efficient for dense data). Note that all numeric values are coerced to double-precision types, which may take up more memory than a direct integer representation. Another issue is how missing values should be interpreted in the sparseMatrix – see `?inflate` for more details.

- Packed symmetric matrices from the Matrix package provide some memory savings for symmetric regions of the interaction space.

- Delayed or HDF5-backed matrices from the DelayedArray and HDF5Array packages allow very large matrices to be represented without loading into memory.

### Author(s)

Aaron Lun

### See Also

`ContactMatrix-access`, `ContactMatrix-subset`, `ContactMatrix-sort`, `Annotated-class`, `Matrix-class`

### Examples

    set.seed(1000)
    N <- 30
    all.starts <- sample(1000, N)
    all.ends <- all.starts + round(runif(N, 5, 20))
    all.regions <- GRanges(rep(c("chrA", "chrB"), c(N-10, 10)),
        IRanges(all.starts, all.ends))

    Nr <- 10
    Nc <- 20
    all.anchor1 <- sample(N, Nr)
    all.anchor2 <- sample(N, Nc)
    counts <- matrix(rpois(Nr*Nc, lambda=10), Nr, Nc)
    x <- ContactMatrix(counts, all.anchor1, all.anchor2, all.regions)

    # Equivalent construction:
    ContactMatrix(counts, all.regions[all.anchor1],
        all.regions[all.anchor2])
    ContactMatrix(counts, all.regions[all.anchor1],
        all.regions[all.anchor2], all.regions)

    # Also works directly with Matrix objects.
    counts2 <- Matrix::Matrix(counts)
    x2 <- ContactMatrix(counts2, all.anchor1, all.anchor2, all.regions)
    counts2 <- as(counts2, "dgCMatrix")
    x2 <- ContactMatrix(counts2, all.anchor1, all.anchor2, all.regions)

[Package InteractionSet version 1.12.0 Index]