getLineages {slingshot} | R Documentation

## Infer Lineage Structure from Clustered Samples

### Description

Given a reduced-dimension data matrix `n` by `p` and a vector of cluster identities (potentially including -1's for "unclustered"), this function infers a forest structure on the clusters and returns paths through the forest that can be interpreted as lineages.

### Usage

    getLineages(data, clusterLabels, ...)

    ## S4 method for signature 'matrix,matrix'
    getLineages(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL
    )

    ## S4 method for signature 'matrix,character'
    getLineages(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL
    )

    ## S4 method for signature 'matrix,ANY'
    getLineages(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL
    )

    ## S4 method for signature 'SlingshotDataSet,ANY'
    getLineages(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL
    )

    ## S4 method for signature 'data.frame,ANY'
    getLineages(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL
    )

    ## S4 method for signature 'matrix,numeric'
    getLineages(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL
    )

    ## S4 method for signature 'matrix,factor'
    getLineages(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL
    )

    ## S4 method for signature 'SingleCellExperiment,ANY'
    getLineages(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL
    )

### Arguments

`data` | a data object containing the matrix of coordinates to be used for lineage inference. Supported types include `matrix`, `SingleCellExperiment`, and `SlingshotDataSet`.
`clusterLabels` | character, a vector of length `n` denoting cluster labels, optionally including `-1`'s for "unclustered." If `reducedDim` is a `SlingshotDataSet`, cluster labels will be taken from it.
`...` | Additional arguments to specify how lineages are constructed from clusters.
`reducedDim` | (optional) identifier to be used if `reducedDim(data)` contains multiple elements. Otherwise, the first element will be used by default.
`start.clus` | (optional) character, indicates the cluster(s) *from* which lineages will be drawn.
`end.clus` | (optional) character, indicates the cluster(s) which will be forced leaf nodes in their trees.
`dist.fun` | (optional) function, method for calculating distances between clusters. Must take two matrices as input, corresponding to points in reduced-dimensional space. If the minimum cluster size is larger than the number dimensions, the default is to use the joint covariance matrix to find squared distance between cluster centers. If not, the default is to use the diagonal of the joint covariance matrix.
`omega` | (optional) numeric, this granularity parameter determines the distance between every real cluster and the artificial cluster, OMEGA. It is parameterized such that this distance is `omega / 2`, making `omega` the maximum distance between two connected clusters. By default, `omega = Inf`.

### Details

The `connectivity` matrix is learned by fitting a (possibly constrained) minimum-spanning tree on the clusters and the artificial cluster, OMEGA, which is a fixed distance away from every real cluster. This effectively limits the maximum branch length in the MST to twice the chosen distance, meaning that the output may contain multiple trees.

Once the `connectivity` is known, lineages are identified in any tree with at least two clusters. For a given tree, if there is an annotated starting cluster, every possible path out of a starting cluster and ending in a leaf that isn't another starting cluster will be returned. If no starting cluster is annotated, every leaf will be considered as a potential starting cluster and whichever configuration produces the longest average lineage length (in terms of number of clusters included) will be returned.

### Value

An object of class `SlingshotDataSet` containing the arguments provided to `getLineages` as well as the following new elements:

- `lineages` a list of `L` items, where `L` is the number of lineages identified. Each lineage is represented by a character vector with the names of the clusters included in that lineage, in order.

- `connectivity` the inferred cluster connectivity matrix.

- `slingParams$start.given`,`slingParams$end.given` logical values indicating whether the starting and ending clusters were specified a priori.

- `slingParams$dist` the pairwise cluster distance matrix.

### Examples

    data("slingshotExample")
    rd <- slingshotExample$rd
    cl <- slingshotExample$cl
    sds <- getLineages(rd, cl, start.clus = '1')

    plot(rd, col = cl, asp = 1)
    lines(sds, type = 'l', lwd = 3)

[Package slingshot version 1.6.1 Index]