slingshot {slingshot} | R Documentation

## Perform lineage inference with Slingshot

### Description

Perform lineage inference with Slingshot

Given a reduced-dimensional data matrix `n` by `p` and a vector of cluster labels (or matrix of soft cluster assignments, potentially including a `-1` label for "unclustered"), this function performs lineage inference using a cluster-based minimum spanning tree and constructing simulatenous principal curves for branching paths through the tree.

This wrapper function performs lineage inference in two steps: (1) identify lineage structure with a cluster-based minimum spanning tree with the `getLineages` function and (2) construct smooth representations of each lineage using simultaneous principal curves from the function `getCurves`.

### Usage

    slingshot(data, clusterLabels, ...)

    ## S4 method for signature 'matrix,character'
    slingshot(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL,
      lineages = list(),
      shrink = TRUE,
      extend = "y",
      reweight = TRUE,
      reassign = TRUE,
      thresh = 0.001,
      maxit = 15,
      stretch = 2,
      approx_points = FALSE,
      smoother = "smooth.spline",
      shrink.method = "cosine",
      allow.breaks = TRUE,
      ...
    )

    ## S4 method for signature 'matrix,matrix'
    slingshot(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL,
      lineages = list(),
      shrink = TRUE,
      extend = "y",
      reweight = TRUE,
      reassign = TRUE,
      thresh = 0.001,
      maxit = 15,
      stretch = 2,
      approx_points = FALSE,
      smoother = "smooth.spline",
      shrink.method = "cosine",
      allow.breaks = TRUE,
      ...
    )

    ## S4 method for signature 'SlingshotDataSet,ANY'
    slingshot(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL,
      lineages = list(),
      shrink = TRUE,
      extend = "y",
      reweight = TRUE,
      reassign = TRUE,
      thresh = 0.001,
      maxit = 15,
      stretch = 2,
      approx_points = FALSE,
      smoother = "smooth.spline",
      shrink.method = "cosine",
      allow.breaks = TRUE,
      ...
    )

    ## S4 method for signature 'data.frame,ANY'
    slingshot(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL,
      lineages = list(),
      shrink = TRUE,
      extend = "y",
      reweight = TRUE,
      reassign = TRUE,
      thresh = 0.001,
      maxit = 15,
      stretch = 2,
      approx_points = FALSE,
      smoother = "smooth.spline",
      shrink.method = "cosine",
      allow.breaks = TRUE,
      ...
    )

    ## S4 method for signature 'matrix,numeric'
    slingshot(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL,
      lineages = list(),
      shrink = TRUE,
      extend = "y",
      reweight = TRUE,
      reassign = TRUE,
      thresh = 0.001,
      maxit = 15,
      stretch = 2,
      approx_points = FALSE,
      smoother = "smooth.spline",
      shrink.method = "cosine",
      allow.breaks = TRUE,
      ...
    )

    ## S4 method for signature 'matrix,factor'
    slingshot(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL,
      lineages = list(),
      shrink = TRUE,
      extend = "y",
      reweight = TRUE,
      reassign = TRUE,
      thresh = 0.001,
      maxit = 15,
      stretch = 2,
      approx_points = FALSE,
      smoother = "smooth.spline",
      shrink.method = "cosine",
      allow.breaks = TRUE,
      ...
    )

    ## S4 method for signature 'matrix,ANY'
    slingshot(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL,
      lineages = list(),
      shrink = TRUE,
      extend = "y",
      reweight = TRUE,
      reassign = TRUE,
      thresh = 0.001,
      maxit = 15,
      stretch = 2,
      approx_points = FALSE,
      smoother = "smooth.spline",
      shrink.method = "cosine",
      allow.breaks = TRUE,
      ...
    )

    ## S4 method for signature 'ClusterExperiment,ANY'
    slingshot(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL,
      lineages = list(),
      shrink = TRUE,
      extend = "y",
      reweight = TRUE,
      reassign = TRUE,
      thresh = 0.001,
      maxit = 15,
      stretch = 2,
      approx_points = FALSE,
      smoother = "smooth.spline",
      shrink.method = "cosine",
      allow.breaks = TRUE,
      ...
    )

    ## S4 method for signature 'SingleCellExperiment,ANY'
    slingshot(
      data,
      clusterLabels,
      reducedDim = NULL,
      start.clus = NULL,
      end.clus = NULL,
      dist.fun = NULL,
      omega = NULL,
      lineages = list(),
      shrink = TRUE,
      extend = "y",
      reweight = TRUE,
      reassign = TRUE,
      thresh = 0.001,
      maxit = 15,
      stretch = 2,
      approx_points = FALSE,
      smoother = "smooth.spline",
      shrink.method = "cosine",
      allow.breaks = TRUE,
      ...
    )

### 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 parameters to pass to scatter plot smoothing function, `smoother`.
`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) of origin. Lineages will be represented by paths coming out of this cluster.
`end.clus` | (optional) character, indicates the cluster(s) which will be forced leaf nodes. This introduces a constraint on the MST algorithm.
`dist.fun` | (optional) function, method for calculating distances between clusters. Must take two matrices as input, corresponding to subsets of `reducedDim`. 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`.
`lineages` | list generated by `getLineages`, denotes lineages as ordered sets of clusters and contains the `K x K` connectivity matrix constructed on the clusters by `getLineages`.
`shrink` | logical or numeric between 0 and 1, determines whether and how much to shrink branching lineages toward their average prior to the split.
`extend` | character, how to handle root and leaf clusters of lineages when constructing the initial, piece-wise linear curve. Accepted values are `'y'` (default), `'n'`, and `'pc1'`. See 'Details' for more.
`reweight` | logical, whether to allow cells shared between lineages to be reweighted during curve-fitting. If `TRUE`, cells shared between lineages will be iteratively reweighted based on the quantiles of their projection distances to each curve. See 'Details' for more.
`reassign` | logical, whether to reassign cells to lineages at each iteration. If `TRUE`, cells will be added to a lineage when their projection distance to the curve is less than the median distance for all cells currently assigned to the lineage. Additionally, shared cells will be removed from a lineage if their projection distance to the curve is above the 90th percentile and their weight along the curve is less than `0.1`.
`thresh` | numeric, determines the convergence criterion. Percent change in the total distance from cells to their projections along curves must be less than `thresh`. Default is `0.001`, similar to `principal_curve`.
`maxit` | numeric, maximum number of iterations, see `principal_curve`.
`stretch` | numeric factor by which curves can be extrapolated beyond endpoints. Default is `2`, see `principal_curve`.
`approx_points` | logical or numeric, whether curves should be approximated by a fixed number of points. If `FALSE`, no approximation will be performed and curves will contain as many points as the input data. If numeric, curves will be approximated by this number of points; preferably about 100 (see `principal_curve`).
`smoother,` | choice of scatter plot smoother. Same as `principal_curve`, but `"lowess"` option is replaced with `"loess"` for additional flexibility.
`shrink.method` | character denoting how to determine the appropriate amount of shrinkage for a branching lineage. Accepted values are the same as for `kernel` in `density` (default is `"cosine"`), as well as `"tricube"` and `"density"`. See 'Details' for more.
`allow.breaks` | logical, determines whether curves that branch very close to the origin should be allowed to have different starting points.

### 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.

When there is only a single lineage, the curve-fitting algorithm is nearly identical to that of `principal_curve`. When there are multiple lineages and `shrink == TRUE`, an additional step is added to the iterative procedure, forcing curves to be similar in the neighborhood of shared points (ie., before they branch).

The `extend` argument determines how to construct the piece-wise linear curve used to initiate the recursive algorithm. The initial curve is always based on the lines between cluster centers and if `extend = 'n'`, this curve will terminate at the center of the endpoint clusters. Setting `extend = 'y'` will allow the first and last segments to extend beyond the cluster center to the orthogonal projection of the furthest point. Setting `extend = 'pc1'` is similar to `'y'`, but uses the first principal component of the cluster to determine the direction of the curve beyond the cluster center. These options typically have little to no impact on the final curve, but can occasionally help with stability issues.

When `shink == TRUE`, we compute a shrinkage curve, w_l(t), for each lineage, a non-increasing function of pseudotime that determines how much that lineage should be shrunk toward a shared average curve. We set w_l(0) = 1, so that the curves will perfectly overlap the average curve at pseudotime `0`. The weighting curve decreases from `1` to `0` over the non-outlying pseudotime values of shared cells (where outliers are defined by the `1.5*IQR` rule). The exact shape of the curve in this region is controlled by `shrink.method`, and can follow the shape of any standard kernel function's cumulative density curve (or more precisely, survival curve, since we require a decreasing function). Different choices of `shrink.method` seem to have little impact on the final curves, in most cases.

When `reweight = TRUE`, weights for shared cells are based on the quantiles of their projection distances onto each curve. The distances are ranked and converted into quantiles between `0` and `1`, which are then transformed by `1 - q^2`. Each cell's weight along a given lineage is the ratio of this value to the maximum value for this cell across all lineages.

### Value

An object of class `SlingshotDataSet` containing the arguments provided to `slingshot` as well as the following output:

- `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`Additional parameters used for lineage inference or fitting simultaneous principal curves. This may include the elements `start.given` and `end.given`, logical values indicating whether the starting and ending clusters were specified a priori. Additionally, this will always include `dist`, the pairwise cluster distance matrix.

- curvesA list of `principal_curve` objects.

### References

Hastie, T., and Stuetzle, W. (1989). "Principal Curves." Journal of the American Statistical Association, 84:502–516.

### Examples

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

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

[Package slingshot version 1.5.1 Index]