SlingshotDataSet-class: Class 'SlingshotDataSet'
In slingshot: Tools for ordering single-cell sequencing

Description Usage Arguments Details Value Methods (by generic) Slots

The SlingshotDataSet class holds data relevant for performing lineage inference with the slingshot package, primarily a reduced dimensional representation of the data and a set of cluster labels. All slingshot methods can take an object of the class SlingshotDataSet as input and will output the same.

## S4 method for signature 'SlingshotDataSet'
show(object)

## S4 method for signature 'SlingshotDataSet,ANY'
reducedDim(x)

## S4 method for signature 'SlingshotDataSet'
reducedDims(x)

## S4 method for signature 'SlingshotDataSet,ANY,ANY,ANY'
x[i, j]

`object`	a `SlingshotDataSet` object.
`x`	a `SlingshotDataSet` object.
`i`	indices to be applied to rows (cells) of the reduced dimensional matrix and cluster labels.
`j`	indices to be applied to the columns (dimensions) of the reduced dimensional matrix.

Warning: this will remove any existing lineages or curves from the SlingshotDataSet object.

The accessor functions reducedDim, clusterLabels, lineages, adjacency, curves, and slingParams return the corresponding elements of a SlingshotDataSet. The functions pseudotime and curveWeights extract useful output elements of a SlingshotDataSet, provided that curves have already been fit with either slingshot or getCurves.

show: a short summary of SlingshotDataSet object.
reducedDim: returns the matrix representing the reduced dimensional dataset.
reducedDims: returns the matrix representing the reduced dimensional dataset.
[: Subset dataset and cluster labels.

reducedDim

matrix. An n by p numeric matrix or data frame giving the coordinates of the cells in a reduced dimensionality space.

clusterLabels

matrix or character. An n by K matrix of weights indicating each cell's cluster assignment or a character vector of cluster assignments, which will be converted into a binary matrix.

lineages

list. A list with each element a character vector of cluster names representing a lineage as an ordered set of clusters.

adjacency

matrix. A binary matrix describing the adjacency between clusters induced by the minimum spanning tree.

curves

list. A list of principal_curve objects produced by getCurves.

slingParams

list. Additional parameters used by Slingshot. These may specify how the minimum spanning tree on clusters was constructed:

start.cluscharacter. The label of the root cluster.
end.cluscharacter. Vector of cluster labels indicating the terminal clusters.
start.givenlogical. A logical value indicating whether the initial state was pre-specified.
end.givenlogical. A vector of logical values indicating whether each terminal state was pre-specified
distmatrix. A numeric matrix of pairwise cluster distances.

They may also specify how simultaneous principal curves were constructed:

shrinklogical or numeric between 0 and 1. Determines whether and how much to shrink branching lineages toward their shared average curve.
extendcharacter. Specifies the method for handling root and leaf clusters of lineages when constructing the initial, piece-wise linear curve. Accepted values are 'y' (default), 'n', and 'pc1'. See getCurves for details.
reweightlogical. Indicates 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.
reassignlogical. Indicates 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.
shrink.methodcharacter. Denotes how to determine the amount of shrinkage for a branching lineage. Accepted values are the same as for kernel in the density function (default is "cosine"), as well as "tricube" and "density". See getCurves for details.
Other parameters specified by principal_curve.