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.
1 2 3 4 5 6 7 8 9 10 11  ## 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 
x 
a 
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.clus
character. The label of the root cluster.
end.clus
character. Vector of cluster labels indicating the
terminal clusters.
start.given
logical. A logical value
indicating whether the initial state was prespecified.
end.given
logical. A vector of logical values indicating
whether each terminal state was prespecified
dist
matrix. A
numeric matrix of pairwise cluster distances.
They may also specify how simultaneous principal curves were constructed:
shrink
logical or numeric between 0 and 1. Determines whether
and how much to shrink branching lineages toward their shared average
curve.
extend
character. Specifies the method for handling
root and leaf clusters of lineages when constructing the initial,
piecewise linear curve. Accepted values are 'y' (default), 'n', and 'pc1'.
See getCurves
for details.
reweight
logical.
Indicates whether to allow cells shared
between lineages to be reweighted during curvefitting. If TRUE
,
cells shared between lineages will be iteratively reweighted based on the
quantiles of their projection distances to each curve.
reassign
logical.
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.method
character.
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
.
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