Description Usage Arguments Details Value Examples
Given a reduceddimension 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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97  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,
omega_scale = 3
)
## S4 method for signature 'matrix,character'
getLineages(
data,
clusterLabels,
reducedDim = NULL,
start.clus = NULL,
end.clus = NULL,
dist.fun = NULL,
omega = NULL,
omega_scale = 3
)
## S4 method for signature 'matrix,ANY'
getLineages(
data,
clusterLabels,
reducedDim = NULL,
start.clus = NULL,
end.clus = NULL,
dist.fun = NULL,
omega = NULL,
omega_scale = 3
)
## S4 method for signature 'SlingshotDataSet,ANY'
getLineages(
data,
clusterLabels,
reducedDim = NULL,
start.clus = NULL,
end.clus = NULL,
dist.fun = NULL,
omega = NULL,
omega_scale = 3
)
## S4 method for signature 'data.frame,ANY'
getLineages(
data,
clusterLabels,
reducedDim = NULL,
start.clus = NULL,
end.clus = NULL,
dist.fun = NULL,
omega = NULL,
omega_scale = 3
)
## S4 method for signature 'matrix,numeric'
getLineages(
data,
clusterLabels,
reducedDim = NULL,
start.clus = NULL,
end.clus = NULL,
dist.fun = NULL,
omega = NULL,
omega_scale = 3
)
## S4 method for signature 'matrix,factor'
getLineages(
data,
clusterLabels,
reducedDim = NULL,
start.clus = NULL,
end.clus = NULL,
dist.fun = NULL,
omega = NULL,
omega_scale = 3
)
## S4 method for signature 'SingleCellExperiment,ANY'
getLineages(
data,
clusterLabels,
reducedDim = NULL,
start.clus = NULL,
end.clus = NULL,
dist.fun = NULL,
omega = NULL,
omega_scale = 3
)

data 
a data object containing the matrix of coordinates to be used for
lineage inference. Supported types include 
clusterLabels 
character, a vector of length 
... 
Additional arguments to specify how lineages are constructed from clusters. 
reducedDim 
(optional) identifier to be used if 
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 reduceddimensional 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_scale 
(optional) numeric, scaling factor to use when 
The connectivity
matrix is learned by fitting a (possibly
constrained) minimumspanning 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
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.
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$dist
the pairwise
cluster distance matrix.
1 2 3 4 5 6 7  data("slingshotExample")
rd < slingshotExample$rd
cl < slingshotExample$cl
sds < getLineages(rd, cl, start.clus = '1')
plot(rd, col = cl, asp = 1)
lines(sds, type = 'l', lwd = 3)

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