Description Usage Arguments Details Value References Examples
Perform lineage inference with Slingshot
Given a reduceddimensional 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 clusterbased minimum spanning tree and
constructing simultaneous principal curves for branching paths through the
tree.
This wrapper function performs lineage inference in two steps:
(1) identify lineage structure with a clusterbased minimum spanning tree
with the getLineages
function and (2) construct smooth
representations of each lineage using simultaneous principal curves from
the function getCurves
.
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 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226  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,
omega_scale = 3,
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,
omega_scale = 3,
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,
omega_scale = 3,
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,
omega_scale = 3,
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,
omega_scale = 3,
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,
omega_scale = 3,
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,
omega_scale = 3,
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,
omega_scale = 3,
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,
omega_scale = 3,
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,
...
)

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 parameters to pass to scatter plot smoothing function,

reducedDim 
(optional) identifier to be used if 
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

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 
lineages 
list generated by 
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, piecewise linear curve. Accepted values are

reweight 
logical, whether to allow cells shared between lineages to be
reweighted during curvefitting. If 
reassign 
logical, whether to reassign cells to lineages at each
iteration. If 
thresh 
numeric, determines the convergence criterion. Percent change
in the total distance from cells to their projections along curves must be
less than 
maxit 
numeric, maximum number of iterations, see

stretch 
numeric factor by which curves can be extrapolated beyond
endpoints. Default is 
approx_points 
logical or numeric, whether curves should be
approximated by a fixed number of points. If 
smoother, 
choice of scatter plot smoother. Same as

shrink.method 
character denoting how to determine the appropriate
amount of shrinkage for a branching lineage. Accepted values are the same
as for 
allow.breaks 
logical, determines whether curves that branch very close to the origin should be allowed to have different starting points. 
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.
When there is only a single lineage, the curvefitting 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
piecewise 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 nonincreasing 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 nonoutlying 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.
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.
Hastie, T., and Stuetzle, W. (1989). "Principal Curves." Journal of the American Statistical Association, 84:502–516.
1 2 3 4 5 6 7  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)

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