makeNhoods | R Documentation |
This function randomly samples vertices on a graph to define neighbourhoods. These are then refined by either computing the median profile for the neighbourhood in reduced dimensional space and selecting the nearest vertex to this position (refinement_scheme = "reduced_dim"), or by computing the vertex with the highest number of triangles within the neighborhood (refinement_scheme = "graph"). Thus, multiple neighbourhoods may be collapsed down together to prevent over-sampling the graph space.
makeNhoods( x, prop = 0.1, k = 21, d = 30, refined = TRUE, reduced_dims = "PCA", refinement_scheme = "reduced_dim" )
x |
A |
prop |
A double scalar that defines what proportion of graph vertices to randomly sample. Must be 0 < prop < 1. |
k |
An integer scalar - the same k used to construct the input graph. |
d |
The number of dimensions to use if the input is a matrix of cells X reduced dimensions. |
refined |
A logical scalar that determines the sampling behavior, default=TRUE implements a refined sampling scheme, specified by the refinement_scheme argument. |
reduced_dims |
If x is an |
refinement_scheme |
A character scalar that defines the sampling scheme, either "reduced_dim" or "graph". Default is "reduced_dim". |
This function randomly samples graph vertices, then refines them to collapse
down the number of neighbourhoods to be tested. The refinement behaviour can
be turned off by setting refine=FALSE
, however, we do not recommend
this as neighbourhoods will contain a lot of redundancy and lead to an
unnecessarily larger multiple-testing burden.
A Milo
object containing a list of vertices and
the indices of vertices that constitute the neighbourhoods in the
nhoods slot. If the input is a igraph
object then the output
is a matrix containing a list of vertices and the indices of vertices that constitute the
neighbourhoods.
Emma Dann, Mike Morgan
require(igraph) m <- matrix(rnorm(100000), ncol=100) milo <- buildGraph(m, d=10) milo <- makeNhoods(milo, prop=0.1) milo
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