`methods.Rd`

Get and set methods for Milo object slots. Generally speaking these methods
are used internally, but they allow the user to assign their own externally computed
values - should be used *with caution*.

In the following descriptions `x`

is always a Milo object.

`graph(x)`

:Returns an

`igraph`

object representation of the KNN-graph, with number of vertices equal to the number of single-cells.`nhoodDistances(x)`

:Returns a list of sparse matrix of cell-to-cell distances between nearest neighbours, one list entry per neighbourhood. Largely used internally for computing the k-distance weighting in

`graphSpatialFDR`

.`nhoodCounts(x)`

:Returns a NxM sparse matrix of cell counts in each of

`N`

neighbourhoods with respect to the`M`

experimental samples defined.`nhoodExpression(x)`

:Returns a GxN matrix of gene expression values.

`nhoodIndex(x)`

:Returns a list of the single-cells that are the neighbourhood indices.

`nhoodReducedDim(x)`

:Returns an NxP matrix of reduced dimension positions. Either generated by

`projectNhoodExpression(x)`

or by providing an NxP matrix (see setter method below).`nhoods(x)`

:Returns a list of

`N`

neighbourhoods and constiuent single-cells.`nhoodGraph(x)`

:Returns an

`igraph`

object representation of the graph of neighbourhoods, with number of vertices equal to the number of neighbourhoods.`nhoodAdjacency(x)`

:Returns a matrix of

`N`

by`N`

neighbourhoods with entries of 1 where neighbourhods share cells, and 0 elsewhere.`graph(x) <- value`

:Populates the graph slot with

`value`

- this should be a valid graph representation in either`igraph`

or`list`

format.`nhoodDistances(x) <- value`

:Replaces the internally comptued neighbourhood distances. This is normally computed internally during graph building, but can be defined externally. Must be a list with one entry per neighbourhood containing the cell-to-cell distances for the cells within that neighbourhood.

`nhoodCounts(x) <- value`

:Replaces the neighbourhood counts matrix. This is normally computed and assigned by

`countCells`

, however, it can also be user-defined.`nhoodExpression(x) <- value`

:Replaces the

`nhoodExpression`

slot. This is calculated internally by`calcNhoodExpression`

, which calculates the`mean`

expression. An alternative summary function can be used to assign an alternative in this way.`nhoodIndex(x) <- value`

:Replaces the list of neighbourhood indices. This is provided purely for completeness, and is usually only set internally in

`makeNhoods`

.`nhoodReducedDim(x) <- value`

:Replaces the reduced dimensional representation or projection of neighbourhoods. This can be useful for externally computed projections or representations.

`nhoods(x) <- value`

:Replaces the neighbourhoods. Generally use of this function is discouraged, however, it may be useful for users to define their own bespoke neighbourhoods by some means.

`nhoodGraph(x) <- value`

:Populates the nhoodGraph slot with

`value`

- this should be a valid graph representation in either`igraph`

or`list`

format.`nhoodAdjacency(x) <- value`

:Populates the nhoodAdjacency slot with

`value`

- this should be a`N`

by`N`

matrix with elements denoting which neighbourhoods share cells`show(x)`

:Prints information to the console regarding the

`Milo`

object.

In the following descriptions `x`

is always a Milo object.

A collection of non-getter and setter methods that operate on Milo objects.

#> class: Milo #> dim: 60 200 #> metadata(0): #> assays(2): counts logcounts #> rownames: NULL #> rowData names(0): #> colnames: NULL #> colData names(0): #> reducedDimNames(1): PCA #> spikeNames(0): #> altExpNames(0): #> nhoods dimensions(1): 0 #> nhoodCounts dimensions(2): 1 1 #> nhoodDistances dimension(1): 0 #> graph names(0): #> nhoodIndex names(1): 0 #> nhoodExpression dimension(2): 1 1 #> nhoodReducedDim names(0): #> nhoodGraph names(0): #> nhoodAdjacency dimension(0):