The SFE method has special treatment for the spatial graphs. In listw,
the neighbors are indicated by indices, which will change after subsetting.
The SFE_graph_subset option determines whether the graphs are
subsetted or reconstructed. In the default (options(SFE_graph_subset =
TRUE)), the graphs are subsetted, in which case singletons may be produced.
For options(SFE_graph_subset = FALSE), which is the behavior of
versions earlier than Bioc 3.20, the graphs are reconstructed with the
parameters recorded in an attribute of the graphs. This option can result
into different graphs. For example, suppose we start with a k nearest
neighbor graph. After subsetting, cells at the boundary of the region used to
subset the SFE object may lose some of their neighbors. In contrast, when the
graph is reconstructed, these same edge cells will gain other cells that
remain after subsetting as neighbors in the new KNN graph.
Details
The option SFE_graph_subset was introduced because subsetting is
usually faster than reconstructing and in some cases such as distance-based
neighbors and Visium spot adjacency give the same results. It was introduced
also because of the development of alabster.sfe for a
language-agnostic on-disk serialization of SFE objects and some parameters
used to construct graphs have special classes whose alabaster methods
have not been implemented, such as BPPARAM and BNPARAM, so when
reconstructing, the defaults for those arguments will be used.
The edge weights will be recomputed from the binary neighborhood indicator with the same normalization style as the original graph, such as "W" for row normalization. When distance-based edge weights are used instead of the binary indicator, the edge weights will be re-normalized, which is mostly some rescaling. This should give the same results as recomputing the distance based edge weights for styles "raw", "W", and "B" since the distances themselves don't change, but the effects of other more complicated styles of re-normalization on spatial statistics should be further investigated.
Examples
# Just like subsetting matrices and SingleCellExperiment
library(SFEData)
sfe <- McKellarMuscleData(dataset = "small")
#> see ?SFEData and browseVignettes('SFEData') for documentation
#> loading from cache
sfe_subset <- sfe[seq_len(10), seq_len(10), drop = TRUE]
# Gives warning as graph reconstruction fails
# \donttest{
sfe_subset <- sfe[seq_len(10), seq_len(10)]
# }