We should depreciate fast = FALSE and remove the warning If you run out of memory, try 'fast = FALSE'.

.format_data function can't be found.

library(lisaClust)
IMC <- lisaClust(IMC, k = 10, sigma = 50, BPPARAM = MulticoreParam(16), fast = FALSE,
                 imageID = "ImageNumber", cellType = "description",
                 spatialCoords = c("Location_Center_X", "Location_Center_Y"))
Error in lisaClust(IMC, k = 10, sigma = 50, BPPARAM = MulticoreParam(16),  : 
  object '.format_data' not found

It looks like it is expected to be housed in SpicyR: cd <- spicyR:::.format_data(cells, imageID, cellType, spatialCoords, FALSE).

Need to convert region to factor if it is numeric

Could pull a LISA curve from the geometries of SFE. This might allow for easy extension to some graph based summaries.

There's an example in the lisaClust paper that demonstrates the effect of sigma on the regions identified by lisaClust using the mouse olfactory bulb as an example image.

We might be able to do faster hatching using ggpatterns. Below is some prelim code. The main development would be defining nice looking hatching patterns with what is available. Their defaults aren't great for our purposes.

library(ggpattern)
library(tidySingleCellExperiment)
library(class)

######## Map predicted regions to a regular grid
#' @importFrom class knn
#' @importFrom grid linesGrob gpar gList
#' @importFrom spatstat.geom as.mask
regionGrid <- function(pp, nbp = 250) {
ow <- pp$window
m <- spatstat.geom::as.mask(ow, dimyx = c(nbp, nbp))$m
x <-
seq(
from = ow$xrange[1],
to = ow$xrange[2],
length.out = nrow(m)
)
y <-
seq(
from = ow$yrange[1],
to = ow$yrange[2],
length.out = ncol(m)
)
grid <- expand.grid(x = x, y = y)
grid <- data.frame(x = grid[, 1], y = grid[, 2])
df <- as.data.frame(pp)
k <- rep(NA, length(m))
K <-
class::knn(
train = df[, c("x", "y")],
test = grid[t(m)[seq_len(length(m))],],
cl = pp$region,
k = 1
)
k[t(m)[seq_len(length(m))]] <- as.character(K)
data.frame(grid, regions = k)
}

######## Convert grid of regions into a polygon mask for a particular region.
#' @importFrom spatstat.geom owin as.polygonal
regionPoly <- function(grid, region) {
rx <- range(grid$x)
ry <- range(grid$y)
mat <-
matrix(
grid$regions == region,
nrow = length(unique(grid$x)),
ncol = length(unique(grid$y)),
byrow = TRUE
)
mat[is.na(mat)] <- FALSE
ow <- spatstat.geom::owin(xrange = rx,
yrange = ry,
mask = mat)
return(spatstat.geom::as.polygonal(ow))
}

data("kerenSPE", package = "StatialBioc2023")
set.seed(51773)

Preparing features for lisaClust

kerenSPE <- lisaClust::lisaClust(kerenSPE, k = 5)

Filter colData to image 6

coords <- kerenSPE |>
filter(imageID == 6) |>
colData()

Set some parameters

window <- "convex"
nbp <- 300

Make the window for the ppp

ow <- makeWindow(coords, window)

Create a ppp

pp <-
spatstat.geom::ppp(coords$x,
coords$y,
window = ow,
marks = coords$region)

pp$region <- pp$marks
pp$cellType <- coords$cellType

Convert to mask

rG <- regionGrid(pp, nbp)

Convert grids for each region into polygons and create columns that

identify each polygon and the region it belongs to.

tree <- sapply(unique(pp$region), function(y){
rPoly <- regionPoly(rG, y)
rPoly <- lapply(rPoly$bdry, function(x){
data.frame(x)
})
rPoly <- bind_rows(rPoly, .id = "id")
}, simplify = FALSE) |>
bind_rows(.id = "region") |>
mutate(group = paste(region, id, sep = "_"))

Make the plot.

The key parameter is fill = NA which allows to see the cells.

We would need to make some nice hatching patterns and better spacings etc.

p1 <- ggplot() +
geom_point(data = data.frame(coords), aes(x,y, colour = cellType))+
geom_polygon_pattern(data = tree,
aes(x, y, group = group, pattern = region, pattern_angle = region),
colour = 'black',
pattern_density = 0.05,
pattern_spacing = 0.02,
pattern_size = 0.05,
fill = NA,
pattern_colour = 'black'
) + theme_bw(18)

p1

An list of NULLs is returned by a call to generateCurves function, so lisa function can't set row names on it.

Browse[1]> head(cellSummary)
SplitDataFrameList of length 6
$`1`
DataFrame with 1625 rows and 6 columns
      imageID      cellID imageCellID         x         y               cellType
     <factor> <character> <character> <numeric> <numeric>            <character>
1           1 cell_358130         1_1    49.000   1.05263                  HER2+
2           1 cell_358131         1_2   345.000   3.92308         HR- CKlow CK5+
3           1 cell_358132         1_3    29.500   5.00000      Fibroblasts CD68+
4           1 cell_358133         1_4   291.192   4.88462 Macrophages Vim+ CD4..
5           1 cell_358134         1_5   261.254   4.29851 Macrophages Vim+ Slug+
...       ...         ...         ...       ...       ...                    ...
1621        1 cell_359750      1_1621   383.965   502.807 Macrophages Vim+ Slug-
1622        1 cell_359751      1_1622   304.675   502.725 Macrophages Vim+ CD4..
1623        1 cell_359752      1_1623   404.263   501.800          Myoepithelial
1624        1 cell_359753      1_1624   464.870   502.522 Macrophages Vim+ Slug-
1625        1 cell_359754      1_1625   394.486   502.486          Myoepithelial

...
<5 more elements>
Browse[1]> fast
  FALSE
Browse[1]> str(generateCurves)
function (data, Rs, window, window.length, BPcellType = BPcellType, sigma = sigma, ...)  
Browse[1]> Rs
  20  50 100
Browse[1]> window
  "convex"
Browse[1]> window.length
NULL
Browse[1]> sigma
  50
Browse[1]> curveList <- BiocParallel::bplapply(cellSummary, generateCurves, 
        Rs = Rs, window = window, window.length = window.length, 
        BPcellType = BPcellType, BPPARAM = BPimage, sigma = sigma)
Browse[1]> head(curveList, 3)
$`1`
NULL
$`3`
NULL
$`4`
NULL

Browse[1]>   rownames(curves) <- as.character(unlist(lapply(cellSummary, function(x) x$cellID)))
  Error in dimnames(x) <- dn: length of 'dimnames' [1] not equal to array extent

Traceback.

1: lisaClust(IMC, k = 10, sigma = 50, BPPARAM = MulticoreParam(16), fast = FALSE, 
2: lisa(cd, Rs = Rs, BPPARAM = BPPARAM, window = window, window.length = window.le
3: `rownames<-`(`*tmp*`, value = as.character(unlist(lapply(cellSummary, function(

We could store the lisa curves in geometries column thing in SpatialFeatureExperiment.