memory leak for single-cell resolution spatial edgelist construction

[paularstrpo]

Hi, Thank you for writing such a wonderful & useful tool!

I am currently using this for my MERFISH data; and I noticed that with even one sample of 30,000 cells; the function for constructing the edgelist causes a memory leak since it seems to first calculate all possible distances between all cells for the spatial edgelist before filtering for the k closest neighbors. For single-cell resolution spatial data, this can quickly get out of hand; in my case, my 64G RAM was no match.

I have made a slower, but more RAM-friendly work-around by hacking together a version of the edgelist construction function that finds the neighboring cells iteratively and only retains the k closest neighbor cells as it goes.

I'm including a snippet of it here (with added option for parallelization with future), since I hope it may be a useful addition to helping to solve this leak for others using your software.

# example for 10 neighbors
# meta is a data.frame with your cell metadata information
# spatial_1 and spatial_2 are column names inside the meta data frame for the x and y coordinates
get_niche_neighbors <- function(meta, spatial_1='spatial_1', spatial_2='spatial_2', k = 10) {
  library(pblapply)
  library(future)

  df <- data.frame(x = meta[,spatial_1], y = meta[, spatial_2])
  rownames(df) <- rownames(meta)

  edgelist <- pblapply(X = rownames(df),
                       cl = "future",
                       FUN = function(cell) {
    # only keep the k closest cells for each iteration; don't let it save the rest
    data.frame(
      from = cell,
      to = names(sort(setNames(sqrt(abs(df[cell, "x"] - df$x)^2 + abs(df[cell, "y"] - df$y)^2), rownames(df)))[1:(k + 1)])
      )
  })
  edgelist <- do.call(rbind, edgelist)

  return(edgelist)
}

Sorry if this would be better placed as a pull request; I am honestly not much of a developer!