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This is a great package! As you mention on the vignette, computing dist() can take (a) take a long time and (b) lots of memory. Abby @abspangler13 and I were considering using https://github.com/alexeckert/parallelDist/blob/master/R/parDist.R#L23 to resolve (a) but we would still be limited by (b). In particular if we computed a distance matrix across 100k spots in a Visium dataset (approx 75 GB of RAM: 1e5 * 1e5 * 8 / 1024^3 = 74.50581).
Hi,
This is a great package! As you mention on the vignette, computing
dist()
can take (a) take a long time and (b) lots of memory. Abby @abspangler13 and I were considering using https://github.com/alexeckert/parallelDist/blob/master/R/parDist.R#L23 to resolve (a) but we would still be limited by (b). In particular if we computed a distance matrix across 100k spots in a Visium dataset (approx 75 GB of RAM:1e5 * 1e5 * 8 / 1024^3 = 74.50581
).That led us to your work and well, something we noticed was missing from the vignette is an example where you choose a given
k
like they do in the following image from https://medium.com/codesmart/r-series-k-means-clustering-silhouette-794774b46586.We thought that adding such an example might be useful for users like us.
Best,
Leo
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