I only today started looking into spatial analysis and squidpy, so please be patient with me if this has an easy answer.
I loaded my data from a whole slide image with 754130 cells, each one carrying a cell type and coordinates (as well as 2 features, although these are randomly generated since I haven’t figured out what they’re used for if I already know the cell type from another source) into the AnnData format.
Visualizing this works fine
When trying to calculate stuff from this data, i.e. Ripley’s L:
mode = "L"
sq.gr.ripley(adatas[0], cluster_key="cell_type", mode=mode)
sq.pl.ripley(adatas[0], cluster_key="cell_type", mode=mode)
I get the error MemoryError: Unable to allocate 1.61 TiB for an array with shape (221285766430,) and data type float64
Is there a way to handle huge amounts of datapoints to do calculations in squidpy? E.g. dividing the datapoints into chunks and working through them sequentially.