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I have been playing with omniscope's COVID dataset that provides 8M TCR receptors. By doing so, I identified several bottlenecks that make working with >1M cells in scirpy painful or impossible.
This meta issue is to give an overview of the progress improving scirpy's scalability.
graph TB
subgraph legend
legend1(could be faster -- minutes)
OK(OK -- seconds)
legend2(prohibitively slow -- hours)
legend3(not profiled yet)
style legend1 stroke:#ff7f00
style OK stroke:#4daf4a
style legend2 stroke:#e41a1c
end
ir_dist (Speed up ir_dist #304). Needs more scalable methods for computing sequence distances.
define_clonotypes (speed up define_clonotypes #368). At the very least needs a better parallelization. Maybe there's room for some jax/numba.
autoencoder-based embedding (Autoencoder-based sequence embedding #369). Possible alternative to ir_dist. Maybe it even makes sense to combine ir_dist and define_clonotypes into a single step.
The text was updated successfully, but these errors were encountered:
Description of feature
I have been playing with omniscope's COVID dataset that provides 8M TCR receptors. By doing so, I identified several bottlenecks that make working with >1M cells in scirpy painful or impossible.
This meta issue is to give an overview of the progress improving scirpy's scalability.
Action items
ir_dist
. Maybe it even makes sense to combineir_dist
anddefine_clonotypes
into a single step.The text was updated successfully, but these errors were encountered: