scverse / scverse/scanpy

turn on the existing `n_iter=` parameter in multicore tsne

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Description

  • Additional function parameters / changed functionality / changed defaults?
  • New analysis tool: A simple analysis tool you have been using and are missing in sc.tools?
  • New plotting function: A kind of plot you would like to seein sc.pl?
  • External tools: Do you know an existing package that should go into sc.external.*?
  • Other?

t-SNE is an interative algorithm, and takes numerous iterations to converge, particularly on larger datasets. For example, if MulticoreTSNE is installed, it accepts n_iter=30000 as opposed to the default n_iter=1000. It would be nice to have this parameter exposed. For larger datasets of say 200K cells, 1000 iterations isn't enough to fully converge to its final compact cluster shapes.

Alternatively, is it possible to pass in a kwargs to scanpy tools that wrap other algorithms, so that the advanced user can flexibly look up additional MulticoreTSNE parameters to modify, without needing to exhaustively enumerate all parameters in the scanpy wrapper?

Finally, it would be even better to have the faster FFT-based tsne to generalize to millions of cells, the most recent re-implementation being https://github.com/pavlin-policar/openTSNE

In the mean time, one has to overwrite the .X_tsne attribute after running these other tools separately.

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First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. Open a pull request that references the issue number.

Research direction

Start at the Scanpy tool wrapper that invokes MulticoreTSNE and trace how t-SNE options are passed; the payload does not name a file or test. Expose the existing n_iter option and verify that a caller can use it without manually overwriting .X_tsne.

Written by the indexing model from the issue text.

Assessment

Tech stack
python
Domain
machine-learning
Issue type
Feature
Difficulty
3/5
Estimated time
1-2 days
Activity status
Stale
Clarity
Mostly clear
Newbie friendliness
35/100

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