scverse / scverse/scanpy

Imputation methods

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Description

@falexwolf, @flying-sheep

From the discussion on #45, I think some more discussion should be had as to what imputation methods are to be included in scanpy. Validation of and comparisons between the currently available imputation methods are both severely lacking---I only know of [1][2][3][4][5], none of which include comprehensive benchmarks, and the updated MAGIC (#187) article at Cell doesn't include relevant comparisons between current methods.

I'd be very interested in hearing/having an open discussion about the motivation, benefits, and limitations of the various imputation methods available.

[1]: Zhang and Zhang, 2017. https://www.biorxiv.org/content/early/2017/12/31/241190
[2]: Lopez et al. 2018, https://www.biorxiv.org/content/early/2018/03/30/292037
[3]: Li and Li, 2018. https://www.nature.com/articles/s41467-018-03405-7
[4]: Eraslan et al. 2018. https://www.biorxiv.org/content/early/2018/04/13/300681
[5]: Huang et al. 2018. https://www.biorxiv.org/content/early/2018/03/08/138677

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Research direction

No files, tests, or entry points are named. Start by reading discussion #45 and the cited papers, then review the current methods and the MAGIC work in #187. The issue needs agreement on which methods and comparisons are wanted before implementation can begin; its completion criteria are not defined.

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Assessment

Tech stack
python
Domain
bioinformatics, machine-learning
Issue type
Feature
Difficulty
5/5
Estimated time
Over a week
Activity status
Stale
Clarity
Needs clarification
Newbie friendliness
20/100

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