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New metric: ksim #75
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New metric: ksim #75
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Co-authored-by: Robrecht Cannoodt <[email protected]>
…h_integration into features/ksim
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@mumichae it's good to go! Please check out whenever you have time :) |
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Looking good! If you update the metric description, it should be ready to merge
src/metrics/ksim/config.vsh.yaml
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| label: kSIM | ||
| summary: "The kSIM acceptance rate measures whether cells of the same pre-annotated cell type are still close to each other in the local neighborhoods after batch correction." | ||
| description: | | ||
| The kSIM acceptance rate requires ground truth cell type information and measures whether the neighbors of a cell have the same cell type as it does. If a method overcorrects the batch effects, it will have a low kSIM acceptance rate. We use the HNSW algorithm to find k-NNs (including the cell itself) for each cell i and denote the number of neighbors that have the same cell type as i as . In addition, we require at least β fraction of neighbors of cell i to have the same cell type as i in order to say cell i has a consistent neighborhood. |
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Could you rephrase in your own words? Also, parameters don't make much sense when the formula isn't included. You could rephrase in such a way that the formula isn't needed to understand the metric
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Could you please rephrase the use of "we", since refers to the study authors, not the Openproblems team. Passive voice would be the best here.
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| * Added `metrics/kbet_pg` and `metrics/kbet_pg_label` components (PR #52). | ||
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| * Added `metircs/ksim` component (PR #75). |
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| * Added `metircs/ksim` component (PR #75). | |
| * Added `metrics/ksim` component (PR #75). |
src/metrics/ksim/config.vsh.yaml
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| label: kSIM | ||
| summary: "The kSIM acceptance rate measures whether cells of the same pre-annotated cell type are still close to each other in the local neighborhoods after batch correction." | ||
| description: | | ||
| The kSIM acceptance rate requires ground truth cell type information and measures whether the neighbors of a cell have the same cell type as it does. If a method overcorrects the batch effects, it will have a low kSIM acceptance rate. We use the HNSW algorithm to find k-NNs (including the cell itself) for each cell i and denote the number of neighbors that have the same cell type as i as . In addition, we require at least β fraction of neighbors of cell i to have the same cell type as i in order to say cell i has a consistent neighborhood. |
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Could you please rephrase the use of "we", since refers to the study authors, not the Openproblems team. Passive voice would be the best here.
Describe your changes
Checklist before requesting a review
I have performed a self-review of my code
Check the correct box. Does this PR contain:
Proposed changes are described in the CHANGELOG.md
CI Tests succeed and look good!