Implement vertical emd and split perfect integration #63
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use nan mean and nan max so NaN elements are not counted
LuLeom
reviewed
May 22, 2025
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Thanks for your effort @ghar1821 !
The PR looks good to me.
The only thing that I would add would be the flag _horizontal for the rest of the emd metrics in emd/config.vsh.yaml and emd/script.py (e.g. emd_mean_ct -> emd_mean_ct_horiz) so that the final heatmap would be easier to understand.
| label: Perfect Integration Vertical | ||
| # A one sentence summary of how this method works (required). Used when | ||
| # rendering summary tables. | ||
| summary: "Positive control method which imitates what perfect batch integration." |
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I would say 'Positive control method for vertical metrics reflecting a scenario in which all samples belong to the same batch'
| description: | | ||
| This control method return all samples from batch 1. | ||
| Because the samples all came from one batch, there should be no batch effect when | ||
| measuring vertical metrics. |
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I would specify that yes, we do not expect batch effect, but we still expect a sample-level effect due to the underlying different biology of the samples included in the same batch
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Describe your changes
Introduced new vertical emd metric and split the perfect integration into both vertical and horizontal.
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I have performed a self-review of my code
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Proposed changes are described in the CHANGELOG.md
CI Tests succeed and look good!