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Prepare data for validation #100
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Use https://github.com/hubmapconsortium/hra-glb-mesh-collisions to get distances between corridors? |
With @bherr2, let's write a query to get:
Later:
How to define most similar RUI location: How to define most similar dataset: |
@bherr2 |
@andreasbueckle To keep name: |
@bherr2 shares new report with:
|
🚧 @andreasbueckle uses https://github.com/x-atlas-consortia/hra-pop/blob/main/output-data/v0.10.3/reports/atlas/validation-v7-x-axis.csv for x-axis |
Update 8/20/24:
For containment: |
Update 8/22/24: Instead, let's create a report to capture nowhereland: #105 |
I committed an updated notebook with 2 scattergraphs, one for each RUI sim measurement: https://github.com/cns-iu/hra-cell-type-populations-supporting-information/blob/main/validations/rui_ctann/rui_ctann_validation.ipynb |
Goal
Create scatter graph where each dot is a dataset out of 553:
Plot as 2D graph with RUI correctness on y-axis and CTann correctness on x-axis.
Each dot is one of the 553 Atlas datasets for which we do have “gold standard” data but we pretend not to have it.
RUI-based spatial correctness on y-axis is computed via %containment and then weighted cosine between original %AS to predicted %AS and
nowhereland
(empty space in registration, sticking out). If predicted = origins then the result is 1.CTann correctness on x-axis is computed via weighted cosine between original CTann to predicted CTann. If predicted = origins then the result is 1.
For each of the 553, we we get both values (each between 0 and 1).
Legend:
Data products needed
Need a long dataset with columns:
Steps
To compute
CTann sim
:To compute
RUI sim
:Get
tool
,organ
,sex
from https://lod.humanatlas.io/graph/hra-pop/v0.10.2/assets/atlas-enriched-dataset-graph.jsonld.The text was updated successfully, but these errors were encountered: