Reframe tissue curves on hover, enriching expression comparison #366
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
This helps compare gene expression in the focused tissue to that in other tissues, via an animated change in perspective.
Reframe_gene-tissue_expression_distirbution_curves_on_hover__Ideogram_2024-01-14.mov
Previously (#365) a given gene's expression in almost all top-expressed tissues often couldn't be usefully compared. That's because, in many cases, one of the ten tissues has a maximum expression drastically higher than all the others. The most expressed tissue would cause the distribution curves for all other tissues to be quite small and narrow. The distribution in other tissues was often indiscernible, beyond looking "very low".
Now, expression in those non-dominant tissues -- which is often biologically relevant -- can be compared in detail.
The focused tissue becomes the new coordinate reference for all tissues. The reference tissue gets scaled and translated to occupy the full width available to mini-curves. Other mini-curves get transformed to be viewed from the perspective of the focused tissue. To clarify what's happening, the coordinate reframing is animated. The expression distribution curves change form in a brief, smooth transition rather than a discontinuous snap from start to end state. This makes the novel scientific visualization technique more engaging and intuitive.
Other aspects of distribution curves, and protein feature colors, were also refined.