facebookresearch / PoincareMaps
The need to understand cell developmental processes has spawned a plethora of computational methods for discovering hierarchies from scRNAseq data. However, existing techniques are based on Euclidean geometry which is not an optimal choice for modeling complex cell trajectories with multiple branches. To overcome this fundamental representation issue we propose Poincaré maps, a method harnessing the power of hyperbolic geometry into the realm of single-cell data analysis.
Summary
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| Main Code: 1,691 LOC (12 files) = PY (100%) Secondary code: Test: 0 LOC (0); Generated: 0 LOC (0); Build & Deploy: 0 LOC (0); Other: 66 LOC (3); |
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| Duplication: 9% | |||
| File Size: 0% long (>1000 LOC), 61% short (<= 200 LOC) | |||
| Unit Size: 0% long (>100 LOC), 23% short (<= 10 LOC) | |||
| Conditional Complexity: 0% complex (McCabe index > 50), 46% simple (McCabe index <= 5) | |||
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Logical Component Decomposition: primary (1 component) | ||
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1 year, 11 months old
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0% of code updated more than 50 times Also see temporal dependencies for files frequently changed in same commits. |
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| Goals: Keep the system simple and easy to change (4) | |||
Latest commit date: 2021-07-14
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