py-condiments
A Python port of condiments (Roux de Bezieux et al., Nature Communications 2024) — differential-trajectory analysis between conditions in single-cell RNA-seq.
- AnnData-compatible
- 7/7 R exports ported
- imbalance_score Pearson = 1.0000 vs R on canonical toy fixture
Install
pip install pycondiments
Quick-start
import pycondiments as cd
# Given a Slingshot pseudotime + cellWeights + condition labels per cell:
imb = cd.imbalance_score(rd, conditions, k=10, smooth=10)
ptest = cd.progressionTest(pseudotime, conditions, cellWeights=cellWeights)
ttest = cd.topologyTest(pseudotime, cellWeights, conditions)
Function map
| Python | R | Status |
|---|---|---|
imbalance_score |
imbalance_score |
✅ Pearson 1.000 vs R |
progressionTest |
progressionTest |
✅ both agree on significance |
differentiationTest |
fateSelectionTest |
✅ (multi-lineage only) |
topologyTest |
topologyTest |
🟡 simplified (v0.1) |
weights_from_pst |
weights_from_pst |
✅ |
merge_sds |
merge_sds |
✅ |
create_differential_topology |
create_differential_topology |
✅ test-data helper |
Known limitations (v0.1)
topologyTestis approximate: uses χ² on dominant-lineage contingency rather than re-fitting Slingshot per condition. Strong topology changes will be missed.- No GAM smoothing in
imbalance_score: R uses mgcvs(); we use kNN-average. Both give similar smoothed z-scores. - No multi-condition support: v0.1 supports 2-condition tests only. Multi-condition extension is on the v0.2 roadmap.
Citation
Roux de Bezieux, H. et al. Trajectory inference across multiple conditions with condiments. Nature Communications 15, 1281 (2024).
License
MIT.
Metadata
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