Comments (4)
No, there is no such way in DPT. We had good experience with manually choosing it. In our opinion, no one really came up with a sound and reliable statistical way of detecting the number of branching points, independent of the underlying algorithm. The best attempts to solve the problem though might be found within Monocle 2 or K-Branches.
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Yes, it seems better to specify the number of branches manually. I have tried Monocle 2 though, which gives about 10 branches for the example dataset from Moignard et al., Nat. Biotechn. (2015). Maybe I didn't operate Monocle 2 properly.
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Thanks for the feedback! It astonishes me a bit, though, that Monocle 2 doesn't work well on that example. For the Paul et al, Cell (2015), it should give very nice results (link to preprocessing / link to plots), as they show in the recent preprint I reference above.
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The approx. graph abstraction (AGA) tool resolves this issue: https://doi.org/10.1101/208819 and https://github.com/theislab/graph_abstraction.
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Related Issues (20)
- Test full dask support for `log1p`, `normalize_per_cell`, `filter_cells`/`filter_genes`
- Incorrect number setting ofr highly_variable_genes HOT 8
- error in sc.tl.rank_genes_groups HOT 6
- Something wrong with read_10x_mtx using anndata-0.10.4 HOT 1
- Returning/changing genes as upper case instead of capitalized after running rank_genes_groups
- Seeming incompatibility with the numpy matrix subclass HOT 2
- read_10x_mtx() with annData 0.10.4 only reads one variable HOT 1
- Unify `method`/`flavor`/`backend`/… parameters
- Parameter docs render text in backticks as `<cite/>` text HOT 1
- Unable to plot embedding after Scanpy release 1.9.7 HOT 4
- pytest 8 compatibility HOT 2
- Fix plotting warnings raised in tutorial notebooks HOT 2
- Violin plots in Preprocessing and clustering 3k PBMCs tutorial HOT 4
- Documentation for percent_top= in scanpy.pp.calculate_qc_metrics is unclear and contradictory HOT 4
- Use of fast_matrix_market library to read mtx files HOT 1
- ValueError: b'Extrapolation not allowed with blending' when using `"sc.pp.highly_variable_genes"` function HOT 3
- sc.pp.subsample, allow for sampling with replacement HOT 1
- String conversion of `qc_vars` into a `Collection` when a `str` input is provided in `calculate_qc_metrics`
- ``biomart_annotations`` still creates cache when ``use_cache=False`` HOT 2
- Submodular optimization using apricot HOT 2
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