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AtheerAS
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Hi @AtheerAS,
Thanks, looks great overall. Left only minor comments.
Major points to discuss:
- Unit tests for each new functions (I suggest starting with those; then changes to the rest would be easier)🙏 🙏 🙏 PS. I added tests for inflow, so that I can extend it to consider compositions.
- Can we try to think of some global summaries to add to
lrdata.var? For example, simply the average or sum; a cell-type frequency scaled average, and e.g. one that represents the mean / var ratio for each interaction; compute_global_score- I'm not sure if this should be constrained to lrdata objects; I also moved it toli.mt.sp; also,compute_global_specificity- to be more ... specific :)- Would it make more sense to filter non-zero variance for
lsrather than lrdata at the end? plot_spatial_inflow_score_single_interaction- let's try to pick a shorter and broader name; not limited to lrdata. More generally, is there any way to simplify a bit, maybe not all optional parameters are needed; i'd have nightmares that a user encountered an issue with it 😄 - see minor comments :)- Would be cool to include a function for the gradient plot also :)
- Inflow tutorial: Also, something is off with the logic where the resource is filtered. The order can also be improved a bit - e.g. show a simple interaction, and explain it how you did in the presentation (with each variable side-by-side), then go into permutations, complex plots by cell type, etc.
| import warnings | ||
| from typing import List, Tuple, Optional, Union | ||
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| def plot_spatial_inflow_score_single_interaction( |
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Lets try to think of a snappier name, this is a bit excessive :) I would also not limit it to inflow scores; you are plotting multiple features across cell types on a slide; spatial_features_by_obs or spatial_features_by_group
src/liana/plotting/_plot_spatial_inflow_score_single_interaction.py
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src/liana/plotting/_plot_spatial_inflow_score_single_interaction.py
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| # Build observed df | ||
| parts = [n.split(lr_sep) for n in full_names.tolist()] | ||
| df = pd.DataFrame(parts, columns=["source", "ligand", "receptor", "target"]) | ||
| df["lr_mean"] = values |
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changing lr_mean to specificity_rank to be compatible with liana_res format and function name.
| """Helper for splitting complex names.""" | ||
| toks = name.split(complex_sep) | ||
| if len(toks) > 1: | ||
| return toks[0], complex_sep.join(toks[1:]) |
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Edited this so complex name includes primary L/R as well (it was not).
src/liana/plotting/_heatmap.py
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| # This should technically not be hit if df_satisfy wasn't empty, but kept for robustness | ||
| raise ValueError(f"No interactions found for ligand_complex={ligand_complex}, receptor_complex={receptor_complex} after groupby-level filtering.") | ||
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| heatmap_df = df.pivot(index="source", columns="target", values="specificity_rank") |
| from liana._logging import _logg | ||
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| @d.dedent | ||
| def spatial_inflow( |
dbdimitrov
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dbdimitrov
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Hey, @AtheerAS, I left a second round of comments now that I tested re-running things.
LMK if I can clarify anything 🙏
src/liana/plotting/_heatmap.py
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| # convert p-values to stars | ||
| def p_to_star(p): |
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This did not work in the tutorial. Also, let's try to make more general - e.g. see heatmap with star in test_inflow.ipynb
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Would also make sense to have this function passed as a parameter to the function - I guess this would fix it; also make it a '_annotate_fun' and pass it to a annotate_lambda parameter; which is broader, not just pval -> then the function would work for e.g. cell pair proximity also
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Add inflow module and tutorials (incl. plotting functions & global score function).