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Merged
Zethson merged 2 commits into from
Sep 10, 2026
Merged

Bump the pre-commit group with 3 updates #1095

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c23e0f2
Bump the pre-commit group with 3 updates
dependabot[bot] Sep 9, 2026
edb2ee8
Format markdown code blocks with updated ruff pre-commit hook
Zethson Sep 9, 2026
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6 changes: 3 additions & 3 deletions .pre-commit-config.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -9,12 +9,12 @@ default_stages:
minimum_pre_commit_version: 2.16.0
repos:
- repo: https://github.com/biomejs/pre-commit
rev: 055c65a8144b42a1f56908484127aec0c614fb85 # frozen: v2.5.10
rev: c4d01b70e92b66e6a88739145624279889634e92 # frozen: v2.5.11
hooks:
- id: biome-format
exclude: ^\.cruft\.json$ # inconsistent indentation with cruft - file never to be modified manually.
- repo: https://github.com/astral-sh/ruff-pre-commit
rev: aab412d509121cb5f7533134b7e67f9fab59c682 # frozen: v0.16.4
rev: 1f1e8bf348ff38fc88619a38d3ca4d9c56abea49 # frozen: v0.16.5
hooks:
- id: ruff-check
args: [--fix, --exit-non-zero-on-fix, --unsafe-fixes]
Expand Down Expand Up @@ -44,7 +44,7 @@ repos:
require_serial: true
pass_filenames: false
- repo: https://github.com/zizmorcore/zizmor-pre-commit
rev: 451b56af716f9f0d0c2b816503a3fd0cf8b036fa # frozen: v1.29.0
rev: cef8b8350da46d8114c7e6b7272aebdccdf193ce # frozen: v1.30.0
hooks:
- id: zizmor
args: [--no-progress, --fix]
24 changes: 6 additions & 18 deletions docs/api/tools_index.md
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Original file line number Diff line number Diff line change
Expand Up @@ -46,9 +46,7 @@ pdata = ps.compute(

edgr = pt.tl.EdgeR(pdata, design="~Efficacy+Treatment")
edgr.fit()
res_df = edgr.test_contrasts(
edgr.contrast(column="Treatment", baseline="Chemo", group_to_compare="Anti-PD-L1+Chemo")
)
res_df = edgr.test_contrasts(edgr.contrast(column="Treatment", baseline="Chemo", group_to_compare="Anti-PD-L1+Chemo"))
```

Inspecting a model summarizes the input data, the design and whether the model has been fitted, rendered as HTML in Jupyter:
Expand Down Expand Up @@ -172,9 +170,7 @@ mdata = milo.load(adata)
sc.pp.neighbors(mdata["rna"], use_rep="X_scVI", n_neighbors=150, n_pcs=10)
milo.make_nhoods(mdata["rna"], prop=0.1)
mdata = milo.count_nhoods(mdata, sample_col="patient_id")
mdata["rna"].obs["Status"] = (
mdata["rna"].obs["Status"].cat.reorder_categories(["Healthy", "Covid"])
)
mdata["rna"].obs["Status"] = mdata["rna"].obs["Status"].cat.reorder_categories(["Healthy", "Covid"])
milo.da_nhoods(mdata, design="~Status")

# Repeated measurements of the same donor are accounted for with a random intercept
Expand Down Expand Up @@ -233,9 +229,7 @@ sccoda_data = sccoda.prepare(
)
sccoda.run_nuts(sccoda_data, modality_key="coda_salm")
sccoda.summary(sccoda_data, modality_key="coda_salm")
sccoda.plot_effects_barplot(
sccoda_data, modality_key="coda_salm", parameter="Final Parameter"
)
sccoda.plot_effects_barplot(sccoda_data, modality_key="coda_salm", parameter="Final Parameter")
```

See [sccoda tutorial](https://pertpy.readthedocs.io/en/latest/tutorials/notebooks/sccoda.html), [extended sccoda tutorial](https://pertpy.readthedocs.io/en/latest/tutorials/notebooks/sccoda_extended.html) and [tasccoda tutorial](https://pertpy.readthedocs.io/en/latest/tutorials/notebooks/tasccoda.html).
Expand Down Expand Up @@ -346,9 +340,7 @@ ps_adata = ps.compute_control_diff(
)
ps_adata = ps_adata[ps_adata.obs["perturbation"] != "control"].copy()

query_profile = -ps_adata[
ps_adata.obs["perturbation"] == "p-sgCREB1-2"
].to_df().iloc[0]
query_profile = -ps_adata[ps_adata.obs["perturbation"] == "p-sgCREB1-2"].to_df().iloc[0]
up_genes = query_profile[query_profile > 0].nlargest(20).index.tolist()
down_genes = query_profile[query_profile < 0].nsmallest(20).index.tolist()

Expand Down Expand Up @@ -469,18 +461,14 @@ import pertpy as pt

train = pt.dt.kang_2018()

train_new = train[
~((train.obs["cell_type"] == "CD4T") & (train.obs["condition"] == "stimulated"))
]
train_new = train[~((train.obs["cell_type"] == "CD4T") & (train.obs["condition"] == "stimulated"))]
train_new = train_new.copy()

pt.tl.Scgen.setup_anndata(train_new, batch_key="condition", labels_key="cell_type")
scgen = pt.tl.Scgen(train_new)
scgen.train(max_epochs=100, batch_size=32)

pred, delta = scgen.predict(
ctrl_key="control", stim_key="stimulated", celltype_to_predict="CD4T"
)
pred, delta = scgen.predict(ctrl_key="control", stim_key="stimulated", celltype_to_predict="CD4T")
pred.obs["condition"] = "pred"
```

Expand Down
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