Change defautl latent distribution to logit normal

docs/conf.py

@@ -108,6 +108,7 @@
 # a list of builtin themes.
 #
 html_theme = "sphinx_book_theme"
+html_logo = "_static/img/logo.png"
 html_static_path = ["_static"]
 html_css_files = ["css/custom.css"]
 

docs/contributing.md

@@ -103,7 +103,7 @@ Please write documentation for new or changed features and use-cases. This proje
 -   [Numpy-style docstrings][numpydoc] (through the [napoloen][numpydoc-napoleon] extension).
 -   Jupyter notebooks as tutorials through [myst-nb][] (See [Tutorials with myst-nb](#tutorials-with-myst-nb-and-jupyter-notebooks))
 -   [Sphinx autodoc typehints][], to automatically reference annotated input and output types
--   Citations (like {cite:p}`Virshup_2023`) can be included with [sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/)
+-   Citations (like {cite:p}`virshup2023`) can be included with [sphinxcontrib-bibtex](https://sphinxcontrib-bibtex.readthedocs.io/)
 
 See the [scanpy developer docs](https://scanpy.readthedocs.io/en/latest/dev/documentation.html) for more information
 on how to write documentation.

sccoral/module/_module.py

@@ -200,7 +200,9 @@ def __init__(
                     n_levels = 1
 
                 name = f"encoder_{cat_name}"
-                model = LinearEncoder(n_levels, 1, mean_bias=True, var_bias=True)
+                model = LinearEncoder(
+                    n_levels, 1, latent_distribution=latent_distribution, mean_bias=True, var_bias=True
+                )
 
                 # Register encoder in class
                 setattr(self, name, model)
@@ -215,7 +217,7 @@ def __init__(
         if continuous_names is not None:
             for con_name, dim in zip(continuous_names, range(n_latent + n_cat, n_latent + n_cat + n_con)):
                 name = f"encoder_{con_name}"
-                model = LinearEncoder(1, 1)
+                model = LinearEncoder(1, 1, latent_distribution=latent_distribution)
 
                 # Register encoder in class
                 setattr(self, name, model)

sccoral/nn/_components.py

@@ -16,7 +16,7 @@ class LinearEncoder(nn.Module):
         Number of input dimensions
     n_output
         Number of output dimensions
-    distributions
+    latent_distribution
         Normal distribution `normal` or lognormal `ln` (:cite:Svensson2020)
     return_dist
         Whether to return the distribution or samples
@@ -30,7 +30,7 @@ def __init__(
         self,
         n_input: int,
         n_output: int,
-        distribution: Literal["ln", "normal"] = "normal",
+        latent_distribution: Literal["ln", "normal"] = "ln",
         return_dist: bool = False,
         mean_bias: bool = True,
         var_bias: bool = True,
@@ -43,7 +43,7 @@ def __init__(
 
         self.var_eps = var_eps
 
-        if distribution == "ln":
+        if latent_distribution == "ln":
             self.z_transformation = nn.Softmax(dim=-1)
         else:
             # Identity function

tests/test_model.py

@@ -78,12 +78,12 @@ def test_pretraining_early_stopping(adata, pretraining_early_stopping, requires_
     assert model.module.z_encoder.encoder.fc_layers[0][0].weight.requires_grad == requires_grad
 
 
-@pytest.fixture(scope="module")
-def basic_train(adata):
+@pytest.fixture(scope="module", params=["normal", "ln"])
+def basic_train(adata, request):
     SCCORAL.setup_anndata(
         adata, categorical_covariates="categorical_covariate", continuous_covariates="continuous_covariate"
     )
-    model = SCCORAL(adata, n_latent=5)
+    model = SCCORAL(adata, n_latent=5, latent_distribution=request.param)
     model.train(max_epochs=20, accelerator="cpu")
 
     return model