Merge pull request #24 from MICS-Lab/dev

Dev

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "scyan"
-version = "1.5.1"
+version = "1.5.2"
 description = "Single-cell Cytometry Annotation Network"
 documentation = "https://mics-lab.github.io/scyan/"
 homepage = "https://mics-lab.github.io/scyan/"

scyan/module/distribution.py

@@ -60,7 +60,7 @@ def compute_modes(self):
         self.factor[self.is_continuum_marker] = 5
         self.factor = self.factor[None, None, :]
 
-        self.modes = self.rho[None, ...] / self.factor
+        self.register_buffer("modes", self.rho[None, ...] / self.factor)
 
     @property
     def prior_h(self) -> distributions.Distribution:

scyan/plot/ratios.py

@@ -19,6 +19,7 @@ def pop_percentage(
     adata: AnnData,
     groupby: Union[str, List[str], None] = None,
     key: str = "scyan_pop",
+    figsize: tuple[float, float] = None,
     show: bool = True,
 ):
     """Show populations percentages. Depending on `groupby`, this is either done globally, or as a stacked bar plot (one bar for each group).
@@ -27,14 +28,15 @@ def pop_percentage(
         adata: An `AnnData` object.
         groupby: Key(s) of `adata.obs` used to create groups (e.g. the patient ID).
         key: Key of `adata.obs` containing the population names (or the values) for which percentage will be displayed.
+        figsize: matplotlib figure size.
         show: Whether or not to display the figure.
     """
     if groupby is None:
-        adata.obs[key].value_counts(normalize=True).mul(100).plot.bar()
+        adata.obs[key].value_counts(normalize=True).mul(100).plot.bar(figsize=figsize)
     else:
         adata.obs.groupby(groupby)[key].value_counts(normalize=True).mul(
             100
-        ).unstack().plot.bar(stacked=True)
+        ).unstack().plot.bar(stacked=True, figsize=figsize)
         plt.legend(
             bbox_to_anchor=(1.04, 0.5), loc="center left", borderaxespad=0, frameon=False
         )
@@ -79,6 +81,7 @@ def pop_dynamics(
         groupby = ([groupby] if isinstance(groupby, str) else groupby) + [time_key]
 
     df = cell_type_ratios(adata, groupby=groupby, key=key, normalize="%", among=among)
+    df.index = df.index.set_levels(df.index.levels[-1].astype(str), level=-1)
     df_log_count = np.log(
         1 + cell_type_ratios(adata, groupby=groupby, key=key, normalize=False)
     )

scyan/tools/gating.py

@@ -122,7 +122,7 @@ class PolygonGatingScatter:
 
     !!! note
 
-        If used on a Jupyter Notebook, you should first run `%matplotlib tk` on a blank jupyter cell. After the selection, you can run `%matplotlib inline` to retrieve the default behavior.
+        If used on a Jupyter Notebook, you should first run `%matplotlib tk`. After the selection, you can run `%matplotlib inline` to retrieve the default behavior.
 
     ```py
     # Usage example (`%matplotlib tk` is required for the cell selection on jupyter notebooks)