change back optional sample_cov_keys for de and add assertion, update…

… docstrings

src/mrvi/_model.py

@@ -760,6 +760,32 @@ def differential_abundance(
         sample_subset: list[str] | None = None,
         minibatch_size: int = 128,
     ) -> xr.Dataset:
+        """
+        Compute the differential abundance between samples.
+
+        Computes the logarithm of the ratio of the probabilities of each sample
+        conditioned on the estimated aggregate posterior distribution of each cell.
+
+        Parameters
+        ----------
+        adata
+            The data object to compute the differential abundance for.
+            If not given, the data object stored in the model is used.
+        sample_cov_keys
+            Keys for covariates (batch, etc.) that should also be taken into account
+            when computing the differential abundance.
+        sample_subset
+            Only compute differential abundance for these sample labels.
+        minibatch_size
+            Minibatch size for computing the differential abundance.
+
+        Returns
+        -------
+        :class:`xarray.Dataset`
+            Returns an xarray.Dataset with data variables:
+            - `log_probs`: Array of shape (n_cells, n_samples) containing the log probabilities for each cell across samples.
+            - `{cov_key}_log_probs`: For each key in `sample_cov_keys`, an array of shape (n_cells, n_cov_values) containing the log probabilities for each cell across covariate values.
+        """
         adata = self._validate_anndata(adata)
         us = self.get_latent_representation(adata, use_mean=True, give_z=False)
 
@@ -912,8 +938,8 @@ def get_outlier_cell_sample_pairs(
 
     def differential_expression(
         self,
-        sample_cov_keys: list[str],
         adata: AnnData | None = None,
+        sample_cov_keys: list[str] | None = None,
         sample_subset: list[str] | None = None,
         batch_size: int = 128,
         use_vmap: bool = True,
@@ -979,7 +1005,26 @@ def differential_expression(
         delta
             LFC threshold used to compute posterior DE probabilities.
             If None does not compute them to save memory consumption.
+
+        Returns
+        -------
+        xr.Dataset
+            An xarray Dataset containing the results of the differential expression analysis. The dataset includes:
+            - `beta`: Coefficients for each covariate across cells and latent dimensions.
+            - `effect_size`: Effect sizes for each covariate across cells.
+            - `pvalue`: P-values for each covariate across cells.
+            - `padj`: Adjusted P-values for each covariate across cells using the Benjamini-Hochberg procedure.
+            - `lfc`: Log fold changes for each covariate across cells and genes, if `store_lfc` is True.
+            - `lfc_std`: Standard deviation of log fold changes, if `store_lfc` is True and `delta` is not None.
+            - `pde`: Posterior DE probabilities, if `store_lfc` is True and `delta` is not None.
+            - `baseline_expression`: Baseline expression levels for each covariate across cells and genes, if `store_baseline` is True.
+            - `n_samples`: Number of admissible samples for each cell, if `filter_inadmissible_samples` is True.
         """
+        assert (
+            sample_cov_keys is not None
+        ), (
+            "Must assign `sample_cov_keys`"
+        )  # Keep as kwarg to maintain order of arguments.
         adata = self.adata if adata is None else adata
         self._check_if_trained(warn=False)
         # Hack to ensure new AnnDatas have indices and indices have correct dimensions.