format and docstring edits for spatial modules

dance/modules/spatial/cell_type_deconvo/__init__.py

@@ -5,7 +5,7 @@
 
 __all__ = [
     "Card",
-    "DSTGLearner",
+    "DSTG",
     "SpatialDecon",
     "SPOTlight",
 ]

dance/modules/spatial/cell_type_deconvo/card.py

@@ -111,35 +111,36 @@ def CARDref(Xinput, U, W, phi, max_iter, epsilon, V, b, sigma_e2, Lambda):
 
 
 class Card:
-    """The CARD cell-type deconvolution model.
-
-    Parameters
-    ----------
-    sc_count : pd.DataFrame
-        Reference single cell RNA-seq counts data.
-    sc_meta : pd.DataFrame
-        Reference cell-type label information.
-    ct_varname : str, optional
-        Name of the cell-types column.
-    ct_select : str, optional
-        Selected cell-types to be considered for deconvolution.
-    cell_varname : str, optional
-        Name of the cells column.
-    sample_varname : str, optional
-        Name of the samples column.
-    minCountGene : int
-        Minimum number of genes required.
-    minCountSpot : int
-        Minimum number of spots required.
-    basis
-        The basis parameter.
-    markers
-        Markers.
-
-    """
+    """The CARD cell-type deconvolution model."""
 
     def __init__(self, sc_count, sc_meta, ct_varname=None, ct_select=None, cell_varname=None, sample_varname=None,
                  minCountGene=100, minCountSpot=5, basis=None, markers=None):
+        """Initialize Card.
+
+        Parameters
+        ----------
+        sc_count : pd.DataFrame
+            Reference single cell RNA-seq counts data.
+        sc_meta : pd.DataFrame
+            Reference cell-type label information.
+        ct_varname : str, optional
+            Name of the cell-types column.
+        ct_select : str, optional
+            Selected cell-types to be considered for deconvolution.
+        cell_varname : str, optional
+            Name of the cells column.
+        sample_varname : str, optional
+            Name of the samples column.
+        minCountGene : int
+            Minimum number of genes required.
+        minCountSpot : int
+            Minimum number of spots required.
+        basis
+            The basis parameter.
+        markers
+            Markers.
+
+        """
         self.sc_count = sc_count
         self.sc_meta = sc_meta
         self.ct_varname = ct_varname

dance/modules/spatial/cell_type_deconvo/spatialdecon.py

@@ -77,31 +77,32 @@ def cell_topic_profile(X, groups, ct_select, axis=0, method='median'):
 
 
 class SpatialDecon:
-    """SpatialDecon.
-
-    Parameters
-    ----------
-    sc_count : pd.DataFrame
-        Reference single cell RNA-seq counts data.
-    sc_annot : pd.DataFrame
-        Reference cell-type label information.
-    mix_count : pd.DataFrame
-        Target mixed-cell RNA-seq counts data to be deconvoluted.
-    ct_varname : str, optional
-        Name of the cell-types column.
-    ct_select : str, optional
-        Selected cell-types to be considered for deconvolution.
-    sc_profile: numpy array optional
-        Pre-constructed cell profile matrix.
-    bias : boolean optional
-        Include bias term, default False.
-    init_bias: numpy array optional
-        Initial bias term (background estimate).
-
-    """
+    """SpatialDecon."""
 
     def __init__(self, sc_count, sc_annot, ct_varname, ct_select, sc_profile=None, bias=False, init_bias=None,
                  device="cpu"):
+        """Initialize SpatialDecon.
+
+        Parameters
+        ----------
+        sc_count : pd.DataFrame
+            Reference single cell RNA-seq counts data.
+        sc_annot : pd.DataFrame
+            Reference cell-type label information.
+        mix_count : pd.DataFrame
+            Target mixed-cell RNA-seq counts data to be deconvoluted.
+        ct_varname : str, optional
+            Name of the cell-types column.
+        ct_select : str, optional
+            Selected cell-types to be considered for deconvolution.
+        sc_profile: numpy array optional
+            Pre-constructed cell profile matrix.
+        bias : boolean optional
+            Include bias term, default False.
+        init_bias: numpy array optional
+            Initial bias term (background estimate).
+
+        """
         super().__init__()
 
         self.device = device
@@ -199,9 +200,9 @@ def score(self, pred, true):
 
         Parameters
         ----------
-        pred :
+        pred
             Predicted cell-type proportions.
-        true :
+        true
             True cell-type proportions.
 
         Returns

dance/modules/spatial/cell_type_deconvo/spotlight.py

@@ -49,20 +49,21 @@ def cell_topic_profile(x, groups, ct_select, axis=0, method="median"):
 
 
 class NNLS(nn.Module):
-    """NNLS.
+    """NNLS."""
 
-    Parameters
-    ----------
-    in_dim : int
-        Input dimension.
-    out_dim : int
-        Output dimension.
-    bias : bool
-        Include bias term, default False.
+    def __init__(self, in_dim, out_dim, bias=False, init_bias=None, device="cpu"):
+        """Initialize NNLS.
 
-    """
+        Parameters
+        ----------
+        in_dim : int
+            Input dimension.
+        out_dim : int
+            Output dimension.
+        bias : bool
+            Include bias term, default False.
 
-    def __init__(self, in_dim, out_dim, bias=False, init_bias=None, device="cpu"):
+        """
         super().__init__()
         self.device = device
         self.model = nn.Linear(in_features=in_dim, out_features=out_dim, bias=bias)
@@ -116,35 +117,36 @@ def fit(self, x, y, max_iter, lr, print_res=False, print_period=100):
 
 
 class SPOTlight:
-    """SPOTlight class.
-
-    Parameters
-    ----------
-    ref_count : pd.DataFrame
-        Reference single cell RNA-seq counts data (cell x gene).
-    ref_annot : pd.DataFrame
-        Reference cell-type label information.
-    mix_count : pd.DataFrame
-        Target mixed-cell RNA-seq counts data to be deconvoluted.
-    ct_varname : str
-        Name of the cell-types column.
-    ct_select : str
-        Selected cell-types to be considered for deconvolution.
-    rank : int
-        Rank of the matrix factorization.
-    sc_profile: np.ndarray
-        Pre-constructed cell profile matrix.
-    bias : bool
-        Include bias term, default False.
-    init_bias: np.ndarray
-        Initial bias term (background estimate).
-    init : str
-        Initialization method for matrix factorization solver (see NMF from sklearn).
-
-    """
+    """SPOTlight."""
 
     def __init__(self, ref_count, ref_annot, ct_varname, ct_select, rank=2, sc_profile=None, bias=False, init_bias=None,
                  init="random", device="cpu"):
+        """Initialize SPOTlight.
+
+        Parameters
+        ----------
+        ref_count : pd.DataFrame
+            Reference single cell RNA-seq counts data (cell x gene).
+        ref_annot : pd.DataFrame
+            Reference cell-type label information.
+        mix_count : pd.DataFrame
+            Target mixed-cell RNA-seq counts data to be deconvoluted.
+        ct_varname : str
+            Name of the cell-types column.
+        ct_select : str
+            Selected cell-types to be considered for deconvolution.
+        rank : int
+            Rank of the matrix factorization.
+        sc_profile: np.ndarray
+            Pre-constructed cell profile matrix.
+        bias : bool
+            Include bias term, default False.
+        init_bias: np.ndarray
+            Initial bias term (background estimate).
+        init : str
+            Initialization method for matrix factorization solver (see NMF from sklearn).
+
+        """
         super().__init__()
         self.device = device
         self.bias = bias

dance/modules/spatial/spatial_domain/louvain.py

@@ -175,7 +175,7 @@ def modularity(partition, graph, weight="weight"):
        and values the communities
     graph : networkx.Graph
        the networkx graph which is decomposed
-    weight : str, optional
+    weight : str
         the key in graph to use as weight. Default to "weight"
 
 
@@ -245,20 +245,20 @@ def best_partition(graph, partition=None, weight="weight", resolution=1., random
     ----------
     graph : networkx.Graph
        the networkx graph which is decomposed
-    partition : dict, optional
+    partition : dict
        the algorithm will start using this partition of the nodes.
        It's a dictionary where keys are their nodes and values the communities
-    weight : str, optional
+    weight : str
         the key in graph to use as weight. Default to "weight"
-    resolution :  double, optional
+    resolution :  double
         Will change the size of the communities, default to 1.
         represents the time described in
         "Laplacian Dynamics and Multiscale Modular Structure in Networks",
         R. Lambiotte, J.-C. Delvenne, M. Barahona
-    randomize : boolean, optional
+    randomize : boolean
         Will randomize the node evaluation order and the community evaluation
         order to get different partitions at each call
-    random_state : int, RandomState instance or None, optional (default=None)
+    random_state : int, RandomState instance or None
         If int, random_state is the seed used by the random number generator;
         If RandomState instance, random_state is the random number generator;
         If None, the random number generator is the RandomState instance used
@@ -335,21 +335,21 @@ def __init__(self, resolution: float = 1):
         self.resolution = resolution
 
     def fit(self, adj, partition=None, weight="weight", randomize=None, random_state=None):
-        """fit function for model training.
+        """Fit function for model training.
 
         Parameters
         ----------
         adj :
             adjacent matrix.
-        partition : dict optional
+        partition : dict
             a dictionary where keys are graph nodes and values the part the node
             belongs to
-        weight : str, optional
+        weight : str
             the key in graph to use as weight. Default to "weight"
-        randomize : boolean, optional
+        randomize : boolean
             Will randomize the node evaluation order and the community evaluation
             order to get different partitions at each call
-        random_state : int, RandomState instance or None, optional (default=None)
+        random_state : int, RandomState instance or None
             If int, random_state is the seed used by the random number generator;
             If RandomState instance, random_state is the random number generator;
             If None, the random number generator is the RandomState instance used
@@ -368,31 +368,23 @@ def fit(self, adj, partition=None, weight="weight", randomize=None, random_state
         print("fit over ")
 
     def predict(self):
-        """prediction function.
-        Parameters
-        ----------
-
-        Returns
-        -------
-        self.predict_result :
-            predicted label.
-
-        """
+        """Prediction function."""
         self.predict_result = partition_at_level(self.dendo, len(self.dendo) - 1)
         self.y_pred = self.predict_result
         return self.predict_result
 
     def score(self, y_true):
-        """score function to get score of prediction.
+        """Score function to evaluate the prediction performance.
+
         Parameters
         ----------
-        y_true :
-            ground truth label.
+        y_true
+            Ground truth label.
 
         Returns
         -------
-        score : float
-            metric eval score.
+        float
+            Evaluation score.
 
         """
         pred_val = []