black

cospar/plotting/_gene.py

@@ -409,7 +409,7 @@ def gene_expression_heatmap(
             vmax=vmax,
             color_bar_label=color_bar_label,
             order_map_x=order_map_x,
-            x_tick_style='italic',
+            x_tick_style="italic",
             order_map_y=order_map_y,
             **kwargs,
         )
@@ -427,7 +427,7 @@ def gene_expression_heatmap(
             vmax=vmax,
             color_bar_label=color_bar_label,
             order_map_x=order_map_x,
-            y_tick_style='italic',
+            y_tick_style="italic",
             order_map_y=order_map_y,
             **kwargs,
         )
@@ -506,7 +506,7 @@ def gene_expression_on_manifold(
             point_size=point_size,
             color_bar_label="Normalized expression",
         )
-        plt.title(g,style='italic')
+        plt.title(g, style="italic")
 
         plt.tight_layout()
         if savefig:

cospar/plotting/_utils.py

@@ -214,12 +214,10 @@ def customized_embedding(
     if color_bar:
 
         norm = mpl_Normalize(vmin=vmin, vmax=vmax)
-        Clb = plt.colorbar(
-            plt.cm.ScalarMappable(norm=norm, cmap=color_map),
-            ax=ax)
+        Clb = plt.colorbar(plt.cm.ScalarMappable(norm=norm, cmap=color_map), ax=ax)
         Clb.set_label(
             color_bar_label,
-            rotation=270, 
+            rotation=270,
             labelpad=20,
         )
         Clb.ax.set_title(color_bar_title)
@@ -396,9 +394,9 @@ def heatmap(
 
     if ax is None:
         fig, ax = plt.subplots()
-        ax_=None
+        ax_ = None
     else:
-        ax_=ax
+        ax_ = ax
     ax.imshow(
         new_data,
         aspect="auto",
@@ -438,10 +436,10 @@ def heatmap(
             print(f"y_ticks: ['{y_ticks_print}']")
 
     if x_label is not None:
-        ax.set_xlabel(x_label,style=x_label_style)
+        ax.set_xlabel(x_label, style=x_label_style)
 
     if y_label is not None:
-        ax.set_ylabel(y_label,style=y_label_style)
+        ax.set_ylabel(y_label, style=y_label_style)
 
     if color_bar:
         norm = mpl_Normalize(vmin=vmin, vmax=vmax)
@@ -747,8 +745,10 @@ def layout(node):
 
     display(Image(filename=os.path.join(figure_path, f"{data_des}.png")))
 
-
-def plot_adata_with_prefered_order(adata,obs_key,basis='X_umap',plot_order=None,palette=None,**kwargs):
+
+def plot_adata_with_prefered_order(
+    adata, obs_key, basis="X_umap", plot_order=None, palette=None, **kwargs
+):
     """
     An example code
     ```python
@@ -760,15 +760,31 @@ def plot_adata_with_prefered_order(adata,obs_key,basis='X_umap',plot_order=None,
     ```
     """
     if plot_order is None:
-        plot_order=list(adata.obs[obs_key].unique())
+        plot_order = list(adata.obs[obs_key].unique())
     if palette is None:
-        palette=dict(zip(plot_order,np.array(sns.color_palette().as_hex())[:len(plot_order)]))
-
-    df_fate_map=pd.DataFrame({obs_key:adata.obs[obs_key],'x':adata.obsm[basis][:,0],'y':adata.obsm[basis][:,1]})
-    df_list=[]
+        palette = dict(
+            zip(plot_order, np.array(sns.color_palette().as_hex())[: len(plot_order)])
+        )
+
+    df_fate_map = pd.DataFrame(
+        {
+            obs_key: adata.obs[obs_key],
+            "x": adata.obsm[basis][:, 0],
+            "y": adata.obsm[basis][:, 1],
+        }
+    )
+    df_list = []
     for z in plot_order:
-        df_list.append(df_fate_map[df_fate_map[obs_key]==z])
-
-    df_map_v2=pd.concat(df_list,ignore_index=True)
-    g=sns.relplot(kind='scatter',data=df_map_v2,x='x',y='y',hue=obs_key,palette=palette,**kwargs)
-    g.ax.axis('off');
+        df_list.append(df_fate_map[df_fate_map[obs_key] == z])
+
+    df_map_v2 = pd.concat(df_list, ignore_index=True)
+    g = sns.relplot(
+        kind="scatter",
+        data=df_map_v2,
+        x="x",
+        y="y",
+        hue=obs_key,
+        palette=palette,
+        **kwargs,
+    )
+    g.ax.axis("off")

cospar/preprocessing/_preprocessing.py

@@ -550,7 +550,7 @@ def get_X_clone(
 
 def refine_state_info_by_leiden_clustering(
     adata,
-    selected_key='state_info',
+    selected_key="state_info",
     selected_values=None,
     resolution=0.5,
     n_neighbors=20,
@@ -568,7 +568,7 @@ def refine_state_info_by_leiden_clustering(
     Parameters
     ----------
     adata: :class:`~anndata.AnnData` object
-    selected_key: 
+    selected_key:
         A key in adata.obs, including 'state_info', or 'time_info'
     selected_values: `list`, optional (default: include all)
         A list of clusters/time_points for further sub-clustering. Should be
@@ -595,8 +595,8 @@ def refine_state_info_by_leiden_clustering(
 
     if selected_values == None:
         selected_values = available_time_points
-    if type(selected_values)==str:
-        selected_values=[selected_values]
+    if type(selected_values) == str:
+        selected_values = [selected_values]
 
     if np.sum(np.in1d(selected_values, available_time_points)) != len(selected_values):
         logg.error(
@@ -635,7 +635,7 @@ def refine_state_info_by_marker_genes(
     adata,
     marker_genes,
     express_threshold=0.1,
-    selected_key='state_info',
+    selected_key="state_info",
     selected_values=None,
     new_cluster_name="new_cluster",
     confirm_change=False,
@@ -663,7 +663,7 @@ def refine_state_info_by_marker_genes(
         Relative threshold of marker gene expression, in the range [0,1].
         A state must have an expression above this threshold for all genes
         to be included.
-    selected_key: 
+    selected_key:
         A key in adata.obs, including 'state_info', or 'time_info'
     selected_values: `list`, optional (default: include all)
         A list of clusters/time_points for further sub-clustering. Should be
@@ -688,8 +688,8 @@ def refine_state_info_by_marker_genes(
 
     if selected_values == None:
         selected_values = available_time_points
-    if type(selected_values)==str:
-        selected_values=[selected_values]
+    if type(selected_values) == str:
+        selected_values = [selected_values]
 
     sp_idx = np.zeros(adata.shape[0], dtype=bool)
     for xx in selected_values:

cospar/tool/_clone.py

@@ -581,7 +581,7 @@ def filter_clones(adata, clone_size_threshold=2, filter_larger_clones=False):
     else:
         filter smaller clones
     """
-    clone_size=adata.obsm['X_clone'].sum(0).A.flatten()
+    clone_size = adata.obsm["X_clone"].sum(0).A.flatten()
     if filter_larger_clones:
         clone_idx = clone_size < clone_size_threshold
         logg.info(
@@ -596,8 +596,8 @@ def filter_clones(adata, clone_size_threshold=2, filter_larger_clones=False):
     X_clone_new = adata.obsm["X_clone"][:, clone_idx]
 
     adata.obsm["X_clone_old"] = adata.obsm["X_clone"]
-    if 'clone_id' not in adata.uns:
-        adata.uns['clone_id']=np.arange(adata.obsm['X_clone'].shape[1])
+    if "clone_id" not in adata.uns:
+        adata.uns["clone_id"] = np.arange(adata.obsm["X_clone"].shape[1])
     adata.uns["clone_id"] = np.array(adata.uns["clone_id"])[clone_idx]
     adata.obsm["X_clone"] = ssp.csr_matrix(X_clone_new)
     logg.info("Updated X_clone")
@@ -608,10 +608,10 @@ def clone_statistics(adata, joint_variable="time_info"):
     """
     Extract the number of clones and clonal cells for each time point
     """
-    
-    if 'clone_id' not in adata.uns:
-        adata.uns['clone_id']=np.arange(adata.obsm['X_clone'].shape[1])
-    adata.obs[joint_variable]=adata.obs[joint_variable].astype(str)
+
+    if "clone_id" not in adata.uns:
+        adata.uns["clone_id"] = np.arange(adata.obsm["X_clone"].shape[1])
+    adata.obs[joint_variable] = adata.obs[joint_variable].astype(str)
 
     df = (
         pd.DataFrame(adata.obsm["X_clone"].A)
@@ -748,14 +748,16 @@ def compute_sister_cell_distance(
     logg.info(np.sum(X_clone.sum(0) >= 2), " clones with >=2 cells selected")
 
     # observed distances
-    distance_list, selected_clone_idx = get_distance_within_each_clone(X_clone,norm_distance)
+    distance_list, selected_clone_idx = get_distance_within_each_clone(
+        X_clone, norm_distance
+    )
 
     # randomized distances
     random_dis = []
     random_dis_stat = []
     for _ in tqdm(range(max_N_simutation)):
         np.random.shuffle(X_clone)
-        temp, __ = get_distance_within_each_clone(X_clone,norm_distance)
+        temp, __ = get_distance_within_each_clone(X_clone, norm_distance)
         random_dis += temp
         random_dis_stat.append(
             [np.mean(temp), np.min(temp), np.median(temp), np.max(temp)]

cospar/tool/_gene.py

@@ -27,8 +27,8 @@ def differential_genes(
     cell_group_B=None,
     FDR_cutoff=0.05,
     sort_by="ratio",
-    min_frac_expr=0.05, 
-    pseudocount=1
+    min_frac_expr=0.05,
+    pseudocount=1,
 ):
     """
     Perform differential gene expression analysis and plot top DGE genes.
@@ -96,7 +96,13 @@ def differential_genes(
 
     else:
 
-        dge = hf.get_dge_SW(adata, selections[0], selections[1],min_frac_expr=min_frac_expr,pseudocount=pseudocount)
+        dge = hf.get_dge_SW(
+            adata,
+            selections[0],
+            selections[1],
+            min_frac_expr=min_frac_expr,
+            pseudocount=pseudocount,
+        )
 
         dge = dge.sort_values(by=sort_by, ascending=False)
         diff_gene_A_0 = dge
@@ -110,51 +116,75 @@ def differential_genes(
 
     return diff_gene_A, diff_gene_B
 
-def identify_TF_and_surface_marker(gene_list,species='mouse',go_term_keywards=['cell surface','cell cycle','regulation of transcription','DNA-binding transcription factor activity','regulation of transcription by RNA polymerase II']):
+
+def identify_TF_and_surface_marker(
+    gene_list,
+    species="mouse",
+    go_term_keywards=[
+        "cell surface",
+        "cell cycle",
+        "regulation of transcription",
+        "DNA-binding transcription factor activity",
+        "regulation of transcription by RNA polymerase II",
+    ],
+):
     """
     From an input gene list, return the go term and annotation for each gene,
     and further select the genes identified as TF or cell surface protein
-    
+
     Returns
     ------
         results:
             Full annotation for each gene
         df_anno
             Only include genes identified as TF or cell surface protein
     """
-    
-    if species not in ['mouse','human']:
-        raise ValueError('species must be either mouse or human')
+
+    if species not in ["mouse", "human"]:
+        raise ValueError("species must be either mouse or human")
     else:
-        if species=='mouse':
-            dataset='mmusculus_gene_ensembl'
-        elif species=='human':
-            dataset='hsapiens_gene_ensembl'
-
-
-
+        if species == "mouse":
+            dataset = "mmusculus_gene_ensembl"
+        elif species == "human":
+            dataset = "hsapiens_gene_ensembl"
+
     from gseapy.parser import Biomart
+
     bm = Biomart()
     ## view validated marts
     marts = bm.get_marts()
     ## view validated dataset
-    datasets = bm.get_datasets(mart='ENSEMBL_MART_ENSEMBL')
+    datasets = bm.get_datasets(mart="ENSEMBL_MART_ENSEMBL")
     ## view validated attributes
-    attrs = bm.get_attributes(dataset=dataset) #hsapiens_gene_ensembl: Human genes (GRCh38.p13);  mmusculus_gene_ensembl for 'Mouse genes (GRCm39)'
+    attrs = bm.get_attributes(
+        dataset=dataset
+    )  # hsapiens_gene_ensembl: Human genes (GRCh38.p13);  mmusculus_gene_ensembl for 'Mouse genes (GRCm39)'
     ## view validated filters
-    filters = bm.get_filters(dataset=dataset) #Gene Name(s) [e.g. MT-TF]
+    filters = bm.get_filters(dataset=dataset)  # Gene Name(s) [e.g. MT-TF]
     ## query results
 
-
-    results = bm.query(dataset=dataset,
-                           attributes=['ensembl_gene_id', 'external_gene_name', 'namespace_1003','name_1006'],
-                           filters={'external_gene_name': gene_list})
-    results=results.dropna()
-    df_list=[]
+    results = bm.query(
+        dataset=dataset,
+        attributes=[
+            "ensembl_gene_id",
+            "external_gene_name",
+            "namespace_1003",
+            "name_1006",
+        ],
+        filters={"external_gene_name": gene_list},
+    )
+    results = results.dropna()
+    df_list = []
     for term in go_term_keywards:
-        tmp_genes=list(set(results[results['name_1006'].apply(lambda x: term == x)]['external_gene_name']))
-        df_tmp=pd.DataFrame({'gene':tmp_genes})
-        df_tmp['annotation']=term
+        tmp_genes = list(
+            set(
+                results[results["name_1006"].apply(lambda x: term == x)][
+                    "external_gene_name"
+                ]
+            )
+        )
+        df_tmp = pd.DataFrame({"gene": tmp_genes})
+        df_tmp["annotation"] = term
         df_list.append(df_tmp)
-    df_anno=pd.concat(df_list,ignore_index=True)
-    return results,df_anno
+    df_anno = pd.concat(df_list, ignore_index=True)
+    return results, df_anno