Merge branch 'main' into deployment

vis_phewas/som/som_utils.py

@@ -118,12 +118,12 @@ def initialise_som(x_normalised, som_x=None, som_y=None, sigma=1.0, learning_rat
     """
     # Use rule of 10 sqrt(n) for the number of neurons if not specified
     if som_x is None:
-        som_x = int(np.sqrt(5 * np.sqrt(x_normalised.shape[0])))
+        som_x = int(np.sqrt(10 * np.sqrt(x_normalised.shape[0])))
     if som_y is None:
-        som_y = int(np.sqrt(5 * np.sqrt(x_normalised.shape[0])))
+        som_y = int(np.sqrt(10 * np.sqrt(x_normalised.shape[0])))
 
-    print(
-        f"Training SOM with {som_x}x{som_y} grid, sigma={sigma}, learning_rate={learning_rate}, num_iterations={num_iterations}")
+    print(f"Training SOM with {som_x}x{som_y} grid, sigma={sigma}, learning_rate={learning_rate}, "
+          f"num_iterations={num_iterations}")
 
     # Initialise the SOM
     input_len = x_normalised.shape[1]
@@ -144,7 +144,6 @@ def clean_filters(filters, som_type):
     :param som_type: Type of the SOM (SNP or disease)
     :return: Cleaned and formatted filters string
     """
-    print(f"Filters: {filters}")
     # If no filters are provided, return "All Genes" or "All Categories" as appropriate
     if not filters:
         return "All Genes" if som_type == 'snp' else "All Categories"
@@ -265,6 +264,7 @@ def plot_metrics_on_som(positions, som_type):
     silhouette_scores = []
     range_n_clusters = range(2, 11)  # Trying 2 to 10 clusters
 
+    # Perform KMeans clustering for each number of clusters
     for n_clusters in range_n_clusters:
         kmeans = KMeans(n_clusters=n_clusters, random_state=42)
         cluster_labels = kmeans.fit_predict(positions)  # Use the SOM positions for clustering

vis_phewas/som/views.py

@@ -160,6 +160,9 @@ def process_and_visualise_som(self, data_id, num_clusters, filters, som_type, te
         filter_list = cleaned_filters.lower()
         filter_list = filter_list.replace('<br>', ',').split(',')
         filter_list = [f.strip() for f in filter_list]
+        # Uppercase is type is SNP
+        if som_type == 'snp':
+            filter_list = [f.upper() for f in filter_list]
 
         # Render the visualisation
         graph_div = pio.to_html(fig, full_html=False)

vis_phewas/static/som/js/som.js

@@ -5,7 +5,6 @@
  * @param {number} [num_clusters] - The number of clusters to generate. Defaults to 5 for 'disease' and 7 for 'allele'.
  */
 function generateSOM(filters, type, num_clusters) {
-  console.log("Generating SOM with filters: " + filters + ", type: " + type + ", num_clusters: " + num_clusters);
   // Validate the type parameter
   if (type !== "snp" && type !== "disease") {
     alert("Invalid SOM type specified. Must be 'snp' or 'disease'.");

vis_phewas/templates/mainapp/tools.html

@@ -3,8 +3,8 @@
 <div class="panel tools_panel">
     <div id="som_options" style="align-items: center; display: flex; flex-direction: column">
         <h4>Generate Self Organising Map</h4>
-        <button class="button btn btn-info dataAction" onclick="generateSOM('', 'disease', 5)" style="color: white; margin-bottom: 1%">Generate Disease SOM</button>
-        <button class="button btn btn-info dataAction" onclick="generateSOM('', 'snp', 7)" style="color: white;">Generate Allele SOM</button>
+        <button class="button btn btn-info dataAction" onclick="generateSOM('', 'disease')" style="color: white; margin-bottom: 1%">Generate Disease SOM</button>
+        <button class="button btn btn-info dataAction" onclick="generateSOM('', 'snp')" style="color: white;">Generate Allele SOM</button>
     </div>
 </div>
 

vis_phewas/templates/som/som_view.html

@@ -42,7 +42,7 @@ <h2 style="align-self: center">Filter by Gene Name</h2>
                         <div class="checkbox-item">
                             <!-- Check the checkbox if it is in the selected filters (or if all genes are selected) -->
                             <input type="checkbox" id="{{ category }}" name="filters" value="{{ category }}"
-                                    {% if "all genes" in cleaned_filters %}
+                                    {% if "ALL GENES" in cleaned_filters %}
                                    checked
                                     {% else %}
                                         {% if category in cleaned_filters %} checked {% endif %}
@@ -70,7 +70,7 @@ <h2 style="align-self: center">Filter by Gene Name</h2>
 
                 <!-- Add a button to submit the form -->
                 <button type="submit" class="button btn btn-success" style="align-self: normal; border-radius: 20px">
-                    Generate SOM with Selection
+                    Generate New SOM with Selection
                 </button>
                 <!-- Add a button to download the cluster results CSV -->
                 <button class="btn btn-info dataAction" type="button">
@@ -118,8 +118,15 @@ <h2 style="align-self: center">Filter by Gene Name</h2>
             const filters = getSelectedFilters();
             const num_clusters = document.getElementById('clusters').value;
             const type = "{{ type }}";
-            console.log("Number of clusters: ", num_clusters);
-            console.log("Type: ", type);
+            // Handle edge case where only DPA1 is selected
+            if(filters === encodeURIComponent('gene_name:==:DPA1')){
+                alert("Insufficient data for this filter alone. Please select another filter.");
+                return;
+            }
+            if (filters === '') {
+                alert("Please select at least one filter.");
+                return;
+            }
             // Call the generateSOM function with the filters, type and number of clusters
             generateSOM(filters, type, num_clusters);
         }