Update file README.md

README.md

@@ -30,25 +30,28 @@ Pipeline Steps
 
 1. Loading and Preprocessing Data
 Load and preprocess the datasets to extract the numeric data, with gene names as row identifiers.
-    ``` data("mrna_data")
+    ``` 
+        data("mrna_data")
         data("protein_data")
         data("phosphosite_data")
         data("drug_gene_interactions") 
 
 2. Identifying Group-Specific Columns
     Identify the columns corresponding to the two groups using prefixes.
-    ``` control <- grep("^control_", colnames(protein_matrix))
+    ``` 
+        control <- grep("^control_", colnames(protein_matrix))
         disease <- grep("^disease_", colnames(protein_matrix)) 
 
 3. Computing Condition-Specific Networks
     Compute condition-specific correlation networks for the control and disease groups.
-    ``` data_control <- cor(t(data_control[, control]))
+    ``` 
+        data_control <- cor(t(data_control[, control]))
         data_disease <- cor(t(data_disease[, disease])) 
 
 4. Reducing Noise Using Thresholds
     Apply a threshold to reduce weak correlations and focus on the stronger relationships.
     ``` 
-    threshold_value <- 0.5
+        threshold_value <- 0.5
         reduced_network_control <- applyThreshold(network_control, threshold_value)
         reduced_network_disease <- applyThreshold(network_disease, threshold_value) 
 
@@ -68,7 +71,8 @@ After generating the  correlation matrices for each sample using LIONESS, the ne
 
 7. Integrating Data and Running DrDimont
 After processing all omics layers, integrate them using the DrDimont framework.
-    ``` # Create layers using DrDimont for each omics dataset
+    ``` 
+        # Create layers using DrDimont for each omics dataset
             mrna_layer <- make_layer(name="mrna", 
                          data_groupA=groupA_correlation_matrices$rna, 
                          data_groupB=groupB_correlation_matrices$rna,
@@ -85,7 +89,8 @@ After processing all omics layers, integrate them using the DrDimont framework.
 8. Creating Inter-Layer Connections
 The inter-layer connections was supplied by the user with make_connection(). The parameters from and to have to match to a name given in the previously created layers by make_layer(). The established connection results in an undirected combined graph. The parameter group indicates whether the connection will be applied to both groups (default) or only group A or B.
 Define the inter-layer connections between the omics layers based on shared gene names.
-    ``` all_inter_layer_connections <- list(
+    ``` 
+        all_inter_layer_connections <- list(
         make_connection(from='mrna', to='protein', connect_on='gene_name', weight=1, group="both"),
         make_connection(from='protein', to='phosphosite', connect_on='gene_name', weight=1, group="both"))