From f67733e408d0073506421c60b748a7ce74744b98 Mon Sep 17 00:00:00 2001
From: swetharom99 <swetharom99@mi.fu-berlin.de>
Date: Wed, 16 Apr 2025 08:53:33 +0000
Subject: [PATCH] Update file README.md
---
README.md | 17 +++++++++++------
1 file changed, 11 insertions(+), 6 deletions(-)
diff --git a/README.md b/README.md
index cfe401d..79458cb 100644
--- a/README.md
+++ b/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"))
--
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