From 58ab80b51838c25339742c30ecc9aadf24c8430c Mon Sep 17 00:00:00 2001
From: swetharom99 <swetharom99@mi.fu-berlin.de>
Date: Wed, 16 Apr 2025 08:52:36 +0000
Subject: [PATCH] Update file README.md
---
README.md | 13 ++++++++-----
1 file changed, 8 insertions(+), 5 deletions(-)
diff --git a/README.md b/README.md
index 2391ca4..cfe401d 100644
--- a/README.md
+++ b/README.md
@@ -47,19 +47,22 @@ Load and preprocess the datasets to extract the numeric data, with gene names as
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)
5. Generating Edge Lists
Generate edge lists to identify interactions between proteins, RNA, and phosphoproteins.
- ``` edge_list_control <- generateEdgeList(reduced_network_control, gene_names)
- ``` edge_list_disease <- generateEdgeList(reduced_network_disease, gene_names)
+ ```
+ edge_list_control <- generateEdgeList(reduced_network_control, gene_names)
+ edge_list_disease <- generateEdgeList(reduced_network_disease, gene_names)
6. Using LIONESS for Sample-Specific Networks
LIONESS is used to generate sample-specific networks for the data.
- ``` sample_specific_control <- lioness(data_control[, control], cor)
- ``` sample_specific_disease <- lioness(data_disease[, disease], cor)
+ ```
+ sample_specific_control <- lioness(data_control[, control], cor)
+ sample_specific_disease <- lioness(data_disease[, disease], cor)
After generating the correlation matrices for each sample using LIONESS, the next step is to integrate the DrDimont pipeline for which the layers must be created for each layer, which includes the identifiers and requires the annotations which is a required for the network generation.
--
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