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Commit 58ab80b5 authored by swetharom99's avatar swetharom99
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Update file README.md

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......@@ -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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