Next peptides removed

README.md

@@ -0,0 +1,90 @@
+# 🧬 MultiomicMenu
+
+**Lead Scientist:** Dr. William G Ryan V
+**Last Updated:** February 11, 2026 
+**Status:** [Draft]
+
+This repository provides a standardized, configuration-driven framework for integrating high-dimensional multi-modal data (e.g., RNA-seq, Proteomics, etc.). The purpose of utilizing **Quarto (`.qmd`)** in this pipeline is to transform raw data into a fully documented, publication-ready HTML or PDF reports.
+
+---
+
+## 🚀 Quick Start
+
+You do not need to rewrite the analysis code. Follow these steps to generate your report:
+
+1.  **Use the Template, Download, and Navigate to Folder:**
+    a. Navigate to [https://github.com/CogDisResLab/MultiomicMenu](https://github.com/CogDisResLab/MultiomicMenu)
+    b. Click "Use this template" on the upper right corner of the page.
+    c. Choose a name for your repository. Remember to make it private for sensitive data.
+    d. Use git clone on your new repository:
+    ```bash
+    git clone `git clone [https://github.com/](https://github.com/)[username]/[repo-name].git`
+    cd [repo-name]
+    ```
+
+2.  **Environment Setup:**
+    Open R in the project working directory and restore the required package versions:
+    ```r
+    if (!require("renv")) install.packages("renv")
+    renv::restore()
+    ```
+3.  **Configure:**
+    Edit `index.qmd` with the following *important details*:
+    a. title
+    b. species
+    c. gmt (MUST be changed if not using human data)
+    d. data -> value -> data files
+4.  **Render:**
+    Execute the pipeline via the terminal:
+    ```bash
+    quarto render .
+    ```
+---
+
+## 📂 Repository Structure
+
+* `index.qmd`: **The Single Source of Truth.** Define all hyperparameters and file paths here.
+* `data/`: Put your DEGs, DPPs, DAPs, or DAKs here.
+* `learn.qmd`: Nothing is altered here: this is for informational purposes.
+* `data.qmd`: All data processing is done in this step, including integration and PPI generation using Kinograte.
+* `pathways.qmd`: All pathway data is processed here using PAVER.
+* `networks.qmd`: Network diagrams are generated here using igraph.
+
+---
+
+## ⚙️ Configuration (`config.yaml`)
+
+The pipeline logic is controlled entirely by the `config.yaml` file. Common parameters include:
+
+| Parameter | Description | Default/Example |
+| :--- | :--- | :--- |
+| `title` | Title used in the final report headers. | `"MultiomicMenu Report"` |
+| `species` | Species used in the experiment. | `[human, rat, or mouse]` |
+| `gmt` | GMT file to use for pathway information. | `gmt: "https://download.baderlab.org/EM_Genesets/current_release/Human/symbol/Human_GO_AllPathways_noPFOCR_with_GO_iea_February_03_2026_symbol.gmt"` |
+| `data` | Data files to use. | `["kinase_stk: 'data/kinase.csv'"]` |
+
+---
+
+## 🧪 Statistical Framework
+
+The integration primarily utilizes **Prize Collecting Steiner Forest (PCSF)** analysis with a PPI graph.
+
+---
+
+## 🛠 Requirements
+
+* **R:** ≥ 4.5.0
+* **Quarto CLI:** System-level installation required ([Download](https://quarto.org/docs/get-started/)).
+* **Memory:** 8GB+ RAM required. 16GB+ RAM recommended.
+
+---
+
+## 📊 Outputs
+
+Upon completion, the `results/` directory will contain:
+1.  **`index.html`**: An interactive report featuring all of the below pages.
+2.  **`learn.html`**: A page dedicated to describing the statistics and bioinformatics of MultiomicMenu.
+3.  **`data.html`**: A page for describing the data, including a "prize plot" which shows the cumulative distribution of "prizes".
+4.  **`pathways.html`**: A page for pathways that are hits in the analysis.
+5.  **`networks.html`**: A concatenation of all networks generated in the analysis, typically including a sample network and a seeded network.
+

data.qmd

@@ -311,7 +311,7 @@ combine_scores_patch <- function(df_rna = NULL, df_prot = NULL, df_kin = NULL, t
 combined_df <- combine_scores_patch(df_rna = data$rna,
                               df_prot = data$protein,
                               df_kin = data$kinase,
-                              df_pep = data$peptide,
+                              # df_pep = data$peptide,
                               tf_kin = chea3_results)
 
 string_ppi <- generate_string_ppi(params$species)

index.qmd

@@ -6,15 +6,15 @@ date: last-modified
 date-format: "[Last Updated on] MMMM DD, YYYY"
 params:
   subtitle: "For Dr. XYZ"
-  species: rat # one of: [human, mouse, rat]
-  gmt: "https://download.baderlab.org/EM_Genesets/current_release/Rat/symbol/GO/Rat_GOALL_with_GO_iea_July_01_2024_symbol.gmt"
+  species: human # one of: [human, mouse, rat]
+  gmt: "https://download.baderlab.org/EM_Genesets/current_release/Human/symbol/Human_GO_AllPathways_noPFOCR_with_GO_iea_February_03_2026_symbol.gmt"
   data:
     value:
     #  rna: "data/rna.csv"
     #  protein: "data/protein.csv"
-      kinase_stk: "data/creedenzymatic.csv"
+      kinase_stk: "data/kinase.csv"
     #  kinase_ptk: "data/kinase.csv"
-      peptide: "data/dpp_file.csv"
+      peptide: "data/peptides.csv"
 ---
 
 This is a MultomicMenu report.

networks.qmd

@@ -79,7 +79,7 @@ generate_seeded_network <- function(kinograte_res, gene, degree=1) {
 ```
 
 ```{r}
-genes_input <- names(combined) %>% sample(100)
+genes_input <- combined %>% head(100) %>% names()
 
 plot <- generate_subnetwork(kinograte_res, genes_input)
 
@@ -90,4 +90,4 @@ gene_input <- names(combined) %>% sample(1)
 plot <- generate_seeded_network(kinograte_res, gene_input, 1)
 
 plot
-```
+```