AI_Omics_Internship_2025

Intro to R for Bioinformatics 🚀

This repository documents my weekly progress in learning R for bioinformatics programming as part of the AI Omics Research Internship (2025).

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📂 Lecture 1 (Class Ib): Getting Started with R

🔑 Topics Covered

1. Setting the working directory properly
2. Creating and organizing project folders
3. How R code works: functions, syntax, and execution
4. Variables and data types in R (numeric, integer, character, factor, logical)
5. Importing CSV files and working with categorical data
6. Saving scripts, outputs, and the R workspace

🎥 Lecture Recording
📌 Course GitHub Repo

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📖 Key Learnings

• Working Directory: how to set and use working folders so R knows where to look for files and save outputs.
• Project Organization: creating structured subfolders (data/, scripts/, results/) for reproducible research.
• R Basics:
  • Functions (mean(), plot(), hist(), etc.)
  • Variables and assignment (<-)
  • Simple data visualizations (scatterplot, histogram, barplot)
• Data Types in R:
  • Numeric vs Integer
  • Character / String
  • Factors for categorical variables
  • Logical data (TRUE/FALSE)
• Data Handling:
  • Importing .csv files with read.csv()
  • Checking structure with str()
  • Converting variables into factors or numeric codes (as.factor(), ifelse())
• Saving Outputs:
  • Export cleaned datasets with write.csv()
  • Save workspace and objects (save(), save.image())

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🧬 Assignment / Tasks

1. Set Working Directory

   • Create a new folder AI_Omics_Internship_2025.

2. Create Project Folder

   • In RStudio, make a new project called Module_I.
   • Inside, create subfolders: raw_data/, clean_data/, scripts/, results/, plots/.

3. Data Cleaning Task

   • Download patient_info.csv from GitHub.
   • Import the dataset into R.
   • Inspect structure (str()).
   • Identify variables with incorrect data types.
   • Convert them to appropriate formats (e.g., factors, numeric).

4. Feature Engineering

   • Create a new binary variable for smoking status:
     • 1 = Yes
     • 0 = No

5. Save Outputs

   • Save cleaned dataset as clean_data/patient_info_clean.csv.
   • Save script as scripts/class_Ib.R.
   • Upload both into this GitHub repository.

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📌 Example Code Snippets

# Set working directory
setwd("C:/Users/YourName/Documents/AI_Omics_Internship_2025")

# Import CSV
data <- read.csv("raw_data/patient_info.csv")

# Inspect structure
str(data)

# Convert gender to factor
data$gender_fac <- as.factor(data$gender)

# Create binary smoking variable
data$smoking_binary <- ifelse(data$smoking == "Yes", 1, 0)

# Save cleaned dataset
write.csv(data, file = "clean_data/patient_info_clean.csv", row.names = FALSE)



# Intro to R for Bioinformatics 🚀

This repository documents my weekly progress in learning **R for bioinformatics programming** as part of the AI Omics Internship (2025).  

## 📂 Contents
- **Lecture Notes & Scripts**: R scripts from weekly lessons.  
- **Assignments**: My solutions to assignments with explanations.  
- **Projects**: Applications of R in bioinformatics data analysis.  

## 📖 This Week's Focus
### Topic: Differential Expression Analysis & Gene Classification
- Learned how to:
  - Define and use **functions in R**.
  - Apply logical conditions to classify genes as *Upregulated*, *Downregulated*, or *Not Significant*.
  - Handle **missing data** (`NA`) using replacement strategies.
  - Add new columns to data frames (`$status`) for classification results.
  - Save and organize results into a dedicated folder (`Results/`).
  - Summarize results using `table()` to count gene categories.

### Assignment
Classify genes based on `logFC` and `padj` values:
- **Upregulated**: `logFC > 1 & padj < 0.05`
- **Downregulated**: `logFC < -1 & padj < 0.05`
- **Not Significant**: otherwise  

📌 Example function implemented:  

```r
classify_gene <- function(logFC, padj){
  if (logFC > 1 & padj < 0.05){
    return("Upregulated")
  } else if (logFC < -1 & padj < 0.05){
    return("Down regulated")
  } else {
    return("Not significant")
  }
}