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Instructions-BulkRNAseq.Rmd
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---
title: "Preparation and Instructions for Bulk RNA-seq Tutorial"
author: "Yasin Kaymaz, Ph.D., Ege University, Bioengineering Dept."
date: "`r Sys.Date()`"
output: html_document
---
## 1st Winter School of Translational Immunology (TIWS)
`Bioinformatics in Immunology sessions | DRY LAB 1 (Group C)`
- Day 2 (14:00 - 16:00) - Bulk RNAseq data analysis
- Day 3 (14:00 - 16:00) - single-cell RNAseq data analysis
- Day 4 (11:10 - 13:00) - single-cell RNAseq data analysis, cont.
### Objective
This tutorial will guide students through a hands-on bulk RNA sequencing (RNA-seq) analysis using Google Colab and Python. Students will analyze gene expression data, perform normalization, PCA, sample correlation, and differential expression analysis using DESeq2.
### Dataset
- SRA Study ID: SRP428267
The publication titled _"TRAIL promotes the polarization of human macrophages toward a proinflammatory M1 phenotype and is associated with increased survival in cancer patients with high tumor macrophage content"_ investigates the role of Tumor Necrosis Factor-Related Apoptosis-Inducing Ligand (TRAIL) in macrophage polarization. The study demonstrates that TRAIL induces human macrophages to adopt a proinflammatory M1 phenotype. Furthermore, the research correlates high tumor macrophage content with improved survival rates in cancer patients, suggesting that TRAIL-mediated M1 polarization may contribute to anti-tumor immunity. [Gunalp et al., 2023](https://pubmed.ncbi.nlm.nih.gov/37809073/)
- Note: You will be able to download the processed gene counts matrix during the workshop.
---
## Preparation Checklist
### 1. Google Account
Ensure you have an active Google account to access Google Colab. If you don't have one, [create a Google account](https://accounts.google.com/signup).
### 2. Install Required Software (Optional)
While Google Colab eliminates the need for local installations, ensure your local machine has:
- A modern browser (e.g., Chrome, Firefox, or Edge).
- Stable internet connection.
### 3. Access the Tutorial Notebook
Download the tutorial notebook provided for this session:
- **File Name**: `Bulk_RNAseq_analysis_tutorial.ipynb`
- [Download the file](https://github.com/BMGLab/CrashCourses/blob/main/Bulk_RNAseq_analysis_tutorial.ipynb).
---
## Session Instructions
### Step 1: Open Google Colab
1. Visit [Google Colab](https://colab.research.google.com/).
2. Sign in with your Google account.
### Step 2: Upload the Notebook
1. Click `File > Upload Notebook`.
2. Upload the `Bulk_RNAseq_analysis_tutorial.ipynb` file from your local machine.
### Step 3: Set Up the Environment
1. Run the first cell in the notebook to install dependencies. This step will set up the required Python libraries and environment.
```python
!pip install pandas numpy seaborn matplotlib scikit-learn pydeseq2
```
2. If additional libraries are required, follow the prompts in the notebook.
### Step 4: Upload or Access Data
- **Option A: Use Preloaded Data**
- Follow the instructions in the notebook to use the sample data provided.
- **Option B: Use Your Own Data**
- Mount your Google Drive to access your datasets:
```python
from google.colab import drive
drive.mount('/content/drive')
```
### Step 5: Execute the Cells
1. Run each cell sequentially by clicking the play icon on the left of each cell.
2. Ensure you read the accompanying instructions for each step.
### Step 6: Troubleshooting
- If any errors occur, check the error message for missing dependencies and install them.
- Ensure that the runtime is set to `Python 3` by navigating to `Runtime > Change Runtime Type`.
---
## Tips for a Successful Hands-On Session
- **Save Your Progress**: Periodically save your notebook by clicking `File > Save a copy in Drive`.
- **Ask Questions**: Use the designated communication channel (e.g., Zoom chat or Slack) for real-time questions.
- **Follow Along**: Ensure you're running the cells in the correct order to avoid errors.
---
## After the Session
### Save Results
1. Save any generated plots or data to your Google Drive or download them to your local machine.
```python
significant_genes.to_csv("significant_genes.csv", index=False)
```
### Provide Feedback
Share your feedback on the session to help improve future tutorials.
---
## Support
If you encounter issues before or during the session, contact the instructor for assistance.