** IMPORTANT:** Complete this setup process before running any R or Python scripts.
Required Version: R 4.5.1 or higher
Required Version: Python 3.10.17
Open R or RStudio and run:
# Install required packages
install.packages("Seurat")
install.packages("Signac")
# Verify installations
library(Seurat)
library(Signac)
# Check versions
packageVersion("Seurat") # Should be 5.3.0
packageVersion("Signac") # Should be 1.15.0# From R console
source("install_R_packages.R")| Package | Version |
|---|---|
| Seurat | 5.3.0 |
| Signac | 1.15.0 |
1. Create Virtual Environment:
# Create virtual environment
python -m venv scatactf_env
# Activate virtual environment
# On Windows:
scatactf_env\Scripts\activate
# On macOS/Linux:
source scatactf_env/bin/activate2. Install All Dependencies:
pip install -r Python_requirements.txtIf you prefer to install packages individually:
pip install pandas==2.2.3
pip install numpy==2.1.2
pip install scikit-learn==1.6.1
pip install xgboost==3.0.5
pip install imbalanced-learn==0.14.0
pip install matplotlib==3.10.1
pip install seaborn==0.13.2
pip install plotly==6.3.0
pip install networkx==3.3
pip install openpyxl==3.1.5
pip install joblib==1.4.2
pip install jupyter==1.1.1| Package | Version | Purpose |
|---|---|---|
| pandas | 2.2.3 | Data manipulation |
| numpy | 2.1.2 | Numerical computing |
| scikit-learn | 1.6.1 | Machine learning |
| xgboost | 3.0.5 | Gradient boosting |
| imbalanced-learn | 0.14.0 | Class imbalance handling |
| matplotlib | 3.10.1 | Visualization |
| seaborn | 0.13.2 | Statistical visualization |
| plotly | 6.3.0 | Interactive plots |
| networkx | 3.3 | Network analysis |
| openpyxl | 3.1.5 | Excel file handling |
| joblib | 1.4.2 | Model serialization |
| jupyter | 1.1.1 | Interactive notebooks |
Run the following in R/RStudio:
library(Seurat)
library(Signac)
cat("Seurat version:", as.character(packageVersion("Seurat")), "\n")
cat("Signac version:", as.character(packageVersion("Signac")), "\n")Expected Output:
Seurat version: 5.3.0
Signac version: 1.15.0
Run the following in Python:
import pandas as pd
import numpy as np
import sklearn
import xgboost as xgb
from imblearn.over_sampling import BorderlineSMOTE
import matplotlib.pyplot as plt
import seaborn as sns
import plotly
import networkx as nx
print("✓ All Python packages imported successfully!")Expected Output:
✓ All Python packages imported successfully!
Solution: Try upgrading pip first
pip install --upgrade pipSolution: Use a fresh virtual environment
# Deactivate current environment
deactivate
# Remove old environment
rm -rf scatactf_env # Linux/macOS
# OR
rmdir /s scatactf_env # Windows
# Create fresh environment
python -m venv scatactf_envSolution: Use a different pip mirror or install specific packages first
pip install --upgrade pip setuptools wheel
pip install -r Python_requirements.txtSolution: Update R to version 4.5.1 or higher
- Download from: https://cran.r-project.org/
Solution: Install BiocManager first
install.packages("BiocManager")
BiocManager::install()Solution: Install dependencies first
install.packages(c("Matrix", "Rcpp", "RcppArmadillo"))
install.packages("Seurat")
install.packages("Signac")All setup files can be found in the project repository's scripts directory:
- Python_requirements.txt - Python package dependencies
- install_R_packages.R - R package installation script
After completing the setup:
- Verify both R and Python installations (see Verification section)
- Proceed to the pipeline workflow guide
- Start with data preprocessing (R scripts)
- Continue with machine learning analysis (Python scripts)
** For detailed pipeline instructions, see:** Pipeline Workflow Guide
For issues or questions, please:
- Open an issue on the GitHub repository
- Contact the development team
- Refer to the documentation
Note: This setup guide is the first step before running any scATACtf analysis scripts. Make sure all packages are installed and verified before proceeding with the pipeline.