https://github.com/ncmbianchi/scculturepredict
An R package to extrapolate phenotypic fingerprint from RNAseq to determine cell culture media.
https://github.com/ncmbianchi/scculturepredict
bioconductor bioinformatics r rnaseq svm
Last synced: 3 months ago
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An R package to extrapolate phenotypic fingerprint from RNAseq to determine cell culture media.
- Host: GitHub
- URL: https://github.com/ncmbianchi/scculturepredict
- Owner: NCMBianchi
- License: mit
- Created: 2025-07-02T14:09:38.000Z (3 months ago)
- Default Branch: main
- Last Pushed: 2025-07-02T14:35:17.000Z (3 months ago)
- Last Synced: 2025-07-02T15:38:34.225Z (3 months ago)
- Topics: bioconductor, bioinformatics, r, rnaseq, svm
- Language: R
- Homepage:
- Size: 4.78 MB
- Stars: 0
- Watchers: 0
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE
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README
# scCulturePredict
[](https://bioconductor.org/)
[](https://www.r-project.org/)
[](https://opensource.org/licenses/MIT)
## Build and Apply Transcriptomic Fingerprints for Cell Culture Media Prediction
scCulturePredict is an R package that provides dual functionality for cell culture media prediction from single-cell transcriptomic data. **BUILD mode** generates transferable transcriptomic fingerprints from labeled training data, while **PREDICT mode** applies these pre-built fingerprints to unlabeled datasets for culture condition prediction.
## Features
### BUILD Mode (Generate Fingerprints)
- Train on labeled single-cell datasets
- Generate transferable transcriptomic fingerprints using KEGG pathway analysis
- Train both similarity-based and SVM prediction models
- Evaluate model performance with cross-validation
- Save fingerprints and models for future predictions
### PREDICT Mode (Apply Fingerprints)
- Apply pre-built fingerprints to unlabeled datasets
- Make culture media predictions using trained models
- Calculate prediction confidence scores
- Generate prediction-specific visualizations
### Core Capabilities
- Load and preprocess single-cell data (supports both CSV and 10X Genomics formats)
- Perform dimensionality reduction with UMAP and t-SNE
- Integrate with Seurat workflows
- Comprehensive evaluation and visualization tools
## Installation
### From Bioconductor (currently under review)
```r
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("scCulturePredict")
```
### From GitHub (development version)
```r
# install.packages("devtools")
devtools::install_github("nccb/scCulturePredict")
```
## Quick Start
### BUILD Mode: Generate Fingerprints from Labeled Data
```r
library(scCulturePredict)
# Build fingerprints from labeled training data
training_results <- scumap(
data_dir = "./DATA_labeled",
kegg_file = "kegg_file",
output_dir = "./training_results",
mode = "build",
experiment_id = "training",
progress = TRUE,
verbose = TRUE
)
# Access training results
fingerprint_file <- training_results$fingerprint_file
training_accuracy <- training_results$evaluation_results$overall_accuracy
print(paste("Training accuracy:", training_accuracy))
```
### PREDICT Mode: Apply Fingerprints to New Data
```r
# Apply fingerprints to unlabeled data
prediction_results <- scumap(
data_dir = "./DATA_unlabeled",
output_dir = "./prediction_results",
mode = "predict",
fingerprint_file = fingerprint_file,
experiment_id = "predictions",
progress = TRUE,
verbose = TRUE
)
# Access predictions
predictions <- prediction_results$seurat_object$classification_pred
confidence_scores <- prediction_results$seurat_object$prediction_confidence
# View results
head(data.frame(
cell_barcode = colnames(prediction_results$seurat_object),
predicted_medium = predictions,
confidence = confidence_scores
))
```
### Complete Workflow Example
```r
# Step 1: Build fingerprints (training phase)
training_results <- scumap(
data_dir = "./DATA_labeled",
kegg_file = "sce00001.keg",
output_dir = "./results/training",
mode = "build"
)
# Step 2: Apply to new data (prediction phase)
prediction_results <- scumap(
data_dir = "./DATA_unlabeled",
output_dir = "./results/predictions",
mode = "predict",
fingerprint_file = training_results$fingerprint_file
)
# Check prediction confidence
summary(prediction_results$seurat_object$prediction_confidence)
table(prediction_results$seurat_object$classification_pred)
```
### Advanced Usage
For users who need more control over individual steps:
```r
# Step-by-step approach
data_dir <- system.file("extdata", "example_data", package = "scCulturePredict")
seurat_object <- load_data(data_dir, experiment_id = "example")
seurat_object <- preprocess_data(seurat_object)
seurat_object <- reduce_dimensions(seurat_object)
# Continue with pathway analysis and predictions...
```
## Documentation
Comprehensive documentation is available in the package:
- `vignette("scCulturePredict-introduction")` - Introduction to scCulturePredict
- `vignette("scCulturePredict-visualization")` - Visualisation guide
- `vignette("scCulturePredict-advanced")` - Advanced usage
## Code Quality
scCulturePredict implements several code quality measures to ensure robustness and maintainability:
### Linting
The package uses `lintr` for static code analysis. To run linting checks:
```r
# Install lintr if needed
# install.packages("lintr")
# Run linting on the package
lintr::lint_package()
```
A `.lintr` configuration file is included in the package root.
### Code Formatting
Code formatting follows the Bioconductor style guidelines and is enforced using `styler`:
```r
# Install styler if needed
# install.packages("styler")
# Apply styling to the package
styler::style_pkg(style = styler::tidyverse_style(indent_by = 2))
```
### Comprehensive Checks
Run the comprehensive check script to ensure the package is ready for Bioconductor submission:
```r
# From the package root directory
Rscript scripts/check_package.R
```
This will run:
- R CMD check (with --as-cran flag)
- BiocCheck
- Linting checks
- Test coverage analysis
- Vignette building
- Example code execution
## Development
### Pre-commit Hook
To enforce code quality during development, you can install the pre-commit hook:
```bash
# From the package root directory
cp scripts/pre-commit-hook.R .git/hooks/pre-commit
chmod +x .git/hooks/pre-commit
```
### Contributing
Contributions are welcome! Please feel free to submit a Pull Request.
1. Fork the repository
2. Create your feature branch (`git checkout -b feature/amazing-feature`)
3. Run the code quality checks (`Rscript scripts/check_package.R`)
4. Commit your changes (`git commit -m 'Add some amazing feature'`)
5. Push to the branch (`git push origin feature/amazing-feature`)
6. Open a Pull Request
## Citation
If you use scCulturePredict in your research, please cite (bibtex format):
```bibtex
@Manual{scCulturePredict2025,
title = {scCulturePredict: Single-Cell Culture Media Prediction Using Transcriptomic Fingerprints},
author = {Nicolò Caporale Bianchi},
year = {2025},
note = {R package version 0.99.0},
url = {https://github.com/ncmbianchi/scCulturePredict},
}
```
## License
This project is licensed under the MIT License - see the LICENSE file for details.