Author: Chen Yang
Date: 2026-05-11

# Contributing Guide

#### Chen Yang

#### 2026-05-11

# Contributing to mLLMCelltype

Thank you for your interest in contributing to mLLMCelltype! This guide will help you understand how to contribute to the project effectively.

## Getting Started

### Fork and Clone the Repository

1. Fork the mLLMCelltype repository on GitHub
2. Clone your fork to your local machine:

    git clone https://github.com/YOUR-USERNAME/mLLMCelltype.git
    cd mLLMCelltype

3. Add the original repository as an upstream remote:

    git remote add upstream https://github.com/cafferychen777/mLLMCelltype.git

### Setting Up the Development Environment

For R package development:

    # Install required packages for development
    install.packages(c("devtools", "roxygen2", "testthat", "knitr", "rmarkdown"))

    # Install the package in development mode
    devtools::install_dev("R")

## Project Structure

The mLLMCelltype project has the following structure:

    mLLMCelltype/
    ├── R/                  # R package source code
    │   ├── R/              # R functions
    │   ├── man/            # Documentation
    │   ├── tests/          # Unit tests
    │   ├── vignettes/      # Package vignettes
    │   └── DESCRIPTION     # Package metadata
    ├── python/             # Python package source code
    ├── .github/            # GitHub workflows and templates
    ├── assets/             # Images and other assets
    ├── examples/           # Example notebooks and scripts
    └── README.md           # Project overview

## Development Workflow

### Creating a New Feature

1. Create a new branch for your feature:

    git checkout -b feature/your-feature-name

2. Make your changes to the codebase
3. Add and commit your changes:

    git add .
    git commit -m "Add your descriptive commit message here"

4. Push your changes to your fork:

    git push origin feature/your-feature-name

5. Create a pull request from your fork to the main repository

### Code Style Guidelines

#### R Code Style

We follow the tidyverse style guide for R code:

- Use snake_case for variable and function names
- Use spaces around operators and after commas
- Use 2 spaces for indentation
- Limit line length to 80 characters
- Use roxygen2 for documentation

Example of properly formatted R code:

    #' Annotate Cell Types
    #'
    #' This function annotates cell types based on marker genes.
    #'
    #' @param input A data frame containing marker genes.
    #' @param tissue_name The name of the tissue.
    #' @param model The LLM model to use.
    #' @param api_key The API key for the LLM provider.
    #'
    #' @return A vector of cell type annotations.
    #' @export
    annotate_cell_types <- function(input, tissue_name, model, api_key) {
      # Function implementation
      results <- process_markers(input, top_n = 10)

      for (i in seq_along(results)) {
        if (is_valid_result(results[i])) {
          results[i] <- clean_result(results[i])
        }
      }

      return(results)
    }

#### Documentation Guidelines

All functions should be documented using roxygen2 with the following sections:

- Title (first line)
- Description (paragraph after title)
- @param for each parameter
- @return for the return value
- @examples for usage examples
- @export if the function should be exported

### Testing

We use the testthat package for testing. Tests should be placed in the `R/tests/testthat/` directory.

To run tests:

    devtools::test()

Example test file (`test-annotate_cell_types.R`):

    context("Cell type annotation")

    test_that("annotate_cell_types returns expected format", {
      # Setup test data
      test_markers <- data.frame(
        cluster = c(0, 0, 1, 1),
        gene = c("CD3D", "CD3E", "CD19", "MS4A1"),
        avg_log2FC = c(2.5, 2.3, 3.1, 2.8),
        p_val_adj = c(0.001, 0.002, 0.001, 0.003)
      )

      # Mock the API response
      mockery::stub(
        annotate_cell_types,
        "get_model_response",
        function(...) c("T cells", "B cells")
      )

      # Run the function
      result <- annotate_cell_types(
        input = test_markers,
        tissue_name = "test tissue",
        model = "test-model",
        api_key = "test-key"
      )

      # Assertions
      expect_is(result, "character")
      expect_length(result, 2)
      expect_equal(result, c("T cells", "B cells"))
    })

## Contributing Areas

### Adding Support for New LLM Models

To add support for a new LLM model:

1. Identify the model provider and API endpoint
2. Create a new processing function in `R/R/process_[provider].R`
3. Update the `get_provider()` function in `R/R/get_provider.R`
4. Add the model to the supported models list
5. Create tests for the new model
6. Update documentation

Example of adding a new model:

    # In process_newprovider.R
    process_newprovider <- function(prompt, api_key) {
      # Implementation for the new provider
      url <- "https://api.newprovider.com/v1/completions"

      headers <- c(
        "Content-Type" = "application/json",
        "Authorization" = paste("Bearer", api_key)
      )

      body <- list(
        model = "newprovider-model",
        prompt = prompt,
        max_tokens = 1000,
        temperature = 0.1
      )

      # Make API request using httr
      response <- httr::POST(
        url = url,
        httr::add_headers(.headers = headers),
        body = jsonlite::toJSON(body, auto_unbox = TRUE),
        encode = "json"
      )

      # Check for HTTP errors
      httr::stop_for_status(response)

      # Parse the response
      content <- httr::content(response, "text", encoding = "UTF-8")
      parsed_response <- jsonlite::fromJSON(content)
      result <- parsed_response$choices[[1]]$text

      return(result)
    }

    # In get_provider.R
    get_provider <- function(model) {
      # Add to the model mapping
      model_mapping <- list(
        # Existing models...
        "newprovider-model" = "newprovider"
      )

      provider <- model_mapping[[model]]
      if (is.null(provider)) {
        stop("Unsupported model: ", model)
      }

      return(provider)
    }

### Improving Documentation

Documentation improvements are always welcome:

1. Update function documentation with roxygen2
2. Improve vignettes with more examples and explanations
3. Add tutorials for specific use cases
4. Fix typos and clarify existing documentation

### Adding New Features

Some ideas for new features:

1. Integration with additional single-cell analysis frameworks
2. Support for spatial transcriptomics data
3. Interactive visualization tools
4. Batch processing for large datasets
5. Performance optimizations

### Reporting Issues

When reporting issues, please include:

1. A minimal reproducible example
2. The version of mLLMCelltype you’re using
3. The error message or unexpected behavior
4. Your R session information (`sessionInfo()`)

## Pull Request Process

1. Ensure your code follows the style guidelines
2. Add or update tests as necessary
3. Update documentation to reflect your changes
4. Ensure all tests pass
5. Submit your pull request with a clear description of the changes

## Code Review Process

All pull requests will be reviewed by the maintainers. The review process includes:

1. Checking that the code follows style guidelines
2. Verifying that tests pass
3. Ensuring documentation is updated
4. Evaluating the overall design and implementation

## Release Process

mLLMCelltype follows semantic versioning (MAJOR.MINOR.PATCH):

- MAJOR version for incompatible API changes
- MINOR version for new functionality in a backward-compatible manner
- PATCH version for backward-compatible bug fixes

## Community Guidelines

### Code of Conduct

We follow a code of conduct to ensure a welcoming and inclusive community:

- Be respectful and inclusive
- Be collaborative
- Be open to feedback
- Focus on the best solution for the community

### Communication Channels

- GitHub Issues: For bug reports, feature requests, and discussions
- GitHub Discussions: For general questions and community discussions
- Pull Requests: For code contributions

## Acknowledgment

Contributors will be acknowledged in the package documentation and README.

## License

By contributing to mLLMCelltype, you agree that your contributions will be licensed under the same license as the project (MIT License).

## Next Steps

Now that you know how to contribute to mLLMCelltype, you can:

- Review the version history to understand recent changes
- Explore advanced features to identify areas for improvement
- Check the FAQ to see common questions that might need better documentation