# Getting Started

`rsynthbio` is an R package that provides a convenient interface to the Synthesize Bio API, allowing users to generate realistic gene expression data based on specified biological conditions. This package enables researchers to easily access AI-generated transcriptomic data for various modalities including bulk RNA-seq and single-cell RNA-seq.

Alternatively, you can AI generate datasets from our web platform.

## How to install

You can install `rsynthbio` from CRAN:

    install.packages("rsynthbio")

If you want the development version, you can install using the `remotes` package to install from GitHub:

    if (!("remotes" %in% installed.packages())) {
      install.packages("remotes")
    }
    remotes::install_github("synthesizebio/rsynthbio")

Once installed, load the package:

    library(rsynthbio)

## Authentication

Before using the Synthesize Bio API, you need to set up your API token. The package provides a secure way to handle authentication:

    # Securely prompt for and store your API token
    # The token will not be visible in the console
    set_synthesize_token()

    # You can also store the token in your system keyring for persistence
    # across R sessions (requires the 'keyring' package)
    set_synthesize_token(use_keyring = TRUE)

Loading your API key for a session.

    # In future sessions, load the stored token
    load_synthesize_token_from_keyring()

    # Check if a token is already set
    has_synthesize_token()

You can manually set the token, but don’t commit it to version control!

    set_synthesize_token(token = "your-token-here")

You can obtain an API token by registering at Synthesize Bio.

## Available Model Types

Synthesize Bio provides several types of models for different use cases:

### Baseline Models

Generate synthetic gene expression data from metadata alone. You describe the biological conditions (tissue type, disease state, perturbations, etc.) and the model generates realistic expression profiles.

- `gem-1-bulk`: Bulk RNA-seq baseline model
- `gem-1-sc`: Single-cell RNA-seq baseline model

See the Baseline Models vignette for detailed usage.

### Reference Conditioning Models

Generate expression data conditioned on a real reference sample. This allows you to “anchor” to an existing expression profile while applying perturbations or modifications.

- `gem-1-bulk_reference-conditioning`: Bulk RNA-seq reference conditioning model
- `gem-1-sc_reference-conditioning`: Single-cell RNA-seq reference conditioning model

See the Reference Conditioning vignette for detailed usage.

### Metadata Prediction Models

Infer metadata from observed expression data. Given a gene expression profile, predict the likely biological characteristics (cell type, tissue, disease state, etc.).

- `gem-1-bulk_predict-metadata`: Bulk RNA-seq metadata prediction model
- `gem-1-sc_predict-metadata`: Single-cell RNA-seq metadata prediction model

See the Metadata Prediction vignette for detailed usage.

Only baseline models are available to all users. You can check which models are available programmatically, use `list_models()`. Contact us at support@synthesize.bio if you have any questions.

### Listing Available Models

You can check which models are available programmatically:

    # Check available models
    list_models()

## Quick Start

Here’s a quick example using a baseline model:

    # Get an example query structure
    query <- get_example_query(model_id = "gem-1-bulk")$example_query

    # Submit the query and get results
    result <- predict_query(query, model_id = "gem-1-bulk")

    # Access the results
    metadata <- result$metadata
    expression <- result$expression

For more detailed examples and advanced usage, see the model-specific vignettes linked above.

## Session info

    sessionInfo()

## Additional Resources

- Package Source Code
- File Bug Reports