Sends a query to the Synthesize Bio API for prediction and retrieves gene expression samples. This function sends the query to the API and processes the response into usable data frames.
Usage
predict_query(
query,
model_id,
api_base_url = API_BASE_URL,
poll_interval_seconds = DEFAULT_POLL_INTERVAL_SECONDS,
poll_timeout_seconds = DEFAULT_POLL_TIMEOUT_SECONDS,
return_download_url = FALSE
)Arguments
- query
A list representing the query data to send to the API. Use `get_example_query()` to generate an example. The query supports additional optional fields:
`total_count` (integer): Library size used when converting predicted log CPM back to raw counts. Higher values scale counts up proportionally.
`deterministic_latents` (logical): If TRUE, the model uses the mean of each latent distribution instead of sampling, producing deterministic outputs for the same inputs. Useful for reproducibility.
`seed` (integer): Random seed for reproducibility.
- model_id
Character string specifying the model ID (e.g., "gem-1-bulk", "gem-1-sc"). Use `list_models()` to see available models.
- api_base_url
The base URL for the API server. Default is API_BASE_URL.
- poll_interval_seconds
Seconds between polling attempts of the status endpoint. Default is DEFAULT_POLL_INTERVAL_SECONDS (2).
- poll_timeout_seconds
Maximum total seconds to wait before timing out. Default is DEFAULT_POLL_TIMEOUT_SECONDS (900 = 15 minutes).
- return_download_url
Logical, if TRUE, returns a list containing the signed download URL instead of parsing into data frames. Default is FALSE.
Value
A list. If `return_download_url` is `FALSE` (default), the list contains two data frames: `metadata` and `expression`. If `TRUE`, the list contains `download_url` and empty `metadata` and `expression` data frames.
Examples
# Set your API key (in practice, use a more secure method)
if (FALSE) { # \dontrun{
# To start using rsynthbio, first you need to have an account with synthesize.bio.
# Go here to create one: https://app.synthesize.bio/
set_synthesize_token()
# Get available models
models <- list_models()
# Create a query for a specific model
query <- get_example_query(model_id = "gem-1-bulk")
# Request raw counts
result <- predict_query(query, model_id = "gem-1-bulk")
# Access the results
metadata <- result$metadata
expression <- result$expression
# Explore the top expressed genes in the first sample
head(sort(expression[1, ], decreasing = TRUE))
# Use deterministic latents for reproducible results
query$deterministic_latents <- TRUE
result_det <- predict_query(query, model_id = "gem-1-bulk")
# Specify a custom total count (library size)
query$total_count <- 5000000
result_custom <- predict_query(query, model_id = "gem-1-bulk")
} # }