AI-Powered Workflow in Glysmith

glysmith leverages the power of Large Language Models (LLMs) to make glyco-omics analysis more intuitive and efficient. From generating complex analytical pipelines to crafting comprehensive reports, AI integration allows you to focus on the science while it handles the boilerplate.

Getting Started

To enable AI features, configure an API key for the LLM provider you want to use. DeepSeek is the default provider, and you can obtain a key from the DeepSeek Platform.

Once you have your key, set it as an environment variable in your R session:

Sys.setenv(DEEPSEEK_API_KEY = "your_api_key")

You can also use other providers supported by ellmer. For example, OpenAI can be used by setting OPENAI_API_KEY and passing provider = "openai":

Sys.setenv(OPENAI_API_KEY = "your_api_key")
bp <- inquire_blueprint(
  "I want to perform DEA and visualize the results.",
  exp = your_exp,
  provider = "openai",
  model = "gpt-4.1"
)

If you prefer to configure the provider once per R session, use package-level options:

options(
  glysmith.ai_provider = "openai",
  glysmith.ai_model = "gpt-4.1"
)

bp <- inquire_blueprint(
  "I want to perform DEA and visualize the results.",
  exp = your_exp
)

For OpenAI-compatible endpoints, set OPENAI_API_KEY and pass both provider = "openai_compatible" and base_url. The corresponding options are glysmith.ai_provider, glysmith.ai_model, glysmith.ai_api_key, and glysmith.ai_base_url.

Note: This environment variable is session-specific. You will need to set it again in new R sessions, or add it to your .Renviron file for a permanent setup.

Cost and Efficiency

While using LLMs does incur costs, it is extremely affordable for glysmith workflows. Generating a blueprint or an AI-driven report typically costs less than $0.01 per run, offering professional-grade assistance for a fraction of a cent.

library(glysmith)

Designing Pipelines with inquire_blueprint()

Instead of manually browsing and selecting various step_xxx functions and manage the parameters, you can describe your analysis goals in plain English (or your language). inquire_blueprint() will translate your requirements into a structured analytical blueprint.

# Describe your goals in natural language
bp <- inquire_blueprint("I want to perform DEA and visualize the results.", exp = your_exp)
print(bp)

By providing the experiment object (exp), the LLM can understand your data structure and experimental design, ensuring the generated blueprint is tailored to your specific dataset. The function also prints a brief rationale for the chosen steps to the console.

After you answering all the questions, a blueprint is returned as bp. You can then pass it to forge_analysis():

res <- forge_analysis(exp, blueprint = bp)

Refining Your Analysis

If the initial blueprint needs adjustment, you can use modify_blueprint() to refine it iteratively without starting from scratch.

# Add or remove steps using natural language
new_bp <- modify_blueprint(bp, "Also include a PCA analysis.", exp = your_exp)
print(new_bp)

AI-Enhanced Reporting

While polish_report() uses robust default rules, setting use_ai = TRUE unlocks advanced AI capabilities for report generation:

polish_report(result, "report.html", use_ai = TRUE)

To use a non-default provider for reporting, pass the ai_ configuration arguments:

polish_report(
  result,
  "report.html",
  use_ai = TRUE,
  ai_provider = "openai",
  ai_model = "gpt-4.1"
)

In AI mode, the LLM performs several high-level tasks:

  1. Structural Optimization: Dynamically arranges sections, text, and figures for optimal flow.
  2. Contextual Narrative: Drafts descriptive text for each analysis section.
  3. Multimodal Interpretation: Uses vision-capable models to interpret and explain generated figures.
  4. Professional Finishing: Polishes titles and subtitles for a publication-ready look.

Complete AI Workflow

The entire pipeline, from raw data to a finished report, can be simplified into a few intelligent steps:

# 1. Generate an analysis plan
bp <- inquire_blueprint("Perform DEA and visualize key findings.", exp = your_exp)

# 2. Execute the analysis
res <- forge_analysis(your_exp, bp)

# 3. Export data and intermediate results
quench_result(res, "analysis_output/")

# 4. Generate an AI-enhanced report
polish_report(res, "report.html", use_ai = TRUE)