Retrieve standardized LLM analysis prompt templates
prompt_template.Rdprompt_template() provides standardized, high-quality prompt templates designed
to be used alongside SKILL.md when querying Large Language Models (LLMs) such as
Claude, ChatGPT, Gemini, or local models (e.g. gpt-oss-20b, Ollama, LLaMA).
It facilitates a structured two-phase discovery workflow: Phase 1 (Reconnaissance & Profiling)
and Phase 2 (Biological Discovery: "What is interesting about this dataset?").
Usage
prompt_template(
type = c("discovery", "profiling", "pairwise", "multicontrast", "extracting")
)Arguments
- type
type of prompt template to retrieve:
"discovery": Comprehensive "What is interesting about this dataset?" biological discovery prompt (Phase 2)."profiling": Dataset reconnaissance and metadata extraction prompt (Phase 1)."pairwise": Targeted two-sample differential abundance prompt."multicontrast": Multi-condition contrast matrix / screen analysis prompt."extracting": Venn diagram overlap and segment extraction prompt.
Examples
library(tidyproteomics)
prompt_template("profiling")
#> You are an expert computational proteomics specialist using the R package `tidyproteomics`.
#> Refer to the instructions in SKILL.md.
#>
#> I have imported my quantitative mass spectrometry dataset in R, but need to inspect its structure before performing statistical analysis.
#>
#> Please provide a minimal, executable R script that:
#> 1. Loads `tidyproteomics` and `dplyr`.
#> 2. Imports the data file located at "[PATH_TO_DATA_FILE]" from platform "[PLATFORM: e.g. MaxQuant / ProteomeDiscoverer / FragPipe / DIA-NN]" as "[ANALYTE: proteins / peptides]".
#> 3. Runs `print(data)` to inspect the concise summary of data contents, sample names, number of proteins, imputed method, automatic normalization selection, and annotations.
#> 4. Runs `summary("sample")` as a data quality check to inspect replicate-level counts, CVs, and PCA separation.
#> 5. Prints all unique sample names using `get_sample_names()`.
#> 6. Prints available annotation categories using `show_annotations()`.
#> 7. Checks for potential sample contaminants using `summary(contamination = "CRAP")`.
#>
#> I will run this script in R and share the console output with you so we can design the comparative analysis.
prompt_template("discovery")
#> You are an expert quantitative proteomics data scientist. You have been provided with the `tidyproteomics` package instructions in SKILL.md.
#>
#> I have an imported `tidyproteomics` dataset with the following experimental setup:
#> - Organism: [e.g. Homo sapiens / Mus musculus]
#> - Biological Question: [e.g. Mechanism of action of Drug X / Knockout phenotype]
#> - Sample Groups: [e.g. Treatment = "drug_10uM", Control = "dmso", 3 replicates each]
#> - Available Annotations: [e.g. gene_name, description, biological_process, reactome_pathway]
#>
#> Your goal is to answer: "What is biologically and technically interesting about this dataset?"
#>
#> Generate a complete, self-contained R analysis script using `tidyproteomics` that:
#> 1. Evaluates Quality Control & Global Structure (Data Quality Check):
#> - Performs a data quality check with `summary("sample")` to inspect replicate CVs, quantifiable fraction, and PCA coordinates.
#> - Saves QC plots using built-in `destination = "png"` (e.g. `plot_variation_cv(destination = "png")`, `plot_dynamic_range(destination = "png")`, `plot_pca(destination = "png")`).
#> - Chains plot calls directly without `ggsave()`, capturing returned data objects in variables (`data <- data %>% plot_...`) to avoid console printing.
#> 2. Selects Optimal Normalization & Normalization Check:
#> - Tests candidate normalizations (`c("median", "linear", "loess", "limma")`) and selects the optimal method (`select_normalization()`).
#> - Runs `summary("normalization")` as a normalization check to confirm CV reduction and variance preservation across candidate normalizations.
#> - Verifies the automatic normalization selection via `print(data)`.
#> 3. Handles Missing Values:
#> - Imputes missing data appropriately (e.g. left-censored minimum within sample groups or random forest).
#> - Verifies the imputed method via `print(data)`.
#> 4. Uncovers Differential Expression:
#> - Compares Treatment vs Control using `expression(Treatment/Control, .method = "limma")`.
#> - Generates and saves a publication-ready volcano plot (`plot_volcano(destination = "png")`, log2fc_min = 1.0, significance_max = 0.05).
#> 5. Explores Biological Mechanism (Pathway Enrichment):
#> - Runs GSEA (`enrichment(Treatment/Control, .method = "gsea", .terms = "biological_process")`).
#> - Generates and saves an enrichment plot (`plot_enrichment(destination = "png")`).
#> 6. Highlights Key Findings:
#> - Extracts and prints the top 10 most significantly upregulated and downregulated proteins.
#> - Extracts and prints the top enriched biological pathways.
#> - Summarizes the findings into coherent biological hypotheses explaining what is most interesting about the dataset.
prompt_template("extracting")
#> You are an expert quantitative proteomics data scientist using `tidyproteomics` (see SKILL.md).
#>
#> I have an imported `tidyproteomics` dataset with multiple sample groups:
#> [List groups, e.g. "control", "drug_low", "drug_high"]
#>
#> I want to investigate which proteins are uniquely expressed in specific conditions versus shared across groups.
#>
#> Write a complete R script that:
#> 1. Filters for non-imputed proteins using `subset(imputed == 0)` to ensure true mass spectrometry identifications.
#> 2. Generates and saves a Venn/Euler diagram (`plot_venn(destination = "png")` or `plot_euler(destination = "png")`).
#> 3. Uses `intersection(.include = ..., .exclude = ...)` to extract the underlying data for:
#> - Proteins unique to [GROUP_A] (absent in other groups).
#> - Proteins shared across all groups.
#> - Specific overlap segments of interest.
#> 4. Uses `extract(na.rm = TRUE)` to convert each extracted segment into a tidy long-format tibble and exports them to CSV (`readr::write_csv()`).
#> 5. Prints a concise summary of each extracted segment using `print()`.