Large language models for question-driven chemometric analysis: Automated generation of R workflows

Chemometrics has become an essential tool in analytical chemistry due to the increasing complexity and dimensionality of modern datasets. However, many researchers and students lack both the coding skills and a clear methodological roadmap required to perform a comprehensive chemometric analysis. In this work, we propose a structured, question-driven workflow comprising 17 sequential questions that guide users through a complete chemometric analysis using large language models (LLMs). The workflow includes data inspection, assessment of normality, identification of discriminative variables, unsupervised analyses by Principal Component Analysis (PCA) and Principal Coordinates Analysis (PCoA), and construction and validation of Partial Least Squares Discriminant Analysis (PLS-DA) models. The workflow was demonstrated using a previously reported lung cancer lipidomics dataset ( Sci. Transl. Med. , 14 , eabk2756, 2022) containing the concentrations of nine lipids measured in healthy controls and lung cancer patients. All analyses were performed in R using scripts generated by ChatGPT (GPT-5.5), Google Gemini (Flash-Lite), and DeepSeek (V4) in response to each analytical question. The generated scripts were executed directly in R to produce all figures and statistical analyses. The three LLMs successfully reproduced the complete exploratory chemometric workflow, whereas reproducible PLS-DA validation required explicit specification of preprocessing and cross-validation parameters. These results demonstrate that LLMs can substantially lower the technical barrier to chemometric analysis by providing methodological guidance and executable R code, making rigorous multivariate data analysis more accessible for both education and research.