Public repositories such as the Gene Expression Omnibus (GEO) contain many transcriptomics experiments that could be reused for downstream analyses such as validation and meta-analysis. However, reuse depends on identifying compatible experiments, which requires a clear representation of their experimental context. In practice, this context is often incompletely and inconsistently recorded across heterogeneous free-text metadata fields and linked publications. Although recent language model- and ontology-based methods have improved the extraction and normalisation of key experimental variables, it remains unclear whether their outputs are sufficiently complete and specific to assess compatibility for the intended reuse. We investigate this problem using human microarray studies from the GEO GPL570 platform, including a subset with manually curated, ontology-mapped annotations from the Gemma transcriptomics database. Using this curated subset, we evaluate extraction methods in terms of coverage, relevance, and experimental-group assignment: whether they recover the annotated experimental information, avoid biological details that are not relevant for reuse, and assign extracted variables to the experimental groups they describe. We then apply the resulting pipeline across the broader collection of GPL570 studies to examine where relevant information is reported and how often the documentation provides enough information to assess experiment compatibility. This work provides a foundation for developing and evaluating metadata-driven methods to identify compatible transcriptomics experiments and support scalable reuse of public transcriptomics data.

Co-authors: Kenneth Baillie, Ian Simpson