Recall and bias of retrieving gene expression microarray datasets through PubMed identifiers

Abstract

The ability to locate publicly available gene expression microarray datasets effectively and efficiently facilitates the reuse of these potentially valuable resources. Centralized biomedical databases allow users to query dataset metadata descriptions, but these annotations are often too sparse and diverse to allow complex and accurate queries. In this study we examined the ability of PubMed article identifiers to locate publicly available gene expression microarray datasets, and investigated whether the retrieved datasets were representative of publicly available datasets found through statements of data sharing in the associated research articles. In a recent article, Ochsner and colleagues identified 397 studies that had generated gene expression microarray data. Their search of the full text of each publication for statements of data sharing revealed 203 publicly available datasets, including 179 in the Gene Expression Omnibus (GEO) or ArrayExpress databases. Our scripted search of GEO and ArrayExpress for PubMed identifiers of the same 397 studies returned 160 datasets, including six not found by the original search for data sharing statements. As a proportion of datasets found by either method, the search for data sharing statements identified 91.4% of the 209 publicly available datasets, compared to only 76.6% found by our search carried out using PubMed identifiers. Searching GEO or ArrayExpress alone retrieved 63.2% and 46.9% of all available datasets, respectively. There was no difference in the type of datasets found by PubMed identifier searches in terms of research theme or the technology used. However, the studies identified were more likely to have larger sample sizes, were more frequently cited, and published in higher impact journals. Searching database entries using PubMed identifiers can identify the majority of publicly available datasets, but caution is required when this method is used to collect data for policy evaluation since studies in low impact journals are disproportionately excluded. We urge authors of all datasets to complete the citation fields for their dataset submissions once publication details are known, thereby ensuring their work has maximum visibility and can contribute to subsequent studies.