Curated compendium of human transcriptional biomarker data

Abstract

Genome-wide transcriptional profiles provide broad insights into cellular activity. One important use of such data isto identify relationships between transcription levels and patient outcomes. These translational insights can guide the development of biomarkers for predicting outcomes in clinical settings. Over the past decades, data from many translational-biomarker studies have been deposited in public repositories, enabling other scientists to reuse the data in follow-up studies. However, data-reuse efforts require considerable time and expertise because transcriptional data are generated using heterogeneous profiling technologies, preprocessed using diverse normalization procedures, and annotated in non-standard ways. To address this problem, we curated a compendium of 45 translational-biomarker datasets from the public domain. To increase the datas utility, we reprocessed the raw expression data using a standard computational pipeline and standardized the clinical annotations in a fully reproducible manner (see osf.io/ssk3t). We believe these data will be particularly useful to researchers seeking to validate gene-level findings or to perform benchmarking studies--for example, to compare and optimize machine-learning algorithms ability to predict biomedical outcomes.