Systematic tissue annotations of genomics samples by modeling unstructured metadata

• Published in: Nature communications Vol. 13; no. 1; pp. 6736 - 13
• Main Authors: Hawkins, Nathaniel T., Maldaver, Marc, Yannakopoulos, Anna, Guare, Lindsay A., Krishnan, Arjun
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 08.11.2022; Nature Publishing Group; Nature Portfolio
• Subjects: 38; 45; 631/114/1305; 631/114/2164; 631/114/2401; 631/1647/514; Annotations; Biological activity; Classification; Data collection; Descriptions; Gene expression; Genomics; Humanities and Social Sciences; Humans; Language; Learning algorithms; Machine Learning; Metadata; multidisciplinary; Natural language; Natural Language Processing; Representations; Science; Science (multidisciplinary); Signal processing; String matching; Tissues; Unstructured data
• ISSN: 2041-1723; 2041-1723
• DOI: 10.1038/s41467-022-34435-x

There are currently >1.3 million human –omics samples that are publicly available. This valuable resource remains acutely underused because discovering particular samples from this ever-growing data collection remains a significant challenge. The major impediment is that sample attributes are routinely described using varied terminologies written in unstructured natural language. We propose a natural-language-processing-based machine learning approach (NLP-ML) to infer tissue and cell-type annotations for genomics samples based only on their free-text metadata. NLP-ML works by creating numerical representations of sample descriptions and using these representations as features in a supervised learning classifier that predicts tissue/cell-type terms. Our approach significantly outperforms an advanced graph-based reasoning annotation method (MetaSRA) and a baseline exact string matching method (TAGGER). Model similarities between related tissues demonstrate that NLP-ML models capture biologically-meaningful signals in text. Additionally, these models correctly classify tissue-associated biological processes and diseases based on their text descriptions alone. NLP-ML models are nearly as accurate as models based on gene-expression profiles in predicting sample tissue annotations but have the distinct capability to classify samples irrespective of the genomics experiment type based on their text metadata. Python NLP-ML prediction code and trained tissue models are available at https://github.com/krishnanlab/txt2onto . The 1+ million publicly-available human –omics samples currently remain acutely underused. Here the authors present an approach combining natural language processing and machine learning to infer the source tissue of public genomics samples based on their plain text descriptions, making these samples easy to discover and reuse.