Fig. 1.

Types of big data in heart failure and the body location from which samples are taken for that data type. Omics and clinical data are the two common big data types to study HF. Clinical data can be gathered via wearables, imaging techniques, echocardiography (ECG), and electronic health records (EHRs). Different omics technologies primarily analyze cardiac tissue or blood and include genomics, transcriptomics, translatomics, proteomics, metabolomics, and lipidomics. Specimen can be studied at different resolutions, including bulk, single cell, single nucleus, and spatial level. To date the different tissue resolutions are not yet available for every omic. Data analysis is challenged by accuracy, structure, and volume of omics and clinical data. Traditional statistical as well as machine learning methods are employed to extract essential information to improve biological understanding and clinical care in HF.