Table 1.

Big data of types in heart failure. Many types of big data used in the study of HF are listed below along with a brief description. Data types specifically addressed in this review are in italics

Types of big data	Description	Examples in HF
Genomics
Genome-wide association study (GWAS)	Observational study testing the association of genome-wide common genetic variation with a trait in a population of individuals.	Reviewed in [6, 7]
Whole-genome sequencing (WGS)	Sequencing of the whole genome. Usually applied in the study of inherited disorders resulting in HF.	[8, 9]
Whole-exome sequencing (WES)	Sequencing of the exome (protein-coding portion) of the genome. Usually used to study forms of HF with known genetic etiologies.	Reviewed in [6]
Transcriptomics
Microarray	Quantification of RNA by fluorescence measurement of cDNA using chips. Limited to genes targeted by array chip.	[10, 11]
Bulk RNAseq	Quantification of RNA though sequencing of cDNA, alignment to reference genome, and counting.	[12, 13]
Single-cell RNAseq	Single cell or nucleus isolation prior to RNAseq	[14••]
Spatial transcriptomics	RNAseq performed on patches of tissue on slides	[15]
Proteomics	The study of proteins or peptides in a targeted or agnostic manner.	Reviewed in [16–18]
Metabolomics	The agnostic or targeted study of metabolites.	Reviewed in [19]
Lipidomics	The study of the complete or targeted lipid profile in an individual or population	[20, 21]
Wearables	An item worn externally that provides continuous data on parameters like heart rate, blood pressure, or fitness activity.	Reviewed in [22]
Clinical data
Electronic health records	Electronic data representing patients or patient groups produced for the purpose of managing clinical care	[23]
Imaging data	The process of creating visual representation of physiology. Examples include CT, MRI, echocardiography, EKG, X-ray.	Reviewed in [24]