Data heterogeneity refers to the variability and diversity of data types, sources, and structures that can arise in biological research. This concept is crucial when integrating multi-omics data, as it highlights the challenge of combining information from various omics layers—like genomics, transcriptomics, proteomics, and metabolomics—each of which may have different formats, scales, and levels of complexity. Understanding data heterogeneity helps researchers recognize the limitations and considerations necessary for meaningful analysis and interpretation across these diverse data sets.
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