Table 2.

Comparative evaluation of single-cell and bulk ‘Omics’ approaches.This table systematically details the advantages and limitations of single-cell and bulk analysis methods across three major ‘omics’ disciplines: genomics, transcriptomics, and proteomics. The comparison highlights the unique strengths and challenges of each method, with particular emphasis on their ability to capture cellular heterogeneity, the depth and breadth of the molecular information provided, and technical considerations such as cost, data complexity, and analytical robustness in the context of biomedical research.

Omics Field	Analysis Type	Advantages	Limitations	Refs.
Genomics	Bulk	Lower cost; matured analytical methods; provides comprehensive sequence information.	Averages over cell populations; misses information about rare cell populations; limited prediction of the ultimate biological effect.	[19,22,23,24,25]
	Single cell	Detects mutations and structural variations in individual cells; highlights cell-to-cell heterogeneity and rare cell populations; enables study of intra-tumoral heterogeneity in cancer.	Requires substantial sequencing depth for accurate results; higher costs; greater complexity of data analysis; limited information on the ultimate biological effect.	[7,43,44,45]
Transcriptomics	Bulk	Lower cost; matured techniques and analytical methods; global expression analysis; detects all splice variants.	Averages over cell populations; misses cell-to-cell heterogeneity; only represents an intermediate step; correlation with protein levels is not always linear.	[32,57,58,59,82,83,84]
	Single cell	Captures cell-to-cell variability in gene expression; detects all splice variants; sensitive, high dynamic range, and quantitative; parses cell-specific transcriptomes in single-cell experiments.	Data can be noisy; more complex data analysis; only represents an intermediate step; correlation with protein levels is not always linear.	[107,110,116,117,118]
Proteomics	Bulk	Comprehensive coverage of the proteome; mature techniques; resolves the final regulatory level.	Averages over cell populations; less sensitivity to low-abundance proteins; certain proteins difficult to isolate; high dynamic range of proteome makes detection difficult.	[137,141,145,147]
	Single cell	Potential to capture protein-level heterogeneity across individual cells; proteins are the main effectors of cellular function.	Technically challenging; limited coverage of the proteome; less mature techniques; certain proteins difficult to isolate; high dynamic range of proteome makes detection difficult; post-translational modifications may greatly influence activity but can be challenging to analyze.	[160,161,162,163,170,175,180]