Table 1.

Potential limitations while designing multi-omics studies and possible strategies to overcome them.

Potential Limitations	Strategies to Overcome Limitation
Limited biomass of sample small sample or limited accessibility to sample (e.g., single cell, skin or saliva swab) [38]	Pooling Specific methods for small samples (e.g., methods for single cell omics)
Heterogeneity of cell type/ composition (e.g., microbiome community, whole organism, tissue or single cell). Proportions of multiple cell types in a sample can change substantially and shift omics profile [27].	Replication Adequate sample size (n) Homogenization Reference samples/data
Differences in specific biomolecules in sample types (e.g., urine may have many metabolites but very few proteins, DNA and RNA in comparison to blood, stool or tissue samples).	Choose appropriate sample for omics analysis or appropriate omics analyses for sample based on hypothesis.
Technical artifacts, including batch effects.	Reference and quality control (QC) samples Internal standards Randomization Appropriate statistical models (e.g., mixed effects) to account for batch effects [27]
Multiple testing loss of statistical power	Replication Adequate sample size
Background contamination (e.g., in a microbiome study stool samples will have host DNA, RNA, protein, and metabolites)	Specific methods depending on omics analysis and contamination (e.g., rRNA or host depletion for RNA extraction for meta-transcriptomics) Background control/reference samples
Differences in analytical platforms and integrating data sets of multi-omics that measure fundamentally different biomolecules	Standard control samples used across all omics data sets may help to harmonize measurements and variations