Table 3.

Summary of approaches to normalize miRNA measurement data

Method (reference)	Compatible platforms	Approach	Strengths	Limitations	Dataset size
Endogenous RNA	qPCR/dPCR	Design probes to detect endogenous small RNAs within sample	Endogenous probes provide biological normalizer for sample input	Assumes consistent expression of controls	1–1000
	NGS	Normalize to some function of average signal per sample		Some biofluids may not contain control RNAs
	Microarray			Assumes equivalent probe detection to miRNAs
	Hybridization methods
Exogenous synthetic RNAs	qPCR/dPCR	Design probes to detect endogenous small RNAs within sample	Synthetic RNAs can be added at defined concentrations	Synthetic probe stability may vary in biological samples	1–1000
	NGS	Normalize to some function of average signal per sample	Enables loading controls for multiple assay steps	Chemical modifications can change detection performance
	Microarray
	Hybridization methods
Target mean normalization (E.g., geNorm, NormFinder, BestKeeper)	qPCR/dPCR	Determine normalization vector by most stable probes between samples	Identifies controls within uncharacterized samples	Technical variation must be removed before stability calculated	10–1000
	NGS			Requires miRNA expression to be stable between samples
	Microarray
	Hybridization methods
Sample mean normalization	qPCR/dPCR	Adjust per probe signal by overall sample mean	Effectively removes some sources of technical variation (e.g., sample input)	Requires most detected miRNA to behave similarly. Highly variant small miRNA signatures are harder to normalize	10–10000
	NGS
	Microarray
	Hybridization methods
Distribution adjustment	qPCR	Adjust per probe signal by overall sample mean (e.g., quantile normalization)	Can adjust for technical variation in amplification efficiency	Requires high number of probes for reliable estimation of distribution	~≥5000
	NGS

Abbreviations: dPCR, digital-based PCR; miRNA, microRNA; NGS, next generation sequencing; qPCR, quantitative polymerase chain reaction.