Sequenom, Inc. - Data Normalization

The primary goal of normalization is to compare quantitative expression data between different samples, experiments, and periods of study. It is important to account for variability in

RNA quality
Cellular input/RNA quantity
Reverse transcription efficiency
Pipetting inaccuracies
Endogenous/biological variance

Challenges of Current Methods

Use of total RNA fails to account for reverse transcription efficiency
Ribosomal RNA may differ during diverse biological states and is present in much greater amounts than the transcript of interest
Use of a single endogenous control gene may be subject to transcriptional changes as a result of the biological process

Solution

Data Normalization with MassARRAY® QGE & geNorm

Multiplex a panel of reference genes in a single reaction to determine the best candidates for data normalization
Easy-to-use Visual Basic Application
Over 650 citations have referenced the importance of data normalization using the geNorm technique