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Data Normalization

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The Value and Ease of 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