What to expect when performing a geNorm analysis?

geNorm is the most popular algorithm to determine the most stable reference (housekeeping) genes from a set of tested candidate reference genes in a sample panel. This is what we call a geNorm pilot experiment. In brief, geNorm calculates the gene expression stability measure M for a reference gene as the average pairwise variation V for that gene with all other tested reference genes.
Stepwise exclusion of the gene with the highest M value allows ranking of the tested genes according to their expression stability.

The underlying principles and calculations are described in Vandesompele et al., Genome Biology, 2002.

Results in qbase+

  1. A first chart (genorm M) indicates the average expression stability value of remaining reference genes at each step during stepwise exclusion of the least stable reference gene.
    Starting from the least stable gene at the left, the genes are ranked. (In the reference target stability window you see the values based on the calculation including all ref genes).
  2. A second chart (genorm V) indicates the pairwise variation V between two sequential normalization factors containing an increasing number of genes. A large variation means that the added gene has a significant effect and should preferably be included for calculation of a reliable normalization factor.
    We propose 0.15 as a cut-off value; below 0.15 the inclusion of an additional reference gene is not required. For example, if the V3/4 value is 0.22, then the normalization factor should preferably contain at least the 4 best reference genes.
    Subsequently, if the V4/5 value is 0.14, then there’s no real need to include a 5th gene in the normalization factor.

    Note: Please bear in mind that the proposed 0.15 value must not be taken as a too strict cut-off. The second graph is only intended to be guidance for determination of the optimal number of reference genes. Sometimes, the observed trend (of changing V values when using additional genes) can be equally informative.
    Anyway, ‘just’ using the 3 best reference genes (and ignoring this second graph) is in most cases a valid normalization strategy, and results in much more accurate and reliable normalization compared to the use of only one single reference gene.
  3. Expert report with recommendations on the number and identity of genes to be used for optimal normalization as well as information on the relative suitability of the selected genes.