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CONSTANd removes the systematic effects induced by sample handling and measurement protocols

In context of omics experimentation, typically where a relative quantification of e.g. biological molecules is envisaged, the molecules are labeled prior to the analytical measurement. The use of labels is a very popular and convenient concept in multiple omics disciplines as they allow for multiplexing, such that, e.g., lipids, genes, metabolites, peptides or proteins from multiple biological samples are quantified simultaneous in one omics experiment. Simultaneous quantification of samples facilitates a direct statistical assessment of the differential biomolecules that are measured by the omics experiment as the biomolecular intensities are affected by the same amount of instrument variability.

Labeling, however, involves additional handling of the samples, prone to systematic effects at the level of the wet-lab. One of the most common handling errors, for example, are pipetting errors and, therefore, normalization methods are required to remove this systematic error. However, at present no dedicated normalization methods for labeled data are at hand; only the classical normalization techniques, already proposed for the standardization of microarray data, are in use. Latter normalization methods to remediate these inaccuracies proved to be insufficient, as they do not permit to fully correct for the systematic effects induced by sample handling and measurement protocols.

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