Table 4 Method for analysis of control values: Calibration curve

Calibration curve

 A typical calibration process for a single-analyte assay might involve running a series of reference standard samples with known values of the target analyte followed by construction of a calibration curve. This curve can then be inverted to produce a mathematical correction that is applied to the raw measured values from the test samples. A multiplicative factor applied to all raw assays values is a simple example of a correction

 In the setting of HDD such as omics data, it would be infeasible to construct a separate calibration curve for every analyte measured by the assay. Instead, calibration approaches used for omics assays typically rely on corrections derived either from a small subset of the analytes measured by the assay platform or on assumptions about the global distribution of the measured values across all analytes measured. An example of the subset approach in the context of gene expression arrays is the calculation of a mean over a small set of so-called “housekeeper genes”, whose expression levels are expected to be roughly constant across all samples being analyzed. This mean is compared to a specified reference value to generate a multiplicative factor specific to each sample, which is then applied globally across the expression data for all genes measured for the sample. Figure 5 [37] shows several examples of calibration curves