3.4. Spatial Normalization Methods
RPPA data quality depends strongly on the image quality obtained by the signal detection approach of choice. Certain detection methods, especially those comprising several working steps, can result in unevenly stained images caused by rim effects, for example. This spatial bias needs to be addressed by proper data analysis measures. The most obvious and most simple approach to tackle artifacts resulting from uneven staining or surface inhomogeneity is choosing a random sample distribution. However, in recent years, sophisticated methods were developed to improve RPPA data quality by co-printing of control spots.
In 2009, Anderson et al. [37] suggested to increase the statistical power by reducing the coefficient of variation so that variability resulting from spatial heterogeneity can be kept under control. This approach, termed “Array Microenvironment Normalization”, foresees printing a layout composed of an alternating checkerboard pattern of positive control spots and experimental sample spots. Controls were designed to match samples with respect to their total protein concentration as well as having a target protein concentration within the linear range of detection. Assuming that the relation of these concentrations is equal for all controls and independent from the position on the array, variations between individual control spots were attributed to spatial heterogeneity. Although the method improved the reproducibility of protein quantification, this approach is associated with a considerable increase of costs and efforts, as the number of samples that can be analyzed by RPPA is reduced.
The surface adjustment method developed by Neeley et al. [18] in 2012 requires a significantly lower number of control spots and relies on duplicate sample delivery in two differently defined printing patterns. This approach uses a generalized additive model to estimate a smoothed surface from which the positive control values are estimated for each spot of the array in relation to all other positive control spots. In case positive control spots were printed as dilution series, step-to-step differences can be used to perform an intensity-based adjustment by scaling each spot to the signal intensity of its immediate surface environment. With this method, a higher inter-slide reproducibility was obtained. However, the power of this approach was not directly compared with the one developed by Anderson and colleagues.
A recent approach from Kaushik et al. [38] accounts for spatial variability by using a simple bi-linear interpolation technique that yields a theoretical surface representing the spatial variation as basis for a calculation of correction factors. Inter-slide and intra-slide technical replicate agreement and intra-slide biological replicate agreement were determined in a 238-slide melanoma cell line study to evaluate this method. Intra-slide reproducibility of technical replicates was good and correlation between inter-slide replicates was high, however, the evaluation via correlation was not a good measure of data quality after normalization because variability between biological replicates can occur for other reasons besides surface inhomogeneity or signal detection artifacts.