PMC:4996411 / 12586-15558 JSONTXT

{"project":"2_test","denotations":[{"id":"27600238-22761696-69479217","span":{"begin":2610,"end":2612},"obj":"22761696"},{"id":"27600238-22467912-69479218","span":{"begin":2968,"end":2970},"obj":"22467912"}],"text":"3.1. Experimental Design\nSuccessful biomarker discovery requires a careful reflection of all aspects that might play a role during data analysis. Key points comprise the experimental design that was chosen to approach the clinical question at hand. Thus, a definition of quality control (QC) measures and control samples meaningful for data analysis is required to analyze RPPA data. Using the same types of controls is also necessary to compare RPPA data across different RPPA platforms.\nAs RPPA enables a relative quantification of proteins in large sample sets, experimental effects that might take influence on raw data must be taken into account such as the dynamic range of measurements or spatial effects that might result from staining artifacts of the signal detection approach. In addition, sample loading has to match the signal detection range. To compensate experimental noise, for example, normalization approaches were developed which require additionally printed spots, such as sample dilution series or so-called loading control spots to account for uneven staining. All normalization methods require an array design that comprises printing of control samples and technical replicates to gain statistically relevant results. Apart from statistical aspects, the information inherent to replicate spots serves several other functions that become important during data analysis: on the one hand, it presents the basis to apply certain normalization methods, on the other hand, it also facilitates data comparability with already existing RPPA data sets, provided that the same controls were used.\nFor RPPA, two fundamentally different approaches exist. The first one is based on printing each sample as serial dilution, thus simultaneously providing a sufficient number of data points for downstream data analysis. Samples can also be printed in a single concentration which may be the approach of choice when the sample volume is too limited to allow for the preparation of serial dilutions, for example when working with scarce patient material. In this instance, it is highly important to choose a method for signal detection with a low experimental noise since this negatively impacts on data quality. However, co-printing serial dilutions of meaningful controls is also required in case samples are supposed to be printed in a single concentration to calibrate the signals obtained by the actual samples as realized in the second approach for RPPA. In this instance, samples are printed as three technical replicate spots to balance statistical power and to guarantee optimal spatial usage [22,28]. Relative protein quantification approaches, as commonly employed in RPPA, can also benefit from not simply aggregating sample replicates but using the information of individual replicate spots as measure of within-sample variability, as realized by the non-parametric estimation of protein expression levels by Li and coworkers as the Reno-approach [31]."}