Data Visualizations to Detect Systematic Errors in Laboratory Assay Results

Abstract

The measurement of concentrations of drugs and endogenous substances is widely used in basic and clinical pharmacology
research and service tasks. Using data science-derived visualizations of laboratory data, it is demonstrated on a real-life example
that basic statistical exploration of laboratory assay results or advised stan- dard visual methods of data inspection may fall
short in detecting systematic labora- tory errors. For example, data pathologies such as generating always the same value in all
probes of a particular assay run may pass undetected when using standard methods of data quality check. It is shown that the
use of different data visualiza- tions that emphasize different views of the data may enhance the detection of sys- tematic laboratory
errors. A dotplot of single data in the order of assay is proposed that provides an overview on the data range, outliers
and a particular type of sys- tematic errors where similar values are wrongly measured in all probes.