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SLAS Discov. 2018 Jun;23(5):448-458. doi: 10.1177/2472555217750377. Epub 2018 Jan 18.

Identification and Correction of Additive and Multiplicative Spatial Biases in Experimental High-Throughput Screening.

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1 Département d'Informatique, Université du Québec à Montréal, Montréal, QC, Canada.
2 McGill University and Genome Quebec Innovation Centre, Montréal, QC, Canada.
3 Department of Human Genetics, McGill University, Montréal, QC, Canada.


Data generated by high-throughput screening (HTS) technologies are prone to spatial bias. Traditionally, bias correction methods used in HTS assume either a simple additive or, more recently, a simple multiplicative spatial bias model. These models do not, however, always provide an accurate correction of measurements in wells located at the intersection of rows and columns affected by spatial bias. The measurements in these wells depend on the nature of interaction between the involved biases. Here, we propose two novel additive and two novel multiplicative spatial bias models accounting for different types of bias interactions. We describe a statistical procedure that allows for detecting and removing different types of additive and multiplicative spatial biases from multiwell plates. We show how this procedure can be applied by analyzing data generated by the four HTS technologies (homogeneous, microorganism, cell-based, and gene expression HTS), the three high-content screening (HCS) technologies (area, intensity, and cell-count HCS), and the only small-molecule microarray technology available in the ChemBank small-molecule screening database. The proposed methods are included in the AssayCorrector program, implemented in R, and available on CRAN.


Anderson–Darling test; Cramer–von Mises test; Mann–Whitney U test; data correction; high-content screening; high-throughput screening; partial mean polish; small-molecule microarray; spatial bias


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