MOTIVATION: Considerable attention has been paid recently to improve data quality in high-throughput screening (HTS) and high-content screening (HCS) technologies widely used in drug development and chemical toxicity research. However, several environmentally- and procedurally-induced spatial biases in experimental HTS and HCS screens decrease measurement accuracy, leading to increased numbers of false positives and false negatives in hit selection. Although effective bias correction methods and software have been developed over the past decades, almost all of these tools have been designed to reduce the effect of additive bias only. Here, we address the case of multiplicative spatial bias. RESULTS: We introduce three new statistical methods meant to reduce multiplicative spatial bias in screening technologies. We assess the performance of the methods with synthetic and real data affected by multiplicative spatial bias, including comparisons with current bias correction methods. We also describe a wider data correction protocol that integrates methods for removing both assay and plate-specific spatial biases, which can be either additive or multiplicative. CONCLUSIONS: The methods for removing multiplicative spatial bias and the data correction protocol are effective in detecting and cleaning experimental data generated by screening technologies. As our protocol is of a general nature, it can be used by researchers analyzing current or next-generation high-throughput screens. AVAILABILITY AND IMPLEMENTATION: The AssayCorrector program, implemented in R, is available on CRAN. CONTACT: [email protected]. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Significant efforts have been made recently to improve data throughput and data quality in screening technologies related to drug design. The modern pharmaceutical industry relies heavily on high-throughput screening (HTS) and high-content screening (HCS) technologies, which include small molecule, complementary DNA (cDNA) and RNA interference (RNAi) types of screening. Data generated by these screening technologies are subject to several environmental and procedural systematic biases, which introduce errors into the hit identification process. We first review systematic biases typical of HTS and HCS screens. We highlight that study design issues and the way in which data are generated are crucial for providing unbiased screening results. Considering various data sets, including the publicly available ChemBank data, we assess the rates of systematic bias in experimental HTS by using plate-specific and assay-specific error detection tests. We describe main data normalization and correction techniques and introduce a general data preprocessing protocol. This protocol can be recommended for academic and industrial researchers involved in the analysis of current or next-generation HTS data.