Abstract
Quality control (QC) metrics are critical in high throughput screening (HTS) platforms to ensure reliability and confidence in assay data and downstream analyses. Most reported HTS QC metrics are designed for plate level or single well level analysis. With the advent of high throughput combination screening there is a need for QC metrics that quantify the quality of combination response matrices. We introduce a predictive, interpretable, matrix-level QC metric, mQC, based on a mix of data-derived and heuristic features. mQC accurately reproduces the expert assessment of combination response quality and correctly identifies unreliable response matrices that can lead to erroneous or misleading characterization of synergy. When combined with the plate-level QC metric, Z’, mQC provides a more appropriate determination of the quality of a drug combination screen. Retrospective analysis on a number of completed combination screens further shows that mQC is able to identify problematic screens whereas plate-level QC was not able to. In conclusion, our data indicates that mQC is a reliable QC filter that can be used to identify problematic drug combinations matrices and prevent further analysis on erroneously active combinations as well as for troubleshooting failed screens. The R source code of mQC is available at http://matrix.ncats.nih.gov/mQC.
Highlights
The development of high throughput screening platforms has necessitated the development of quality control (QC) measures to determine assay performance at various levels
To justify the development of a novel QC metric for combination screening, we compared the plate-level QC, Z’, and the expert opinions of matrix-level quality polled from 9 experienced scientists at National Center for Advancing Translational Sciences (NCATS) (Fig. 1A, Supplementary Dataset S1 and Supplementary Fig. S6)
In this article we have introduced a predictive, interpretable, matrix-level screening QC metric, matrix QC (mQC), based on heuristic features. mQC has the potential to serve as a QC filter for prioritizing drug combinations and a tool for troubleshooting failed combination screens
Summary
The development of high throughput screening platforms has necessitated the development of quality control (QC) measures to determine assay performance at various levels. Since controls are usually used for normalization of the sample area on the plate, poor control performance will lead to erroneous normalization and subsequently low quality assay readouts. Examples include edge effects[3,4] (due to evaporation from wells on the edge of a plate) and dispense errors Both these types of errors can manifest themselves in a signal that varies in a systematic fashion across rows or columns (or both) on a plate. The presence of spatial artifacts can be characterized using a variety of spatial autocorrelation metrics including Geary’s C6 and Moran’s I7 This does not apply to screens with intra-plate titrations or screens where samples from different, focused libraries are insufficiently randomized. The second class of QC parameters applies to sample level controls and report variability on biological responses in the assay throughout a screen. For small molecule high throughput screens, the Minimum Significant Ratio (MSR)[8] is probably the most widely used and characterizes the assay variability in terms of the variability of sample (or control compound) potencies
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