We have developed a quantitative approach to characterizing the quality of individual dose combination matrices in a high throughput combinations screening program. This method, termed mQC, is in contrast to traditional plate level metrics commonly used in HTS. A manuscript describing the approach, along with benchmark and validation is available here. The abstract is given below.

Quality control (QC) metrics are critical in high throughput screening (HTS) platforms to ensure reliability and confidence in assay data and downstream analyses. Most 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

The R code and supporting data files are provided below:

If you use or extend this method, consider citing it as

Chen, L. et al., "mQC: A Heuristic Quality-Control Metric for High-Throughput Drug Combination Screening", Sci. Rep., 2016, 6:37741

If you have any questions please contact Lu Chen or Rajarshi Guha