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mQC: A Heuristic Quality-Control Metric for High-Throughput Drug Combination Screening

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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.

No MeSH data available.


Drift effect identified by mQC.Each plot represents an independent screen consisting of 14 plates. The point in the upper part of plot represents the median response of matrix-level negative control (DMSO) on ith column. The point in the upper part of plot represents the proportion of “Good” response matrices on ith column, according to mQC assessment.
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f7: Drift effect identified by mQC.Each plot represents an independent screen consisting of 14 plates. The point in the upper part of plot represents the median response of matrix-level negative control (DMSO) on ith column. The point in the upper part of plot represents the proportion of “Good” response matrices on ith column, according to mQC assessment.

Mentions: Drift is one of the systematic sources of variability that cannot be easily identified by Z’. HTS guideline suggests scatterplots to diagnose layout-dependent responses, but this can be infeasible for large scale cHTS due to different layout of dose combinations. Figure 7 and Table 2 showed four screens (assay ID 702, 703, 704, 705) from which we have observed significant left-to-right drift effects. In these cases, Z’ failed to identify such drift effect because the negative and positive controls were placed at the left four columns (see Supplementary Dataset S2 for plate layout and Supplementary Fig. S5H–K for QC summary). mQC which assesses the negative control and variation of does responses in the response matrices, on the other hand, have successfully flagged these four screens for violation of “screenings containing >60% Good response matrices and >90% Good or Medium response matrices” criteria. We found that the proportion of the “Good” response matrices correlated with the drift trend across the columns (lower plots in Fig. 7).


mQC: A Heuristic Quality-Control Metric for High-Throughput Drug Combination Screening
Drift effect identified by mQC.Each plot represents an independent screen consisting of 14 plates. The point in the upper part of plot represents the median response of matrix-level negative control (DMSO) on ith column. The point in the upper part of plot represents the proportion of “Good” response matrices on ith column, according to mQC assessment.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5121902&req=5

f7: Drift effect identified by mQC.Each plot represents an independent screen consisting of 14 plates. The point in the upper part of plot represents the median response of matrix-level negative control (DMSO) on ith column. The point in the upper part of plot represents the proportion of “Good” response matrices on ith column, according to mQC assessment.
Mentions: Drift is one of the systematic sources of variability that cannot be easily identified by Z’. HTS guideline suggests scatterplots to diagnose layout-dependent responses, but this can be infeasible for large scale cHTS due to different layout of dose combinations. Figure 7 and Table 2 showed four screens (assay ID 702, 703, 704, 705) from which we have observed significant left-to-right drift effects. In these cases, Z’ failed to identify such drift effect because the negative and positive controls were placed at the left four columns (see Supplementary Dataset S2 for plate layout and Supplementary Fig. S5H–K for QC summary). mQC which assesses the negative control and variation of does responses in the response matrices, on the other hand, have successfully flagged these four screens for violation of “screenings containing >60% Good response matrices and >90% Good or Medium response matrices” criteria. We found that the proportion of the “Good” response matrices correlated with the drift trend across the columns (lower plots in Fig. 7).

View Article: PubMed Central - PubMed

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.

No MeSH data available.