Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies

Mazoure, Bogdan; Nadon, Robert

doi:10.1038/s41598-017-11940-4

Cited by 17 publications

(13 citation statements)

References 26 publications

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“…In the case of dose-response data, metrology focuses on variability among technical and biological replicates, assessment of edge effects, and outlier detection. Edge effects and other spatial artifacts can be identified by statistical analysis ( Mazoure et al, 2017 ) and plate-wise data visualization ( Boutros et al, 2006 ). Spatial artifacts can then be removed with plate-level normalization such as LOESS/LOWESS smoothing ( Boutros et al, 2006 ; Pelz et al, 2010 ), spatial autocorrelation ( Lachmann et al, 2016 ), or statistical modeling ( Mazoure et al, 2017 ).…”

Section: Discussionmentioning

confidence: 99%

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines

et al. 2019

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Section: Discussionmentioning

confidence: 99%

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines

et al. 2019

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“…In the case of dose-response data, metrology focuses on variability among technical and biological replicates, assessment of edge effects, and outlier detection. Edge effects and other spatial artifacts can be identified by statistical analysis (Mazoure et al, 2017) and plate-wise data visualization (Boutros et al, 2006). Spatial artifact can then be removed with plate-level normalization such as LOESS/LOWESS smoothing (Boutros et al, 2006;Pelz et al, 2010), spatial autocorrelation (Lachmann et al, 2016), or statistical modeling (Mazoure et al, 2017).…”

Section: Elements Of a Reproducible Workflowmentioning

confidence: 99%

A multi-center study on factors influencing the reproducibility ofin vitrodrug-response studies

Niepel

Hafner

Williams

et al. 2017

Preprint

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Evidence that some influential biomedical results cannot be recapitulated has increased calls for data that is findable, accessible, interoperable, and reproducible (FAIR). Here, we study factors influencing the reproducibility of a prototypical cell-based assay: responsiveness of cultured cell lines to anti-cancer drugs. Such assays are important for drug development, mechanism of action studies, and patient stratification. This study involved seven research centers comprising the NIH LINCS Program Consortium, which aims to systematically characterize the responses of human cells to perturbation by gene disruption, small molecule drugs, and components of the microenvironment. We found that factors influencing the measurement of drug response vary substantially with the compound being analyzed and thus, the underlying biology. For example, substitution of a surrogate assay such as CellTiter-Glo® for direct microscopy-based cell counting is acceptable in the case of neratinib or alpelisib, but not palbociclib or etoposide. Uncovering and controlling for such context sensitivity requires systematic measurement of assay robustness in the face of biological variation, which is distinct from assay precision and sensitivity. Conversely, validating assays only over a narrow range of conditions has the potential to introduce serious systematic error in a large dataset spanning many compounds and cell lines.

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“…Practitioners of HTS have learned to recognize a wide range of chemical and physical processes leading to apparent activity in HTS assays 13 . The science of active compound detection and corresponding statistical practice developed early 14,15 with many subsequent refinements [16][17][18][19] . The underpinning instrumental technologies of HTS have been influential for increasing the scale achievable in routine laboratory work and these technologies are widely deployed in the form of plate readers, lab robotics, and compound libraries accessible to researchers 20 .…”

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confidence: 99%

Statistical models for identifying frequent hitters in high throughput screening

Goodwin

Shahtahmassebi

Hanley

2020

Sci Rep

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High throughput screening (HTS) interrogates compound libraries to find those that are “active” in an assay. To better understand compound behavior in HTS, we assessed an existing binomial survivor function (BSF) model of “frequent hitters” using 872 publicly available HTS data sets. We found large numbers of “infrequent hitters” using this model leading us to reject the BSF for identifying “frequent hitters.” As alternatives, we investigated generalized logistic, gamma, and negative binomial distributions as models for compound behavior. The gamma model reduced the proportion of both frequent and infrequent hitters relative to the BSF. Within this data set, conclusions about individual compound behavior were limited by the number of times individual compounds were tested (1–1613 times) and disproportionate testing of some compounds. Specifically, most tests (78%) were on a 309,847-compound subset (17.6% of compounds) each tested ≥ 300 times. We concluded that the disproportionate retesting of some compounds represents compound repurposing at scale rather than drug discovery. The approach to drug discovery represented by these 872 data sets characterizes the assays well by challenging them with many compounds while each compound is characterized poorly with a single assay. Aggregating the testing information from each compound across the multiple screens yielded a continuum with no clear boundary between normal and frequent hitting compounds.

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Identification and correction of spatial bias are essential for obtaining quality data in high-throughput screening technologies

Cited by 17 publications

References 26 publications

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines

A Multi-center Study on the Reproducibility of Drug-Response Assays in Mammalian Cell Lines

A multi-center study on factors influencing the reproducibility ofin vitrodrug-response studies

Statistical models for identifying frequent hitters in high throughput screening

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