An efficient method for the detection and elimination of systematic error in high-throughput screening

Makarenkov, Vladimir; Zentilli, Pablo; Kevorkov, Dmytro; Gagarin, Andrei; Malo, Nathalie; Nadon, Robert

doi:10.1093/bioinformatics/btm145

Cited by 88 publications

(92 citation statements)

References 12 publications

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“…Although diverse in silico methods have been proposed to identify hits (Makarenkov et al, 2007;Malo et al, 2006) and predict targets for chemicals [reviewed in (Kuhn et al, 2008)], only few of them are available as easy-to-use online tools (Keiser et al, 2007;Wang et al, 2012). To overcome this situation and assist in the analysis and interpretation of chemical phenotypic screens, we introduce HitPick, the first web server for hit identification and target prediction of chemical screenings.…”

Section: Introductionmentioning

confidence: 99%

HitPick: a web server for hit identification and target prediction of chemical screenings

et al. 2013

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Motivation: High-throughput phenotypic assays reveal information about the molecules that modulate biological processes, such as a disease phenotype and a signaling pathway. In these assays, the identification of hits along with their molecular targets is critical to understand the chemical activities modulating the biological system. Here, we present HitPick, a web server for identification of hits in highthroughput chemical screenings and prediction of their molecular targets. HitPick applies the B-score method for hit identification and a newly developed approach combining 1-nearest-neighbor (1NN) similarity searching and Laplacian-modified naïve Bayesian target models to predict targets of identified hits. The performance of the HitPick web server is presented and discussed. Availability: The server can be accessed at

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Section: Introductionmentioning

confidence: 99%

HitPick: a web server for hit identification and target prediction of chemical screenings

et al. 2013

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“…Only plate-specific bias is addressed by commonly used normalization methods such as Z-scores or methods which use internal controls (e.g., percentage inhibition or activation, percentage of control, and plate-median normalization). More recent methods have been developed that simultaneously address all three types of bias [1][2][3][4][5] with additional performance improvements provided by randomized study designs. 4 Current normalization methods take advantage of the fact that most primary-screen features (e.g., compounds and small interfering RNAs [siRNAs]) within each plate are inactive, which permits using robust estimates of row and column effects to remove systematic error.…”

Section: Introductionmentioning

confidence: 99%

Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features

Murie¹,

Barette²,

Lafanechère³

et al. 2014

SLAS Discovery

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Systematic error is present in all high-throughput screens, lowering measurement accuracy. Because screening occurs at the early stages of research projects, measurement inaccuracy leads to following up inactive features and failing to follow up active features. Current normalization methods take advantage of the fact that most primary-screen features (e.g., compounds) within each plate are inactive, which permits robust estimates of row and column systematic-error effects. Screens that contain a majority of potentially active features pose a more difficult challenge because even the most robust normalization methods will remove at least some of the biological signal. Control plates that contain the same feature in all wells can provide a solution to this problem by providing well-by-well estimates of systematic error, which can then be removed from the treatment plates. We introduce the robust control-plate regression (CPR) method, which uses this approach. CPR's performance is compared to a high-performing primary-screen normalization method in four experiments. These data were also perturbed to simulate screens with large numbers of active features to further assess CPR's performance. CPR performs almost as well as the best performing normalization methods with primary screens and outperforms the Z-score and equivalent methods with screens containing a large proportion of active features.

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“…However, the behavior could be parametrized using a technique derived from statistical moments. Systemic errors become more noticeable as they create border effects, which are systematically over-or underestimated [24]. The basic preprocessing steps are usually baseline removal and data smoothing [4].…”

Section: Introductionmentioning

confidence: 99%

Unsupervised adaptive filter for baseline thresholding and elimination in liquid chromatography-mass spectrometry via approximation of the standard deviation of baseline distribution in retention time domain

Urban

Vanĕk

Štys

2013

Acta Chromatographica

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Summary.The omitting presence of a baseline (background, systemic noise, mobile phase) in liquid chromatography-mass spectrometry (LC-MS) measurements impedes objective analysis. Therefore, there is a demand to remove its contribution from the signal response. Elimination of baseline contribution is justified by increasing the data mining output, both qualitatively and quantitatively. Behavior of the baseline content is not perfectly constant on the time axis, and it is often necessary to experiment with gradient changes. However, the behavior could be parametrized using a technique derived from statistical moments. In this article, we propose adaptive thresholding as an unsupervised method for baseline removal from the measurement data. Results of a real analyte measurement are discussed to illustrate its efficiency.

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An efficient method for the detection and elimination of systematic error in high-throughput screening

Abstract: Supplementary data are available at Bioinformatics online.

Cited by 88 publications

References 12 publications

HitPick: a web server for hit identification and target prediction of chemical screenings

HitPick: a web server for hit identification and target prediction of chemical screenings

Control-Plate Regression (CPR) Normalization for High-Throughput Screens with Many Active Features

Unsupervised adaptive filter for baseline thresholding and elimination in liquid chromatography-mass spectrometry via approximation of the standard deviation of baseline distribution in retention time domain

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