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Cited by 721Computational Drug Discovery MethodsCell Image Analysis TechniquesOptimal Experimental Design Methods

Statistical practice in high-throughput screening data analysis

Malo, Nathalie, Hanley, James A, Cerquozzi, Sonia, Pelletier, Jerry, Nadon, Robert·2006·Nature Biotechnology
DOI 10.1038/nbt1186
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Abstract

High-throughput screening is an early critical step in drug discovery. Its aim is to screen a large number of diverse chemical compounds to identify candidate 'hits' rapidly and accurately. Few statistical tools are currently available, however, to detect quality hits with a high degree of confidence. We examine statistical aspects of data preprocessing and hit identification for primary screens. We focus on concerns related to positional effects of wells within plates, choice of hit threshold and the importance of minimizing false-positive and false-negative rates. We argue that replicate measurements are needed to verify assumptions of current methods and to suggest data analysis strategies when assumptions are not met. The integration of replicates with robust statistical methods in primary screens will facilitate the discovery of reliable hits, ultimately improving the sensitivity and specificity of the screening process.

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  • The robust Z-score is sensitive to shifts in median and commonly used as a normalization and strength of perturbation value in image-based phenotypic profiling, but it has not been used for estimating replicate dissimilarity nor compared to other distance metrics.

    “The robust Z-score is sensitive to shifts in median, and commonly used as a normalization and strength of perturbation value in the context of image-based phenotypic profiling, but it has not been used for estimating replicate dissimilarity, nor compared to other distance metrics12,30,37.”

    From A statistical framework for high-content phenotypic profiling using cellular feature distributions (2022) · cites this paper as [30]

  • Positional effects due to technical variability manifest as distinct spatial patterns across rows, columns and edges in different plates.

    “In contrast, positional effects due to technical variability manifest as distinct spatial patterns across the rows, columns and edges in different plates, a common challenge in multi-well-based assays30–33.”

    From A statistical framework for high-content phenotypic profiling using cellular feature distributions (2022) · cites this paper as [30]

  • Positional effects (edge effects, row/column dependencies, or gradient artifacts) are a persistent factor in multi-well assays and microarray experiments.

    “First, positional effects (edge effects, row/column dependencies, or gradient artifacts) are a persistent factor in multi-well assays and microarray experiments30,31,52.”

    From A statistical framework for high-content phenotypic profiling using cellular feature distributions (2022) · cites this paper as [30]

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Authors
Malo, N., Hanley, J. A., Cerquozzi, S., Pelletier, J., & Nadon, R.
Affiliations
  • McGill University and Génome Québec Innovation Centre (CA)
  • McGill University (CA)
Corresponding author
Nadon, Robert
ORCID
Venue
Nature Biotechnology
Publisher
Springer Science and Business Media LLC
Published
2006-02
Volume
24
Issue
2
Pages
167-175
ISSN
  • 0733-222X (ISSN-L)
  • 1087-0156
  • 1546-1696
DOI
10.1038/nbt1186
OpenAlex
W2097449851
Cited by
721
References
25
Citations by year
2026: 30 · 2025: 25 · 2024: 28 · 2023: 23 · 2022: 21 · 2021: 28 · 2020: 34 · 2019: 46 · 2018: 49 · 2017: 42 · 2016: 38 · 2015: 44 · 2014: 36 · 2013: 42 · 2012: 44
FWCI
33.54
Type
journal article
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)
Locations
  • Nature Biotechnology (publishedVersion)
  • PubMed (publishedVersion)
Topics
  • Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Biophysics › Cell Image Analysis Techniques
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Optimal Experimental Design Methods
Keywords
high-throughput screening, hit identification, data preprocessing, position effect, false positive rate, false negative rate, replicate measurements, robust statistical methods, drug discovery
MeSH terms
Biological Assay, Biometry, Data Interpretation, Statistical, Drug Evaluation, Preclinical, Sensitivity and Specificity, Drug Design, Reproducibility of Results, Guidelines as Topic, Gene Expression Profiling, Microarray Analysis

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