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High-throughput Screening for Chemical Modulators of Post-transcriptionally Regulated Genes
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Optimal design for high-throughput screening via false discovery rate control.

Tao Feng1, Pallavi Basu2, Wenguang Sun3

  • 1Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, California.

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|March 30, 2019
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Summary

This study introduces new statistical methods for high-throughput screening (HTS) to reduce errors and costs. The proposed two-stage procedure optimizes replicate numbers for better accuracy within budget constraints.

Keywords:
drug discoveryexperimental designfalse discovery rate controlhigh-throughput screeningtwo-stage design

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Area of Science:

  • Biostatistics
  • Drug Discovery
  • Computational Biology

Background:

  • High-throughput screening (HTS) involves multi-stage testing of numerous chemicals.
  • Conventional statistical methods in HTS face challenges with high error rates and costs.
  • Efficient error control and optimized experimental design are crucial for HTS success.

Purpose of the Study:

  • To develop novel methodologies for false discovery rate control in HTS.
  • To propose an optimal experimental design for multi-stage HTS studies.
  • To balance error control, detection power, and budgetary constraints in HTS.

Main Methods:

  • A two-stage statistical procedure for HTS is developed.
  • Methodology integrates false discovery rate control with optimal design.
  • The procedure determines optimal replication numbers across screening stages under a budget.

Main Results:

  • The proposed methods effectively control error rates in HTS.
  • The optimized design enhances detection power within a limited budget.
  • Simulated and real data validate the effectiveness of the new procedures.

Conclusions:

  • The developed methodologies offer a cost-effective approach to HTS.
  • The two-stage procedure improves statistical rigor and efficiency in HTS.
  • This work provides a framework for optimizing large-scale chemical screening processes.