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A comprehensive evaluation of connectivity methods for L1000 data.

Kequan Lin1, Lu Li1, Yifei Dai2

  • 1School of Life Sciences, Tsinghua University, Beijing 100084, China.

Briefings in Bioinformatics
|November 28, 2019
PubMed
Summary

ZhangScore outperforms other methods for analyzing large-scale gene expression data, aiding drug discovery and mechanism of action studies. This tool helps researchers select optimal connectivity methods for gene expression profile analysis.

Keywords:
L1000ZhangScoreconnectivity mapconnectivity methodsdrug repurposingpartial area under the ROC

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Area of Science:

  • Computational biology
  • Bioinformatics
  • Pharmacology

Background:

  • Gene expression profiling is crucial for understanding drug mechanisms of action (MoAs) and identifying new drug indications.
  • The L1000-based Connectivity Map (CMap) dataset offers a significant scale-up for such analyses.
  • Previous evaluations of similarity assessment methods were based on smaller datasets and require re-evaluation for L1000 data.

Purpose of the Study:

  • To re-evaluate the performance of six popular gene expression similarity assessment methods using the large-scale L1000 CMap dataset.
  • To identify the most accurate method for predicting drug-drug relationships and understanding compound MoAs.
  • To provide guidelines for selecting appropriate connectivity methods in gene expression analysis.

Main Methods:

  • Six published gene expression similarity evaluation methods were assessed.
  • Performance was benchmarked against drug-drug similarities from the Drug Repurposing Hub database.
  • Prediction accuracy was measured using partial area under the receiver operating characteristic curve (AUC) at specific false positive rates (0.001, 0.005, 0.01).
  • Methods were further validated using an estrogen-related gene signature from breast cancer cells and a TOP2A knockdown gene signature.

Main Results:

  • ZhangScore demonstrated superior performance compared to other methods, achieving the highest accuracy for gene signature sizes between 10 and 200.
  • ZhangScore's accuracy was confirmed with experimental gene signatures, including estrogen-related and TOP2A knockdown signatures.
  • Analysis of the TOP2A knockdown signature using ZhangScore identified potential TOP2A inhibitors beyond known ones, with at least two previously investigated.

Conclusions:

  • ZhangScore is a highly accurate method for evaluating gene expression similarities, particularly with large datasets like L1000.
  • The findings offer practical guidance for researchers in selecting effective connectivity methods for drug discovery and MoA studies.
  • The study highlights the utility of ZhangScore in identifying novel drug candidates and repurposing opportunities.