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NExUS: Bayesian simultaneous network estimation across unequal sample sizes.

Priyam Das1, Christine B Peterson1, Kim-Anh Do1

  • 1Department of Biostatistics, TX 77030, USA.

Bioinformatics (Oxford, England)
|September 11, 2019
PubMed
Summary
This summary is machine-generated.

We developed NExUS, a Bayesian method to analyze proteomic networks across cancer subtypes with varying sample sizes. NExUS accurately estimates network differences, outperforming existing methods for heterogeneous population studies.

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Area of Science:

  • Computational biology
  • Systems biology
  • Genomics

Background:

  • Network-based analyses of high-throughput genomics data offer systems-level insights into biological mechanisms.
  • Estimating multiple networks across heterogeneous sub-populations with varying sample sizes presents challenges in accurate inference.
  • Differences in network characteristics may be confounded by statistical power variations due to unequal sample sizes, particularly in rare cancer subtypes.

Purpose of the Study:

  • To develop a robust computational method for joint learning of multiple biological networks from data with unequal sample sizes.
  • To address the challenge of artefactual relationships between sample size and network sparsity in comparative network analyses.
  • To apply the developed method to proteomic data from The Cancer Genome Atlas (TCGA) for related cancer types.

Main Methods:

  • Developed NExUS (Network Estimation across Unequal Sample sizes), a Bayesian statistical framework.
  • Implemented joint learning of multiple networks to account for varying sample sizes.
  • Validated performance through simulations comparing NExUS against existing network estimation techniques.

Main Results:

  • NExUS effectively learns multiple networks while mitigating the influence of sample size on network sparsity.
  • Simulations demonstrated superior performance of NExUS compared to conventional methods in scenarios with unequal sample sizes.
  • Applied NExUS to TCGA proteomic data to identify network similarities and shared pathway activities across related cancer groups.

Conclusions:

  • NExUS provides a reliable approach for comparative network analysis in heterogeneous biological data, especially with limited sample sizes for certain groups.
  • The method enhances the accuracy of inferring biological network differences across sub-populations.
  • Facilitates a deeper understanding of shared and distinct molecular mechanisms in related cancers using proteomic data.