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Compositional data network analysis via lasso penalized D-trace loss.

Huili Yuan1, Shun He1, Minghua Deng1,2

  • 1School of Mathematical Sciences, Peking University, Beijing, China.

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|June 21, 2019
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Summary
This summary is machine-generated.

We introduce CD-trace, a novel method for analyzing microbial community interactions from compositional data. CD-trace outperforms existing methods in recovering ecological association networks, offering a more reliable approach for microbiome research.

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Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • High-throughput sequencing of 16S-rRNA genes enables microbial community analysis.
  • Understanding direct microbial interactions is crucial for elucidating community structure mechanisms.
  • Analyzing compositional and high-dimensional microbiome data presents significant challenges.

Purpose of the Study:

  • To develop a novel loss function, CD-trace, for analyzing compositional microbiome data.
  • To establish a sparse matrix estimator for direct microbial interaction networks using CD-trace.
  • To evaluate the performance of CD-trace against existing methods for ecological association inference.

Main Methods:

  • Proposed a new loss function for compositional data, CD-trace, based on D-trace loss.
  • Defined a sparse matrix estimator by minimizing lasso penalized CD-trace loss under a positive-definite constraint.
  • Developed an efficient alternating direction algorithm for numerical computation.
  • Compared CD-trace with gCoda and sparse inverse covariance estimation for ecological association inference (SPIEC-EASI).

Main Results:

  • CD-trace demonstrated favorable performance compared to gCoda.
  • CD-trace outperformed SPIEC-EASI in network recovery with compositional data.
  • The method was validated using mouse skin microbiome data.

Conclusions:

  • CD-trace offers an effective approach for inferring direct microbial interaction networks from compositional data.
  • The proposed method provides improved network recovery compared to existing techniques.
  • CD-trace is available as open-source software for broader research application.