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High-dimensional genomic feature selection with the ordered stereotype logit model.

Anna Eames Seffernick1, Krzysztof Mrózek2,3, Deedra Nicolet2,3,4

  • 1Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA.

Briefings in Bioinformatics
|October 2, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian approach to analyze high-dimensional genomic data with ordinal outcomes, offering improved variable selection and uncertainty quantification for discrete biological variables.

Keywords:
acute myeloid leukemiahierarchical modelordinal responsevariable selection

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Area of Science:

  • Genomics and Bioinformatics
  • Statistical Modeling
  • Computational Biology

Background:

  • Many high-dimensional genomic and epigenomic datasets have ordinal outcomes, which can be truly discrete.
  • Existing frequentist methods for feature selection with ordinal outcomes have limitations.
  • The stereotype logistic model is suitable for assessed ordinal variables but requires a Bayesian framework for enhanced analysis.

Approach:

  • Reviewed the stereotype model and Bayesian variable selection methods.
  • Combined Bayesian variable selection with the stereotype model for high-dimensional genomic data.
  • Applied the Bayesian stereotype method to an acute myeloid leukemia (AML) RNA-sequencing dataset.

Key Points:

  • The Bayesian framework offers advantages in simultaneous uncertainty quantification and variable selection for ordinal genomic data.
  • Compared Bayesian and frequentist methods for variable selection performance.
  • Identified genomic features associated with the European LeukemiaNet prognostic risk score in AML.

Conclusions:

  • The Bayesian stereotype model effectively selects genomic features related to discrete ordinal outcomes.
  • This approach enhances the analysis of complex genomic datasets, particularly in cancer research.
  • Provides a robust method for feature selection in high-dimensional biological data with ordinal endpoints.