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The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
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Evaluating single-subject study methods for personal transcriptomic interpretations to advance precision medicine.

Samir Rachid Zaim1,2,3, Colleen Kenost1,2, Joanne Berghout1,2,4

  • 1The Center for Biomedical Informatics & Biostatistics of the University of Arizona Health Sciences, 1230 N. Cherry Ave, Tucson, AZ, 85721, USA.

BMC Medical Genomics
|July 13, 2019
PubMed
Summary
This summary is machine-generated.

Precision medicine requires interpreting whole transcriptome expression for single subjects. An "all-against-one" framework effectively evaluates single-subject methods for identifying differentially expressed genes (DEGs), with an ensemble approach showing reliable accuracy.

Keywords:
Genomic medicineMedical genomicsN-of-1N-of-1 studiesPrecision medicineSingle-subject studiesTranscriptome

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Area of Science:

  • Genomics and Bioinformatics
  • Computational Biology
  • Molecular Biology

Background:

  • Gene expression profiling offers insights for medicine but lacks precision for individual variability.
  • Interpreting whole transcriptome expression is crucial for personalized risk assessment, diagnosis, and treatment.
  • Current methods struggle to integrate individual 'omics data for precise clinical decision-making.

Purpose of the Study:

  • To propose and evaluate an "all-against-one" framework for assessing single-subject (ss) differentially expressed gene (DEG) identification methods.
  • To compare the performance of various ss DEG analysis methods using biological replicates in isogenic conditions as reference standards.
  • To determine the most accurate and reliable ss DEG identification methods for precision medicine applications.

Main Methods:

  • Developed an "all-against-one" framework using biological replicates in isogenic conditions for ss DEG analysis.
  • Constructed reference standards (RSs) using five conventional replicate-anchored analyses (NOISeq, DEGseq, edgeR, DESeq, DESeq2).
  • Evaluated eight ss DEG methods (including NOISeq, DEGseq, edgeR, DESeq, DESeq2, mixture model, iDEG, and ensemble) against RSs in Yeast and MCF7 cell line datasets.

Main Results:

  • NOISeq, edgeR, and DESeq showed high concordance in creating reference standards.
  • Single-subject versions of NOISeq, DEGseq, and an ensemble learner achieved the highest median ROC area under the curve (>90% in Yeast, >0.75 in MCF7).
  • Performance varied among ss methods depending on the proportion of DEGs, indicating no single method is universally optimal.

Conclusions:

  • The "all-against-one" framework provides a robust evaluation for ss DEG studies.
  • The ss-ensemble method demonstrated reliable accuracy across tested conditions.
  • Further improvements are needed for ss DEG methods in paired samples to achieve higher precision and recall.