Single subject transcriptome analysis to identify functionally signed gene set or pathway activity.

Journal: Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
PMID: 29218900

Abstract

Analysis of single-subject transcriptome response data is an unmet need of precision medicine, made challenging by the high dimension, dynamic nature and difficulty in extracting meaningful signals from biological or stochastic noise. We have proposed a method for single subject analysis that uses a mixture model for transcript fold-change clustering from isogenically paired samples, followed by integration of these distributions with Gene Ontology Biological Processes (GO-BP) to reduce dimension and identify functional attributes. We then extended these methods to develop functional signing metrics for gene set process regulation by incorporating biological repressor relationships encoded in GO-BP as negatively_regulates edges. Results revealed reproducible and biologically meaningful signals from analysis of a single subject's response, opening the door to future transcriptomic studies where subject and resource availability are currently limiting. We used inbred mouse strains fed different diets to provide isogenic biological replicates, permitting rigorous validation of our method. We compared significant genotype-specific GO-BP term results for overlap and rank order across three replicate pairs per genotype, and cross-methods to reference standards (limma+FET, SAM+FET, and GSEA). All single-subject analytics findings were robust and highly reproducible (median area under the ROC curve=0.96, n=24 genotypes × 3 replicates), providing confidence and validation of this approach for analyses in single subjects. R code is available online at http://www.lussiergroup.org/publications/PathwayActivity.

Authors

  • Joanne Berghout
    Center for Biomedical Informatics and Biostatistics (CB2) & The Center for Applied Genetics and Genomic Medicine, Department of Medicine, University of Arizona, Tucson, AZ 85721, USA, jberghout@email.arizona.edu.
  • Qike Li
  • Nima Pouladi
  • Jianrong Li
    College of Food Science and Technology, Bohai University, Jinzhou, China.
  • Yves A Lussier
    Department of Medicine, The University of Arizona, Tucson, AZ, USA.

Keywords

External Resources

Popular Topics
Recent Journals