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

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 sig

SUBMITTER: Berghout J 

PROVIDER: S-EPMC5730358 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature