A model-based clustering via mixture of hierarchical models with covariate adjustment for detecting differentially expressed genes from paired design.

ABSTRACT: The causes of many complex human diseases are still largely unknown. Genetics plays an important role in uncovering the molecular mechanisms of complex human diseases. A key step to characterize the genetics of a complex human disease is to unbiasedly identify disease-associated gene transcripts on a whole-genome scale. Confounding factors could cause false positives. Paired design, such as measuring gene expression before and after treatment for the same subject, can reduce the effect of known confounding factors. However, not all known confounding factors can be controlled in a paired/match design. Model-based clustering, such as mixtures of hierarchical models, has been proposed to detect gene transcripts differentially expressed between paired samples. To the best of our knowledge, no

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC10633962 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature