Selection of higher order regression models in the analysis of multi-factorial transcription data.

ABSTRACT:

Introduction

Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ.

Results

We set up four different linear regressi

SUBMITTER: Prazeres da Costa O 

PROVIDER: S-EPMC3962375 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature