TY  - JOUR
A1  - Prazeres da Costa, Olivia
A1  - Hoffman, Arthur
A1  - Rey, Johannes W.
A1  - Mansmann, Ulrich
A1  - Buch, Thorsten
A1  - Tresch, Achim
T1  - Selection of higher order regression models in the analysis of multi-factorial transcription data
T2  - PLoS One
N2  - 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 regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes.
Conclusions: We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.
Y1  - 2014
UR  - http://publikationen.ub.uni-frankfurt.de/frontdoor/index/index/docId/33318
UR  - https://nbn-resolving.org/urn:nbn:de:hebis:30:3-333183
SN  - 1932-6203
N1  - Copyright: © 2014 Prazeres da Costa et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
VL  - 9
IS  - (3):e91840
PB  - PLoS
CY  - Lawrence, Kan.
ER  -