Universität zu Köln

da Costa, Olivia Prazeres, Hoffman, Arthur, Rey, Johannes W., Mansmann, Ulrich, Buch, Thorsten ORCID: 0000-0002-2236-9074 and Tresch, Achim (2014). Selection of Higher Order Regression Models in the Analysis of Multi-Factorial Transcription Data. PLoS One, 9 (3). SAN FRANCISCO: PUBLIC LIBRARY SCIENCE. ISSN 1932-6203

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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-c. 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.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code da Costa, Olivia Prazeres UNSPECIFIED UNSPECIFIED UNSPECIFIED Hoffman, Arthur UNSPECIFIED UNSPECIFIED UNSPECIFIED Rey, Johannes W. UNSPECIFIED UNSPECIFIED UNSPECIFIED Mansmann, Ulrich UNSPECIFIED UNSPECIFIED UNSPECIFIED Buch, Thorsten UNSPECIFIED orcid.org/0000-0002-2236-9074 UNSPECIFIED Tresch, Achim UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-443194
DOI:	10.1371/journal.pone.0091840
Journal or Publication Title:	PLoS One
Volume:	9
Number:	3
Date:	2014
Publisher:	PUBLIC LIBRARY SCIENCE
Place of Publication:	SAN FRANCISCO
ISSN:	1932-6203
Language:	English
Faculty:	Unspecified
Divisions:	Unspecified
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language YERSINIA-ENTEROCOLITICA; INFECTION; RESISTANT; NETWORK Multiple languages Multidisciplinary Sciences Multiple languages
URI:	http://kups.ub.uni-koeln.de/id/eprint/44319