A novel approach for biomarker selection and the integration of repeated measures experiments from two assays

Liquet, Benoit, Le Cao, Kim-Anh, Hocini, Hakim and Thiebaut, Rodolphe (2012) A novel approach for biomarker selection and the integration of repeated measures experiments from two assays. BMC Bioinformatics, 13 1: 325.1-325.14. doi:10.1186/1471-2105-13-325

Author Liquet, Benoit
Le Cao, Kim-Anh
Hocini, Hakim
Thiebaut, Rodolphe
Title A novel approach for biomarker selection and the integration of repeated measures experiments from two assays
Journal name BMC Bioinformatics   Check publisher's open access policy
ISSN 1471-2105
Publication date 2012-12
Year available 2012
Sub-type Article (original research)
DOI 10.1186/1471-2105-13-325
Open Access Status DOI
Volume 13
Issue 1
Start page 325.1
End page 325.14
Total pages 14
Place of publication London, United Kingdom
Publisher BioMed Central
Collection year 2013
Language eng
Formatted abstract
Background: High throughput 'omics' experiments are usually designed to compare changes observed between different conditions (or interventions) and to identify biomarkers capable of characterizing each condition. We consider the complex structure of repeated measurements from different assays where different conditions are applied on the same subjects.

Results: We propose a two-step analysis combining a multilevel approach and a multivariate approach to reveal separately the effects of conditions within subjects from the biological variation between subjects. The approach is extended to two-factor designs and to the integration of two matched data sets. It allows internal variable selection to highlight genes able to discriminate the net condition effect within subjects. A simulation study was performed to demonstrate the good performance of the multilevel multivariate approach compared to a classical multivariate method. The multilevel multivariate approach outperformed the classical multivariate approach with respect to the classification error rate and the selection of relevant genes. The approach was applied to an HIV-vaccine trial evaluating the response with gene expression and cytokine secretion. The discriminant multilevel analysis selected a relevant subset of genes while the integrative multilevel analysis highlighted clusters of genes and cytokines that were highly correlated across the samples.

Conclusions: Our combined multilevel multivariate approach may help in finding signatures of vaccine effect and allows for a better understanding of immunological mechanisms activated by the intervention. The integrative analysis revealed clusters of genes, that were associated with cytokine secretion. These clusters can be seen as gene signatures to predict future cytokine response. The approach is implemented in the R package mixOmics (http://cran.r-project.org/) with associated tutorials to perform the analysisa
Keyword Partial Least Squares
Canonical Correlation Analysis
Gene Expression
Microarray Experiments
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Non HERDC
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