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The use of mixed models to identify differentially expressed genes when a single replicate per biological condition is present

Author
Organization
Abstract
In microarray data analysis, selection of biologically relevant genes are essential tasks. In this study, we propose a novel statistical procedure based on a linear mixed-effects model to identify the differentially expressed genes in a preprocessed cDNA microarray experiments. This method uses standardized conditional residuals to perform gene-specific comparisons of experimental conditions. The novelty of this approach is that it enables hypothesis testing when only a single replicate per experimental condition is present. This method accommodates a wide variety of experimental designs and can simultaneously assess differences between multiple types of biological samples. Interestingly, the method can be applied for cDNA as well as oligonucleotide microarray experiments. The method is illustrated using two publicly available gene expression datasets. Simulations show that the tests developed here control the Type-I error and have enough power to detect biologically important genes.
Keywords
molecular biophysics, bioinformatics, data analysis

Citation

Please use this url to cite or link to this publication:

MLA
Thilakarathne, PJ, G Verbeke, K Engelen, et al. “The Use of Mixed Models to Identify Differentially Expressed Genes When a Single Replicate Per Biological Condition Is Present.” Proceedings of the 2009 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2009. Ed. HR Arabnia & MQ Yang. Las Vegas, NV, USA: CSREAPpress, 2009. 186–190. Print.
APA
Thilakarathne, P., Verbeke, G., Engelen, K., & Marchal, K. (2009). The use of mixed models to identify differentially expressed genes when a single replicate per biological condition is present. In H. Arabnia & M. Yang (Eds.), Proceedings of the 2009 international conference on bioinformatics and computational biology, BIOCOMP 2009 (pp. 186–190). Presented at the 2009 International conference on Bioinformatics and Computational Biology (BIOCOMP 2009), Las Vegas, NV, USA: CSREAPpress.
Chicago author-date
Thilakarathne, PJ, G Verbeke, K Engelen, and Kathleen Marchal. 2009. “The Use of Mixed Models to Identify Differentially Expressed Genes When a Single Replicate Per Biological Condition Is Present.” In Proceedings of the 2009 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2009, ed. HR Arabnia and MQ Yang, 186–190. Las Vegas, NV, USA: CSREAPpress.
Chicago author-date (all authors)
Thilakarathne, PJ, G Verbeke, K Engelen, and Kathleen Marchal. 2009. “The Use of Mixed Models to Identify Differentially Expressed Genes When a Single Replicate Per Biological Condition Is Present.” In Proceedings of the 2009 International Conference on Bioinformatics and Computational Biology, BIOCOMP 2009, ed. HR Arabnia and MQ Yang, 186–190. Las Vegas, NV, USA: CSREAPpress.
Vancouver
1.
Thilakarathne P, Verbeke G, Engelen K, Marchal K. The use of mixed models to identify differentially expressed genes when a single replicate per biological condition is present. In: Arabnia H, Yang M, editors. Proceedings of the 2009 international conference on bioinformatics and computational biology, BIOCOMP 2009. Las Vegas, NV, USA: CSREAPpress; 2009. p. 186–90.
IEEE
[1]
P. Thilakarathne, G. Verbeke, K. Engelen, and K. Marchal, “The use of mixed models to identify differentially expressed genes when a single replicate per biological condition is present,” in Proceedings of the 2009 international conference on bioinformatics and computational biology, BIOCOMP 2009, Las Vegas, NV, USA, 2009, pp. 186–190.
@inproceedings{3191393,
  abstract     = {In microarray data analysis, selection of biologically relevant genes are essential tasks. In this study, we propose a novel statistical procedure based on a linear mixed-effects model to identify the differentially expressed genes in a preprocessed cDNA microarray experiments. This method uses standardized conditional residuals to perform gene-specific comparisons of experimental conditions. The novelty of this approach is that it enables hypothesis testing when only a single replicate per experimental condition is present. This method accommodates a wide variety of experimental designs and can simultaneously assess differences between multiple types of biological samples. Interestingly, the method can be applied for cDNA as well as oligonucleotide microarray experiments. The method is illustrated using two publicly available gene expression datasets. Simulations show that the tests developed here control the Type-I error and have enough power to detect biologically important genes.},
  author       = {Thilakarathne, PJ and Verbeke, G and Engelen, K and Marchal, Kathleen},
  booktitle    = {Proceedings of the 2009 international conference on bioinformatics and computational biology, BIOCOMP 2009},
  editor       = {Arabnia, HR and Yang, MQ},
  isbn         = {9781601320933},
  keywords     = {molecular biophysics,bioinformatics,data analysis},
  language     = {eng},
  location     = {Las Vegas, NV, USA},
  pages        = {186--190},
  publisher    = {CSREAPpress},
  title        = {The use of mixed models to identify differentially expressed genes when a single replicate per biological condition is present},
  year         = {2009},
}

Web of Science
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