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Clustering of short time-course gene expression data with dissimilar replicates

  • Data Mining and Analytics
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Abstract

Microarrays are used in genetics and medicine to examine large numbers of genes simultaneously through their expression levels under any condition such as a disease of interest. The information from these experiments can be enriched by following the expression levels through time and biological replicates. The purpose of this study is to propose an algorithm which clusters the genes with respect to the similarities between their behaviors through time. The algorithm is also aimed at highlighting the genes which show different behaviors between the replicates and separating the constant genes that keep their baseline expression levels throughout the study. Finally, we aim to feature cluster validation techniques to suggest a sensible number of clusters when it is not known a priori. The illustrations show that the proposed algorithm in this study offers a fast approach to clustering the genes with respect to their behavior similarities, and also separates the constant genes and the genes with dissimilar replicates without any need for pre-processing. Moreover, it is also successful at suggesting the correct number of clusters when that is not known.

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Acknowledgements

The authors would like to thank the Academic Writing Center at Middle East Technical University for their writing consultation and editorial help.

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Author notes

  1. Ozlem Ilk and Cem Iyigun have contributed equally to this work.

Authors and Affiliations

  1. Department of Psychiatry and Neuropsychology, Maastricht University, Maastricht, The Netherlands

    Ozan Cinar

  2. Department of Statistics, Middle East Technical University, Ankara, Turkey

    Ozlem Ilk

  3. Department of Industrial Engineering, Middle East Technical University, Ankara, Turkey

    Cem Iyigun

Authors
  1. Ozan Cinar
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  2. Ozlem Ilk
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  3. Cem Iyigun
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Corresponding author

Correspondence to Ozan Cinar.

Appendix

Appendix

Irigoien et al. (2011) proposed a method to cluster the time-course gene expression levels. This procedure involves several steps, where the gene profiles are first standardized, and then the genes are filtered out if they have dissimilar replicates or constant patterns. The distances are measured with Procrustes statistics. The default Irigoien procedure is their methodology with Procrustes statistics. Furthermore, they embed their Procrustes statistics in k-means and compare this method with usual k-means algorithms with Euclidean and correlation distances.

ORIOGEN is a Java-based software package used to cluster time-course gene expression profiles (Peddada et al. 2005). ORIOGEN requires candidate gene profiles such as increasing or cyclical profiles. Next, each individual gene is tested against each profile by obtaining a goodness-of-fit statistic. According to the goodness-of-fit statistics each gene is assigned to one pattern which constitutes the clusters at the end of the algorithm.

EMMIX is another piece of software proposed by McLachlan et al. (1999) to fit a mixture model of multivariate normal or t-distributed components. This process is done with the Maximum Likelihood method and the optimum likelihood is obtained using an Expectation-Maximization (EM) algorithm. Two parameters for random effects and mixture effects can be used to specify the initial step of the EM algorithm. These two parameters can also be specified such that they can determine the clusters in which the subjects are assigned.

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Cinar, O., Ilk, O. & Iyigun, C. Clustering of short time-course gene expression data with dissimilar replicates. Ann Oper Res 263, 405–428 (2018). https://doi.org/10.1007/s10479-017-2583-3

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  • DOI: https://doi.org/10.1007/s10479-017-2583-3

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