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High dimensional extension of the growth curve model and its application in genetics

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Abstract

Recent advances in technology have allowed researchers to collect large scale complex biological data, simultaneously, often in matrix format. In genomic studies, for instance, measurements from tens to hundreds of thousands of genes are taken from individuals across several experimental groups. In time course microarray experiments, gene expression is measured at several time points for each individual across the whole genome resulting in a high-dimensional matrix for each gene. In such experiments, researchers are faced with high-dimensional longitudinal data. Unfortunately, traditional methods for longitudinal data are not appropriate for high-dimensional situations. In this paper, we use the growth curve model and introduce test useful for high-dimensional longitudinal data and evaluate its performance using simulations. We also show how our approach can be used to filter genes in time course genomic experiments. We illustrate this using publicly available genomic data, involving experiments comparing normal human lung tissue with vanadium pentoxide treated human lung tissue, designed with the aim of understanding the susceptibility of individuals working in petro-chemical factories to airway re-modelling. Using our method, we were able to filter out 1053 (about 5 %) genes as non-noise genes from a pool of  22,277. Although our focus is on hypothesis testing, we also provided modified maximum likelihood estimator for the mean parameter of the growth curve model and assessed its performance through bias and mean squared error.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, McMaster University, Hamilton, Canada

    Sayantee Jana, Narayanaswamy Balakrishnan & Jemila Seid Hamid

  2. Department of Energy and Technology, Swedish Agricultural University, Uppsala, Sweden

    Dietrich von Rosen

  3. Department of Mathematics, Linköping University, Linköping, Sweden

    Dietrich von Rosen

  4. Li Ka Shing Knowledge Institute, St. Michael’s Hospital, Toronto, ON, Canada

    Jemila Seid Hamid

  5. Department Clinical Epidemiology and Biostatistics, McMaster University, Hamilton, ON, Canada

    Jemila Seid Hamid

Authors
  1. Sayantee Jana
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  2. Narayanaswamy Balakrishnan
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  3. Dietrich von Rosen
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  4. Jemila Seid Hamid
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Correspondence to Jemila Seid Hamid.

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Jana, S., Balakrishnan, N., von Rosen, D. et al. High dimensional extension of the growth curve model and its application in genetics. Stat Methods Appl 26, 273–292 (2017). https://doi.org/10.1007/s10260-016-0369-4

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  • DOI: https://doi.org/10.1007/s10260-016-0369-4

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