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High dimensional extension of the growth curve model and its application in genetics
McMaster University, Canada.
McMaster University, Canada.
Linköping University, Department of Mathematics, Mathematical Statistics . Linköping University, Faculty of Science & Engineering. Swedish Agriculture University, Sweden.
McMaster University, Canada; St Michaels Hospital, Canada; McMaster University, Canada.
2017 (English)In: Statistical Methods & Applications, ISSN 1618-2510, E-ISSN 1613-981X, Vol. 26, no 2, 273-292 p.Article in journal (Refereed) Published
Abstract [en]

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.

Place, publisher, year, edition, pages
SPRINGER HEIDELBERG , 2017. Vol. 26, no 2, 273-292 p.
Keyword [en]
Growth curve model; Longitudinal data; High-dimensional data; Time course data; Genomic data; Gene filtering
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-138221DOI: 10.1007/s10260-016-0369-4ISI: 000401893400005OAI: oai:DiVA.org:liu-138221DiVA: diva2:1109402
Available from: 2017-06-14 Created: 2017-06-14 Last updated: 2017-06-14

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
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More styles
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