Discovery of relevant response in infected potato plants from time series of gene expression data

Gamberger, Dragan; Stare, Tjaša; Miljkovic, Dragana; Gruden, Kristina; Lavrač, Nada (2019) Discovery of relevant response in infected potato plants from time series of gene expression data. Machine Learning and Knowledge Extraction, 1 (1). pp. 400-413. ISSN 2504-4990

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Abstract

The paper presents a methodology for analyzing time series of gene expression data collected from the leaves of potato virus Y (PVY) infected and non-infected potato plants, with the aim to identify significant differences between the two sets of potato plants’ characteristic for various time points. We aim at identifying differentially- expressed genes whose expression values are statistically significantly different in the set of PVY infected potato plants compared to non- infected plants, and which demonstrate also statistically significant changes of expression values of genes of PVY infected potato plants in time. The novelty of the approach includes stratified data randomization used in estimating the statistical properties of gene expression of the samples in the control set of non-infected potato plants. A novel estimate that computes the relative minimal distance between the samples has been defined that enables reliable identification of the differences between the target and control datasets when these sets are small. The relevance of the outcomes is demonstrated by visualizing the relative minimal distance of gene expression changes in time for three different types of potato leaves for the genes that have been identified as relevant by the proposed methodology.

Item Type:	Article
Uncontrolled Keywords:	gene expression time series ; potato virus infections ; variance-stabilized data ; randomization test ; stratified randomization ; relative minimal distance of samples
Subjects:	NATURAL SCIENCES > Biology
Divisions:	Division of Electronics
Depositing User:	Dragan Gamberger
Date Deposited:	13 Feb 2019 15:33
URI:	http://fulir.irb.hr/id/eprint/4419
DOI:	10.3390/make1010023

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