A feature selection strategy for gene expression time series experiments with hidden Markov models

Autoři: Roberto A. Cárdenas-Ovando aff001;  Edith A. Fernández-Figueroa aff002;  Héctor A. Rueda-Zárate aff001;  Julieta Noguez aff001;  Claudia Rangel-Escareño aff002
Působiště autorů: School of Engineering and Sciences, Tecnológico de Monterrey, Mexico City, Mexico aff001;  Computational Genomics Lab, Instituto Nacional de Medicina Genómica, Mexico City, Mexico aff002
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: https://doi.org/10.1371/journal.pone.0223183


Studies conducted in time series could be far more informative than those that only capture a specific moment in time. However, when it comes to transcriptomic data, time points are sparse creating the need for a constant search for methods capable of extracting information out of experiments of this kind. We propose a feature selection algorithm embedded in a hidden Markov model applied to gene expression time course data on either single or even multiple biological conditions. For the latter, in a simple case-control study features or genes are selected under the assumption of no change over time for the control samples, while the case group must have at least one change. The proposed model reduces the feature space according to a two-state hidden Markov model. The two states define change/no-change in gene expression. Features are ranked in consonance with three scores: number of changes across time, magnitude of such changes and quality of replicates as a measure of how much they deviate from the mean. An important highlight is that this strategy overcomes the few samples limitation, common in transcriptome experiments through a process of data transformation and rearrangement. To prove this method, our strategy was applied to three publicly available data sets. Results show that feature domain is reduced by up to 90% leaving only few but relevant features yet with findings consistent to those previously reported. Moreover, our strategy proved to be robust, stable and working on studies where sample size is an issue otherwise. Hence, even with two biological replicates and/or three time points our method proves to work well.

Klíčová slova:

Algorithms – Computational pipelines – Diet – Gene expression – Hidden Markov models – Microarrays – Normal distribution – Time measurement


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2019 Číslo 10
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