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Identification of under-detected periodicity in time-series microarray data by using empirical mode decomposition.

Chen CR, Shu WY, Chang CW, Hsu IC - PLoS ONE (2014)

Bottom Line: We present a novel method based on empirical mode decomposition (EMD) to examine this effect.We demonstrated that EMD can be used incorporating with existing periodicity detection methods to improve their performance.This approach can be applied to other time-series microarray studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.

ABSTRACT
Detecting periodicity signals from time-series microarray data is commonly used to facilitate the understanding of the critical roles and underlying mechanisms of regulatory transcriptomes. However, time-series microarray data are noisy. How the temporal data structure affects the performance of periodicity detection has remained elusive. We present a novel method based on empirical mode decomposition (EMD) to examine this effect. We applied EMD to a yeast microarray dataset and extracted a series of intrinsic mode function (IMF) oscillations from the time-series data. Our analysis indicated that many periodically expressed genes might have been under-detected in the original analysis because of interference between decomposed IMF oscillations. By validating a protein complex coexpression analysis, we revealed that 56 genes were newly determined as periodic. We demonstrated that EMD can be used incorporating with existing periodicity detection methods to improve their performance. This approach can be applied to other time-series microarray studies.

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Boxplots of expression profiles for genes associated with enriched GO terms.
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pone-0111719-g008: Boxplots of expression profiles for genes associated with enriched GO terms.

Mentions: To gain a comprehensive understanding of the functional roles of the 1469 PP probesets, a gene ontology (GO) enrichment analysis [33] was conducted. The enriched GO terms (in the biological process (BP) category) of the PP probesets were compared with those of the OX, RB, and RC clusters (Table S2). The results clearly indicated that these clusters comprised distinct subclasses of genes associated with various biological processes. Nearly all of the common GO terms for the PP probesets were shared with the OX cluster (Table S2). Confirming the results reported by Tu et al., the functions associated with these GO terms were primarily related to nucleic acid metabolic processes, ribosome biogenesis, and rRNA processing. Table S2 shows a complete list of enriched GO terms. Furthermore, many PP genes in the OX cluster peaked periodically and abruptly during the cycles. Tu et al. suggested that these OX genes might play critical regulatory roles in ensuring tight coupling between transcription and metabolic cycles. As shown in Figure 8 (top row), a typical example of a common GO term is ribosome biogenesis (GO:0042254). The average expression level of 121 OX genes was approximately 2.5-fold higher than the average expression level of 158 PP genes, indicating that low expression levels might contribute to the under-detection of periodicity. Moreover, the distribution of the number of IMFs demonstrated that the PP genes were decomposed into more IMFs than the OX genes (Figure S3), suggesting that interference between IMF oscillations might also lead to the under-detection of low-intensity periodicity.


Identification of under-detected periodicity in time-series microarray data by using empirical mode decomposition.

Chen CR, Shu WY, Chang CW, Hsu IC - PLoS ONE (2014)

Boxplots of expression profiles for genes associated with enriched GO terms.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4221108&req=5

pone-0111719-g008: Boxplots of expression profiles for genes associated with enriched GO terms.
Mentions: To gain a comprehensive understanding of the functional roles of the 1469 PP probesets, a gene ontology (GO) enrichment analysis [33] was conducted. The enriched GO terms (in the biological process (BP) category) of the PP probesets were compared with those of the OX, RB, and RC clusters (Table S2). The results clearly indicated that these clusters comprised distinct subclasses of genes associated with various biological processes. Nearly all of the common GO terms for the PP probesets were shared with the OX cluster (Table S2). Confirming the results reported by Tu et al., the functions associated with these GO terms were primarily related to nucleic acid metabolic processes, ribosome biogenesis, and rRNA processing. Table S2 shows a complete list of enriched GO terms. Furthermore, many PP genes in the OX cluster peaked periodically and abruptly during the cycles. Tu et al. suggested that these OX genes might play critical regulatory roles in ensuring tight coupling between transcription and metabolic cycles. As shown in Figure 8 (top row), a typical example of a common GO term is ribosome biogenesis (GO:0042254). The average expression level of 121 OX genes was approximately 2.5-fold higher than the average expression level of 158 PP genes, indicating that low expression levels might contribute to the under-detection of periodicity. Moreover, the distribution of the number of IMFs demonstrated that the PP genes were decomposed into more IMFs than the OX genes (Figure S3), suggesting that interference between IMF oscillations might also lead to the under-detection of low-intensity periodicity.

Bottom Line: We present a novel method based on empirical mode decomposition (EMD) to examine this effect.We demonstrated that EMD can be used incorporating with existing periodicity detection methods to improve their performance.This approach can be applied to other time-series microarray studies.

View Article: PubMed Central - PubMed

Affiliation: Department of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.

ABSTRACT
Detecting periodicity signals from time-series microarray data is commonly used to facilitate the understanding of the critical roles and underlying mechanisms of regulatory transcriptomes. However, time-series microarray data are noisy. How the temporal data structure affects the performance of periodicity detection has remained elusive. We present a novel method based on empirical mode decomposition (EMD) to examine this effect. We applied EMD to a yeast microarray dataset and extracted a series of intrinsic mode function (IMF) oscillations from the time-series data. Our analysis indicated that many periodically expressed genes might have been under-detected in the original analysis because of interference between decomposed IMF oscillations. By validating a protein complex coexpression analysis, we revealed that 56 genes were newly determined as periodic. We demonstrated that EMD can be used incorporating with existing periodicity detection methods to improve their performance. This approach can be applied to other time-series microarray studies.

Show MeSH