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

Chaang-Ray Chen, Wun-Yi Shu, Cheng-Wei Chang, Ian C Hsu

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Article in PloS one, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact, top 90% of its field

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 4 citations in OpenAlex.

Improving forecasting accuracy for stock market data using EMD-HW bagging.PloS one · 2018
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5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Chaang-Ray ChenDepartment of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.

Wun-Yi ShuInstitute of Statistics, National Tsing Hua University, Hsinchu, Taiwan.

Cheng-Wei ChangDepartment of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.

Ian C HsuDepartment of Biomedical Engineering and Environmental Sciences, National Tsing Hua University, Hsinchu, Taiwan.

National Tsing Hua University · TW

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

Datasets as TopicGene Expression ProfilingGene Regulatory NetworksOxidation-ReductionReproducibility of ResultsYeasts

Identifiers

PMID25372711

DOI10.1371/journal.pone.0111719

PMCPMC4221108

OpenAlexW2071375446

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LicenceCC BY

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.