Spectral estimation in unevenly sampled space of periodically expressed microarray time series data

Liew, Alan Wee‐Chung; Xian, Jun; Wu, Shan; Smith, David K.; Yan, Hong

doi:10.1186/1471-2105-8-137

Cited by 30 publications

(28 citation statements)

References 32 publications

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“…Outside of laboratory science, nearly all data sets are difficult to control and have many of the same problems that EHR data have. Thus, we claim that while we apply our analysis in the context of human health and physiology, our methods can be easily generalized to nearly all time-dependent contexts; e.g., astronomy [10], geology [11], climatology [12], and genetics [13].…”

Section: Motivating Examplesmentioning

confidence: 99%

Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations

Albers

Hripcsak

2012

Chaos: An Interdisciplinary Journal of Nonlinear Science

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This paper addresses how to calculate and interpret the time-delayed mutual information (TDMI) for a complex, diversely and sparsely measured, possibly non-stationary population of time-series of unknown composition and origin. The primary vehicle used for this analysis is a comparison between the time-delayed mutual information averaged over the population and the time-delayed mutual information of an aggregated population (here, aggregation implies the population is conjoined before any statistical estimates are implemented). Through the use of information theoretic tools, a sequence of practically implementable calculations are detailed that allow for the average and aggregate time-delayed mutual information to be interpreted. Moreover, these calculations can also be used to understand the degree of homo or heterogeneity present in the population. To demonstrate that the proposed methods can be used in nearly any situation, the methods are applied and demonstrated on the time series of glucose measurements from two different subpopulations of individuals from the Columbia University Medical Center electronic health record repository, revealing a picture of the composition of the population as well as physiological features.

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Section: Motivating Examplesmentioning

confidence: 99%

Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations

Albers

Hripcsak

2012

Chaos: An Interdisciplinary Journal of Nonlinear Science

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“…This dataset contains 4,774 gene expression levels and 25 sampling time points with a 5-minute interval. Three benchmark sets of genes that have been utilized in Lichtenberg et al [19] and Liew et al [20] as standards of cell cycle genes are also applied herein for performance comparison. These benchmark sets, involving 113, 352, and 518 genes, respectively, include candidates of cycle cell regulated genes in yeast proposed by Spellman et al [2], Johansson et al [21], Simon et al [22], Lee et al [23], and Mewes et al [24] and are accessible in a laboratory website (http://www.cbs.dtu.dk/cellcycle/).…”

Section: Methodsmentioning

confidence: 99%

Spectral Analysis on Time-Course Expression Data: Detecting Periodic Genes Using a Real-Valued Iterative Adaptive Approach

Agyepong

Hsu

Dougherty

et al. 2013

Advances in Bioinformatics

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Time-course expression profiles and methods for spectrum analysis have been applied for detecting transcriptional periodicities, which are valuable patterns to unravel genes associated with cell cycle and circadian rhythm regulation. However, most of the proposed methods suffer from restrictions and large false positives to a certain extent. Additionally, in some experiments, arbitrarily irregular sampling times as well as the presence of high noise and small sample sizes make accurate detection a challenging task. A novel scheme for detecting periodicities in time-course expression data is proposed, in which a real-valued iterative adaptive approach (RIAA), originally proposed for signal processing, is applied for periodogram estimation. The inferred spectrum is then analyzed using Fisher's hypothesis test. With a proper p-value threshold, periodic genes can be detected. A periodic signal, two nonperiodic signals, and four sampling strategies were considered in the simulations, including both bursts and drops. In addition, two yeast real datasets were applied for validation. The simulations and real data analysis reveal that RIAA can perform competitively with the existing algorithms. The advantage of RIAA is manifested when the expression data are highly irregularly sampled, and when the number of cycles covered by the sampling time points is very reduced.

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“…A well known set of gene expression time series datasets is that of the Yeast Saccharomyces cerevisiae from Spellman et al [6]. [5]). The left and right panels of Fig.…”

Section: Gene Expression Time Series Profilesmentioning

confidence: 99%

“…For each N, we performed 30 simulation runs and calculated // and cr. This ranking has been exploited by us for periodicity detection in gene expression time series data in a recent paper where the Fisher test caimot be reliably performed [5]. 3.…”

Section: Power Of Fisher Test Versus Signal Lengthmentioning

confidence: 99%

Statistical Detection of Short Periodic Gene Expression Time Series Profiles

Liew

Law

Yan

2007

AIP Conference Proceedings

Self Cite

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Many cellular processes exhibit periodic behaviors. Hence, one of the important tasks in gene expression data analysis is to detect subset of genes that exhibit cyclicity or periodicity in their gene expression time series profiles. Unfortunately, gene expression time series profiles are usually of very short length and highly contaminated with noise. This makes detection of periodic profiles a very difficult problem. Recently, a hypothesis testing method based on the Fisher g-statistic with correction for multiple testing has been proposed to detect periodic gene expression profiles. However, it was observed that the test is not reliable if the signal length is too short. In this paper, we performed extensive simulation study to investigate the statistical power of the test as a function of signal length, SNR, and the false discovery rate. We found that the number of periodic profiles can be severely underestimated for short length signal. The findings indicated that caution needs to be exercised when interpreting the test result for very short length signals.

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Spectral estimation in unevenly sampled space of periodically expressed microarray time series data

Cited by 30 publications

References 32 publications

Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations

Using time-delayed mutual information to discover and interpret temporal correlation structure in complex populations

Spectral Analysis on Time-Course Expression Data: Detecting Periodic Genes Using a Real-Valued Iterative Adaptive Approach

Statistical Detection of Short Periodic Gene Expression Time Series Profiles

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