Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments

Liu, Tianqing; Lin, Nan; Shi, Ning; Zhang, Baoxue

doi:10.1186/1471-2105-10-146

Cited by 21 publications

(50 citation statements)

References 42 publications

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“…Since we have a short time series, we used template-matching clustering of the expression data, using ORIClust (37). We performed the time-profile clustering on a specific filtered subset of genes, namely, those identified by edgeR to be differentially expressed in at least one time point compared to time point zero (38).…”

Section: Methodsmentioning

confidence: 99%

Dynamics of the Saccharomyces cerevisiae Transcriptome during Bread Dough Fermentation

Aslankoohi

Zhu

Rezaei

et al. 2013

Appl Environ Microbiol

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The behavior of yeast cells during industrial processes such as the production of beer, wine, and bioethanol has been extensively studied. In contrast, our knowledge about yeast physiology during solid-state processes, such as bread dough, cheese, or cocoa fermentation, remains limited. We investigated changes in the transcriptomes of three genetically distinct Saccharomyces cerevisiae strains during bread dough fermentation. Our results show that regardless of the genetic background, all three strains exhibit similar changes in expression patterns. At the onset of fermentation, expression of glucose-regulated genes changes dramatically, and the osmotic stress response is activated. The middle fermentation phase is characterized by the induction of genes involved in amino acid metabolism. Finally, at the latest time point, cells suffer from nutrient depletion and activate pathways associated with starvation and stress responses. Further analysis shows that genes regulated by the high-osmolarity glycerol (HOG) pathway, the major pathway involved in the response to osmotic stress and glycerol homeostasis, are among the most differentially expressed genes at the onset of fermentation. More importantly, deletion of HOG1 and other genes of this pathway significantly reduces the fermentation capacity. Together, our results demonstrate that cells embedded in a solid matrix such as bread dough suffer severe osmotic stress and that a proper induction of the HOG pathway is critical for optimal fermentation.

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Section: Methodsmentioning

confidence: 99%

Dynamics of the Saccharomyces cerevisiae Transcriptome during Bread Dough Fermentation

Aslankoohi

Zhu

Rezaei

et al. 2013

Appl Environ Microbiol

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“…Our results on the false positive rate of ORIOGEN suggest some error in Figure three of Liu et al [1], perhaps due to a programming error.…”

Section: Discussionmentioning

confidence: 51%

“…The methodology of Liu et al [1], implemented in ORICC, does not control Type I error rate explicitly. It thereby risks attributing differential expression through time to an excessive proportion of genes whose expression does not truly change.…”

Section: Introductionmentioning

confidence: 99%

“…Performance of their proposal was compared with three other methods including the order-restricted inference based methodology of Peddada et al [2,3]. In this note we point out several inaccuracies in Liu et al [1] and conclude that the order-restricted inference based methodology of Peddada et al (programmed in the software ORIOGEN) indeed operates at the desired nominal Type 1 error level, an important feature of a statistical decision rule, while being computationally substantially faster than indicated by Liu et al [1]. …”

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confidence: 99%

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A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments

2009

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BackgroundFor gene expression data obtained from a time-course microarray experiment, Liu et al. [1] developed a new algorithm for clustering genes with similar expression profiles over time. Performance of their proposal was compared with three other methods including the order-restricted inference based methodology of Peddada et al. [2,3]. In this note we point out several inaccuracies in Liu et al. [1] and conclude that the order-restricted inference based methodology of Peddada et al. (programmed in the software ORIOGEN) indeed operates at the desired nominal Type 1 error level, an important feature of a statistical decision rule, while being computationally substantially faster than indicated by Liu et al. [1].ResultsApplication of ORIOGEN to the well-known breast cancer cell line data of Lobenhofer et al. [4] revealed that ORIOGEN software took only 21 minutes to run (using 100,000 bootstraps with p = 0.0025), substantially faster than the 72 hours found by Liu et al. [1] using Matlab. Also, based on a data simulated according to the model and parameters of simulation 1 (σ2 = 1, M = 5) in [1] we found that ORIOGEN took less than 30 seconds to run in stark contrast to Liu et al. who reported that their implementation of the same algorithm in R took 2979.29 seconds. Furthermore, for the simulation studies reported in [1], unlike the claims made by Liu et al. [1], ORIOGEN always maintained the desired false positive rate. According to Figure three in Liu et al. [1] their algorithm had a false positive rate ranging approximately from 0.20 to 0.70 for the scenarios that they simulated.ConclusionsOur comparisons of run times indicate that the implementations of ORIOGEN's algorithm in Matlab and R by Liu et al. [1] is inefficient compared to the publicly available JAVA implementation. Our results on the false positive rate of ORIOGEN suggest some error in Figure three of Liu et al. [1], perhaps due to a programming error.

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“…The use of the AIC statistic in isotonic models backs to Anraku (1999), yet posterior modifications have been introduced (see Zhao andPeng, 2002, andLiu et al 2009). In this paper, the idea of Kato (2009) and Rueda (2013) proposing a penalty term equal to 2D K (v) in a regression context has been applied.…”

Section: The Aic Criterion and The Degrees Of Freedommentioning

confidence: 99%

Checking unimodality using isotonic regression: an application to breast cancer mortality rates

Rueda

Ugarte

Militino

2015

Stoch Environ Res Risk Assess

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In some diseases it is well-known that a unimodal mortality pattern exists. A clear example in developed countries is breast cancer, where mortality increased sharply until the nineties and then decreased. This clear unimodal pattern is not necessarily applicable to all regions within a country. In this paper, we develop statistical tools to check if the unimodality pattern persists within regions using order restricted inference. Break points as well as confidence intervals are also provided. In addition, a new test for checking monotonicity against unimodality is derived allowing to discriminate between a simple increasing pattern and an up-then-down response pattern. A comparison with the widely used joinpoint regression technique under unimodality is provided. We show that the joinpoint technique could fail when the underlying function is not piecewise linear. Results will be illustrated using age-specific breast cancer mortality data from Spain in the period 1975-2005.

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Information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments

Cited by 21 publications

References 42 publications

Dynamics of the Saccharomyces cerevisiae Transcriptome during Bread Dough Fermentation

Dynamics of the Saccharomyces cerevisiae Transcriptome during Bread Dough Fermentation

A response to information criterion-based clustering with order-restricted candidate profiles in short time-course microarray experiments

Checking unimodality using isotonic regression: an application to breast cancer mortality rates

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