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A method to identify differential expression profiles of time-course gene data with Fourier transformation

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Title: A method to identify differential expression profiles of time-course gene data with Fourier transformation
Authors: Kim, Jaehee, Ogden, Robert T., Kim, Haseong
Publication Year: 2013
Collection: Columbia University: Academic Commons
Subject Terms: Gene expression--Data processing, Genetics--Data processing, Fourier analysis, Bioinformatics, Gene expression, Genetics, Biology
Description: Background: Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. Results: This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization. The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Conclusions: Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially ...
Document Type: article in journal/newspaper
Language: English
Relation: https://doi.org/10.7916/D8S75DT6
DOI: 10.7916/D8S75DT6
Availability: https://doi.org/10.7916/D8S75DT6
Accession Number: edsbas.51BC4812
Database: BASE
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  Data: A method to identify differential expression profiles of time-course gene data with Fourier transformation
– Name: Author
  Label: Authors
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  Data: <searchLink fieldCode="AR" term="%22Kim%2C+Jaehee%22">Kim, Jaehee</searchLink><br /><searchLink fieldCode="AR" term="%22Ogden%2C+Robert+T%2E%22">Ogden, Robert T.</searchLink><br /><searchLink fieldCode="AR" term="%22Kim%2C+Haseong%22">Kim, Haseong</searchLink>
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  Data: 2013
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  Data: Columbia University: Academic Commons
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  Data: <searchLink fieldCode="DE" term="%22Gene+expression--Data+processing%22">Gene expression--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Genetics--Data+processing%22">Genetics--Data processing</searchLink><br /><searchLink fieldCode="DE" term="%22Fourier+analysis%22">Fourier analysis</searchLink><br /><searchLink fieldCode="DE" term="%22Bioinformatics%22">Bioinformatics</searchLink><br /><searchLink fieldCode="DE" term="%22Gene+expression%22">Gene expression</searchLink><br /><searchLink fieldCode="DE" term="%22Genetics%22">Genetics</searchLink><br /><searchLink fieldCode="DE" term="%22Biology%22">Biology</searchLink>
– Name: Abstract
  Label: Description
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  Data: Background: Time course gene expression experiments are an increasingly popular method for exploring biological processes. Temporal gene expression profiles provide an important characterization of gene function, as biological systems are both developmental and dynamic. With such data it is possible to study gene expression changes over time and thereby to detect differential genes. Much of the early work on analyzing time series expression data relied on methods developed originally for static data and thus there is a need for improved methodology. Since time series expression is a temporal process, its unique features such as autocorrelation between successive points should be incorporated into the analysis. Results: This work aims to identify genes that show different gene expression profiles across time. We propose a statistical procedure to discover gene groups with similar profiles using a nonparametric representation that accounts for the autocorrelation in the data. In particular, we first represent each profile in terms of a Fourier basis, and then we screen out genes that are not differentially expressed based on the Fourier coefficients. Finally, we cluster the remaining gene profiles using a model-based approach in the Fourier domain. We evaluate the screening results in terms of sensitivity, specificity, FDR and FNR, compare with the Gaussian process regression screening in a simulation study and illustrate the results by application to yeast cell-cycle microarray expression data with alpha-factor synchronization. The key elements of the proposed methodology: (i) representation of gene profiles in the Fourier domain; (ii) automatic screening of genes based on the Fourier coefficients and taking into account autocorrelation in the data, while controlling the false discovery rate (FDR); (iii) model-based clustering of the remaining gene profiles. Conclusions: Using this method, we identified a set of cell-cycle-regulated time-course yeast genes. The proposed method is general and can be potentially ...
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      – SubjectFull: Genetics--Data processing
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      – SubjectFull: Fourier analysis
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            NameFull: Kim, Jaehee
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            NameFull: Kim, Haseong
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              Y: 2013
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