An effective differential expression analysis of deep-sequencing data based on the Poisson log-normal model

Tremendous amount of deep-sequencing data has unprecedentedly improved our understanding in biomedical science by digital sequence reads. To mine useful information from such data, a proper distribution for modeling all range of the count data and accurate parameter estimation are required. In this...
Ausführliche Beschreibung

Gespeichert in:

Autor*in:	Wu, Jun [verfasserIn] Zhao, Xiaodong Lin, Zongli Shao, Zhifeng

Format:	Artikel
Sprache:	Englisch

Erschienen:	2015

Rechteinformationen:	Nutzungsrecht: © 2015, Imperial College Press

Übergeordnetes Werk:	Enthalten in: Journal of bioinformatics and computational biology - Londorn : Imperial College Press, 2003, 13(2015), 2
Übergeordnetes Werk:	volume:13 ; year:2015 ; number:2

Links:	Volltext

DOI / URN:	10.1142/S0219720015500018

Katalog-ID:	OLC1961381346

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allfieldsSound	10.1142/S0219720015500018 doi PQ20160617 (DE-627)OLC1961381346 (DE-599)GBVOLC1961381346 (PRQ)p948-23d3a723a423d63bbc42ad625f7defc67091a7e25943b5e8c5d9408114398f830 (KEY)0519760020150000013000200000effectivedifferentialexpressionanalysisofdeepseque DE-627 ger DE-627 rakwb eng 570 ZDB 42.11 bkl 42.13 bkl 54.80 bkl Wu, Jun verfasserin aut An effective differential expression analysis of deep-sequencing data based on the Poisson log-normal model 2015 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier Tremendous amount of deep-sequencing data has unprecedentedly improved our understanding in biomedical science by digital sequence reads. To mine useful information from such data, a proper distribution for modeling all range of the count data and accurate parameter estimation are required. In this paper, we propose a method, called "DEPln," for differential expression analysis based on the Poisson log-normal (PLN) distribution with an accurate parameter estimation strategy, which aims to overcome the inconvenience in the mathematical analysis of the traditional PLN distribution. The performance of our proposed method is validated by both synthetic and real data. Experimental results indicate that our method outperforms the traditional methods in terms of the discrimination ability and results in a good tradeoff between the recall rate and the precision. Thus, our work provides a new approach for gene expression analysis and has strong potential in deep-sequencing based research. Nutzungsrecht: © 2015, Imperial College Press Zhao, Xiaodong oth Lin, Zongli oth Shao, Zhifeng oth Enthalten in Journal of bioinformatics and computational biology Londorn : Imperial College Press, 2003 13(2015), 2 (DE-627)37660753X (DE-600)2131422-6 (DE-576)45226524X 0219-7200 nnns volume:13 year:2015 number:2 http://dx.doi.org/10.1142/S0219720015500018 Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-MAT SSG-OLC-PHA SSG-OLC-DE-84 GBV_ILN_70 GBV_ILN_2190 42.11 AVZ 42.13 AVZ 54.80 AVZ AR 13 2015 2
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title	An effective differential expression analysis of deep-sequencing data based on the Poisson log-normal model
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title_sort	effective differential expression analysis of deep-sequencing data based on the poisson log-normal model
title_auth	An effective differential expression analysis of deep-sequencing data based on the Poisson log-normal model
abstract	Tremendous amount of deep-sequencing data has unprecedentedly improved our understanding in biomedical science by digital sequence reads. To mine useful information from such data, a proper distribution for modeling all range of the count data and accurate parameter estimation are required. In this paper, we propose a method, called "DEPln," for differential expression analysis based on the Poisson log-normal (PLN) distribution with an accurate parameter estimation strategy, which aims to overcome the inconvenience in the mathematical analysis of the traditional PLN distribution. The performance of our proposed method is validated by both synthetic and real data. Experimental results indicate that our method outperforms the traditional methods in terms of the discrimination ability and results in a good tradeoff between the recall rate and the precision. Thus, our work provides a new approach for gene expression analysis and has strong potential in deep-sequencing based research.
abstractGer	Tremendous amount of deep-sequencing data has unprecedentedly improved our understanding in biomedical science by digital sequence reads. To mine useful information from such data, a proper distribution for modeling all range of the count data and accurate parameter estimation are required. In this paper, we propose a method, called "DEPln," for differential expression analysis based on the Poisson log-normal (PLN) distribution with an accurate parameter estimation strategy, which aims to overcome the inconvenience in the mathematical analysis of the traditional PLN distribution. The performance of our proposed method is validated by both synthetic and real data. Experimental results indicate that our method outperforms the traditional methods in terms of the discrimination ability and results in a good tradeoff between the recall rate and the precision. Thus, our work provides a new approach for gene expression analysis and has strong potential in deep-sequencing based research.
abstract_unstemmed	Tremendous amount of deep-sequencing data has unprecedentedly improved our understanding in biomedical science by digital sequence reads. To mine useful information from such data, a proper distribution for modeling all range of the count data and accurate parameter estimation are required. In this paper, we propose a method, called "DEPln," for differential expression analysis based on the Poisson log-normal (PLN) distribution with an accurate parameter estimation strategy, which aims to overcome the inconvenience in the mathematical analysis of the traditional PLN distribution. The performance of our proposed method is validated by both synthetic and real data. Experimental results indicate that our method outperforms the traditional methods in terms of the discrimination ability and results in a good tradeoff between the recall rate and the precision. Thus, our work provides a new approach for gene expression analysis and has strong potential in deep-sequencing based research.
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title_short	An effective differential expression analysis of deep-sequencing data based on the Poisson log-normal model
url	http://dx.doi.org/10.1142/S0219720015500018
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author2	Zhao, Xiaodong Lin, Zongli Shao, Zhifeng
author2Str	Zhao, Xiaodong Lin, Zongli Shao, Zhifeng
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doi_str	10.1142/S0219720015500018
up_date	2024-07-04T01:02:28.365Z
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An effective differential expression analysis of deep-sequencing data based on the Poisson log-normal model

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