Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset

Background Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset cons...
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Autor*in:	Tsuyuzaki, Koki [verfasserIn] Tominaga, Daisuke Kwon, Yeondae Miyazaki, Satoru

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2013

Schlagwörter:	Cystic Fibrosis Patient Quorum Sensing Outlier Detection Experiment Side Operon Gene

Anmerkung:	© Tsuyuzaki et al.; licensee BioMed Central Ltd. 2013

Übergeordnetes Werk:	Enthalten in: BMC genomics - London : BioMed Central, 2000, 14(2013), Suppl 2 vom: 15. Feb.
Übergeordnetes Werk:	volume:14 ; year:2013 ; number:Suppl 2 ; day:15 ; month:02

Links:	Volltext

DOI / URN:	10.1186/1471-2164-14-S2-S9

Katalog-ID:	SPR027086232

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520			\|a Background Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset. Results As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes. Conclusions The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions.
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allfields	10.1186/1471-2164-14-S2-S9 doi (DE-627)SPR027086232 (SPR)1471-2164-14-S2-S9-e DE-627 ger DE-627 rakwb eng Tsuyuzaki, Koki verfasserin aut Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Tsuyuzaki et al.; licensee BioMed Central Ltd. 2013 Background Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset. Results As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes. Conclusions The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions. Cystic Fibrosis Patient (dpeaa)DE-He213 Quorum Sensing (dpeaa)DE-He213 Outlier Detection (dpeaa)DE-He213 Experiment Side (dpeaa)DE-He213 Operon Gene (dpeaa)DE-He213 Tominaga, Daisuke aut Kwon, Yeondae aut Miyazaki, Satoru aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 2 vom: 15. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 2 day:15 month:02 https://dx.doi.org/10.1186/1471-2164-14-S2-S9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 Suppl 2 15 02
spelling	10.1186/1471-2164-14-S2-S9 doi (DE-627)SPR027086232 (SPR)1471-2164-14-S2-S9-e DE-627 ger DE-627 rakwb eng Tsuyuzaki, Koki verfasserin aut Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Tsuyuzaki et al.; licensee BioMed Central Ltd. 2013 Background Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset. Results As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes. Conclusions The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions. Cystic Fibrosis Patient (dpeaa)DE-He213 Quorum Sensing (dpeaa)DE-He213 Outlier Detection (dpeaa)DE-He213 Experiment Side (dpeaa)DE-He213 Operon Gene (dpeaa)DE-He213 Tominaga, Daisuke aut Kwon, Yeondae aut Miyazaki, Satoru aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 2 vom: 15. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 2 day:15 month:02 https://dx.doi.org/10.1186/1471-2164-14-S2-S9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 Suppl 2 15 02
allfields_unstemmed	10.1186/1471-2164-14-S2-S9 doi (DE-627)SPR027086232 (SPR)1471-2164-14-S2-S9-e DE-627 ger DE-627 rakwb eng Tsuyuzaki, Koki verfasserin aut Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Tsuyuzaki et al.; licensee BioMed Central Ltd. 2013 Background Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset. Results As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes. Conclusions The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions. Cystic Fibrosis Patient (dpeaa)DE-He213 Quorum Sensing (dpeaa)DE-He213 Outlier Detection (dpeaa)DE-He213 Experiment Side (dpeaa)DE-He213 Operon Gene (dpeaa)DE-He213 Tominaga, Daisuke aut Kwon, Yeondae aut Miyazaki, Satoru aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 2 vom: 15. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 2 day:15 month:02 https://dx.doi.org/10.1186/1471-2164-14-S2-S9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 Suppl 2 15 02
allfieldsGer	10.1186/1471-2164-14-S2-S9 doi (DE-627)SPR027086232 (SPR)1471-2164-14-S2-S9-e DE-627 ger DE-627 rakwb eng Tsuyuzaki, Koki verfasserin aut Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Tsuyuzaki et al.; licensee BioMed Central Ltd. 2013 Background Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset. Results As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes. Conclusions The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions. Cystic Fibrosis Patient (dpeaa)DE-He213 Quorum Sensing (dpeaa)DE-He213 Outlier Detection (dpeaa)DE-He213 Experiment Side (dpeaa)DE-He213 Operon Gene (dpeaa)DE-He213 Tominaga, Daisuke aut Kwon, Yeondae aut Miyazaki, Satoru aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 2 vom: 15. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 2 day:15 month:02 https://dx.doi.org/10.1186/1471-2164-14-S2-S9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 Suppl 2 15 02
allfieldsSound	10.1186/1471-2164-14-S2-S9 doi (DE-627)SPR027086232 (SPR)1471-2164-14-S2-S9-e DE-627 ger DE-627 rakwb eng Tsuyuzaki, Koki verfasserin aut Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset 2013 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © Tsuyuzaki et al.; licensee BioMed Central Ltd. 2013 Background Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset. Results As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes. Conclusions The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions. Cystic Fibrosis Patient (dpeaa)DE-He213 Quorum Sensing (dpeaa)DE-He213 Outlier Detection (dpeaa)DE-He213 Experiment Side (dpeaa)DE-He213 Operon Gene (dpeaa)DE-He213 Tominaga, Daisuke aut Kwon, Yeondae aut Miyazaki, Satoru aut Enthalten in BMC genomics London : BioMed Central, 2000 14(2013), Suppl 2 vom: 15. Feb. (DE-627)326644954 (DE-600)2041499-7 1471-2164 nnns volume:14 year:2013 number:Suppl 2 day:15 month:02 https://dx.doi.org/10.1186/1471-2164-14-S2-S9 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_206 GBV_ILN_213 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2031 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2057 GBV_ILN_2061 GBV_ILN_2111 GBV_ILN_2113 GBV_ILN_2190 GBV_ILN_4012 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4249 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 14 2013 Suppl 2 15 02
language	English
source	Enthalten in BMC genomics 14(2013), Suppl 2 vom: 15. Feb. volume:14 year:2013 number:Suppl 2 day:15 month:02
sourceStr	Enthalten in BMC genomics 14(2013), Suppl 2 vom: 15. Feb. volume:14 year:2013 number:Suppl 2 day:15 month:02
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Cystic Fibrosis Patient Quorum Sensing Outlier Detection Experiment Side Operon Gene
isfreeaccess_bool	true
container_title	BMC genomics
authorswithroles_txt_mv	Tsuyuzaki, Koki @@aut@@ Tominaga, Daisuke @@aut@@ Kwon, Yeondae @@aut@@ Miyazaki, Satoru @@aut@@
publishDateDaySort_date	2013-02-15T00:00:00Z
hierarchy_top_id	326644954
id	SPR027086232
language_de	englisch
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author	Tsuyuzaki, Koki
spellingShingle	Tsuyuzaki, Koki misc Cystic Fibrosis Patient misc Quorum Sensing misc Outlier Detection misc Experiment Side misc Operon Gene Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset
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topic_title	Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset Cystic Fibrosis Patient (dpeaa)DE-He213 Quorum Sensing (dpeaa)DE-He213 Outlier Detection (dpeaa)DE-He213 Experiment Side (dpeaa)DE-He213 Operon Gene (dpeaa)DE-He213
topic	misc Cystic Fibrosis Patient misc Quorum Sensing misc Outlier Detection misc Experiment Side misc Operon Gene
topic_unstemmed	misc Cystic Fibrosis Patient misc Quorum Sensing misc Outlier Detection misc Experiment Side misc Operon Gene
topic_browse	misc Cystic Fibrosis Patient misc Quorum Sensing misc Outlier Detection misc Experiment Side misc Operon Gene
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title	Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset
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title_full	Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset
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author_browse	Tsuyuzaki, Koki Tominaga, Daisuke Kwon, Yeondae Miyazaki, Satoru
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title_sort	two-way aic: detection of differentially expressed genes from large scale microarray meta-dataset
title_auth	Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset
abstract	Background Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset. Results As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes. Conclusions The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions. © Tsuyuzaki et al.; licensee BioMed Central Ltd. 2013
abstractGer	Background Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset. Results As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes. Conclusions The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions. © Tsuyuzaki et al.; licensee BioMed Central Ltd. 2013
abstract_unstemmed	Background Detection of significant differentially expressed genes (DEGs) from DNA microarray datasets is a common routine task conducted in biomedical research. For the detection of DEGs, numerous methods are proposed. By such conventional methods, generally, DEGs are detected from one dataset consisting of group of control and treatment. However, some DEGs are easily to be detected in any experimental condition. For the detection of much experiment condition specific DEGs, each measurement value of gene expression levels should be compared in two dimensional ways, or both with other genes and other datasets simultaneously. For this purpose, we retrieve the gene expression data from public database as possible and construct "meta-dataset" which summarize expression change of all genes in various experimental condition. Herein, we propose "two-way AIC" (Akaike Information Criteria), method for simultaneous detection of significance genes and experiments on meta-dataset. Results As a case study of the Pseudomonas aeruginosa, we evaluate whether two-way AIC method can detect test data which is the experiment condition specific DEGs. Operon genes are used as test data. Compared with other commonly used statistical methods (t-rank/F-test, RankProducts and SAM), two-way AIC shows the highest specificity of detection of operon genes. Conclusions The two-way AIC performs high specificity for operon gene detection on the microarray meta-dataset. This method can also be applied to estimation of mutual gene interactions. © Tsuyuzaki et al.; licensee BioMed Central Ltd. 2013
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title_short	Two-way AIC: detection of differentially expressed genes from large scale microarray meta-dataset
url	https://dx.doi.org/10.1186/1471-2164-14-S2-S9
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author2	Tominaga, Daisuke Kwon, Yeondae Miyazaki, Satoru
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up_date	2024-07-04T00:17:54.183Z
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