iGC—an integrated analysis package of gene expression and copy number alteration

Background With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results...
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Autor*in:	Lai, Yi-Pin [verfasserIn] Wang, Liang-Bo Wang, Wei-An Lai, Liang-Chuan Tsai, Mong-Hsun Lu, Tzu-Pin Chuang, Eric Y.

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2017

Schlagwörter:	Copy number alteration Gene expression R/Bioconductor

Anmerkung:	© The Author(s). 2017

Übergeordnetes Werk:	Enthalten in: BMC bioinformatics - London : BioMed Central, 2000, 18(2017), 1 vom: 14. Jan.
Übergeordnetes Werk:	volume:18 ; year:2017 ; number:1 ; day:14 ; month:01

Links:	Volltext

DOI / URN:	10.1186/s12859-016-1438-2

Katalog-ID:	SPR026909049

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Indexfelder

author_variant	y p l ypl l b w lbw w a w waw l c l lcl m h t mht t p l tpl e y c ey eyc
matchkey_str	article:14712105:2017----::gaitgaeaayipcaefeexrsinnc
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publishDate	2017
allfields	10.1186/s12859-016-1438-2 doi (DE-627)SPR026909049 (SPR)s12859-016-1438-2-e DE-627 ger DE-627 rakwb eng Lai, Yi-Pin verfasserin aut iGC—an integrated analysis package of gene expression and copy number alteration 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. Results We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. Conclusion The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package’s source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html. Copy number alteration (dpeaa)DE-He213 Gene expression (dpeaa)DE-He213 R/Bioconductor (dpeaa)DE-He213 Wang, Liang-Bo aut Wang, Wei-An aut Lai, Liang-Chuan aut Tsai, Mong-Hsun aut Lu, Tzu-Pin aut Chuang, Eric Y. aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 18(2017), 1 vom: 14. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:18 year:2017 number:1 day:14 month:01 https://dx.doi.org/10.1186/s12859-016-1438-2 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2017 1 14 01
spelling	10.1186/s12859-016-1438-2 doi (DE-627)SPR026909049 (SPR)s12859-016-1438-2-e DE-627 ger DE-627 rakwb eng Lai, Yi-Pin verfasserin aut iGC—an integrated analysis package of gene expression and copy number alteration 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. Results We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. Conclusion The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package’s source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html. Copy number alteration (dpeaa)DE-He213 Gene expression (dpeaa)DE-He213 R/Bioconductor (dpeaa)DE-He213 Wang, Liang-Bo aut Wang, Wei-An aut Lai, Liang-Chuan aut Tsai, Mong-Hsun aut Lu, Tzu-Pin aut Chuang, Eric Y. aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 18(2017), 1 vom: 14. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:18 year:2017 number:1 day:14 month:01 https://dx.doi.org/10.1186/s12859-016-1438-2 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2017 1 14 01
allfields_unstemmed	10.1186/s12859-016-1438-2 doi (DE-627)SPR026909049 (SPR)s12859-016-1438-2-e DE-627 ger DE-627 rakwb eng Lai, Yi-Pin verfasserin aut iGC—an integrated analysis package of gene expression and copy number alteration 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. Results We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. Conclusion The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package’s source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html. Copy number alteration (dpeaa)DE-He213 Gene expression (dpeaa)DE-He213 R/Bioconductor (dpeaa)DE-He213 Wang, Liang-Bo aut Wang, Wei-An aut Lai, Liang-Chuan aut Tsai, Mong-Hsun aut Lu, Tzu-Pin aut Chuang, Eric Y. aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 18(2017), 1 vom: 14. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:18 year:2017 number:1 day:14 month:01 https://dx.doi.org/10.1186/s12859-016-1438-2 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2017 1 14 01
allfieldsGer	10.1186/s12859-016-1438-2 doi (DE-627)SPR026909049 (SPR)s12859-016-1438-2-e DE-627 ger DE-627 rakwb eng Lai, Yi-Pin verfasserin aut iGC—an integrated analysis package of gene expression and copy number alteration 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. Results We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. Conclusion The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package’s source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html. Copy number alteration (dpeaa)DE-He213 Gene expression (dpeaa)DE-He213 R/Bioconductor (dpeaa)DE-He213 Wang, Liang-Bo aut Wang, Wei-An aut Lai, Liang-Chuan aut Tsai, Mong-Hsun aut Lu, Tzu-Pin aut Chuang, Eric Y. aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 18(2017), 1 vom: 14. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:18 year:2017 number:1 day:14 month:01 https://dx.doi.org/10.1186/s12859-016-1438-2 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2017 1 14 01
allfieldsSound	10.1186/s12859-016-1438-2 doi (DE-627)SPR026909049 (SPR)s12859-016-1438-2-e DE-627 ger DE-627 rakwb eng Lai, Yi-Pin verfasserin aut iGC—an integrated analysis package of gene expression and copy number alteration 2017 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s). 2017 Background With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. Results We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. Conclusion The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package’s source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html. Copy number alteration (dpeaa)DE-He213 Gene expression (dpeaa)DE-He213 R/Bioconductor (dpeaa)DE-He213 Wang, Liang-Bo aut Wang, Wei-An aut Lai, Liang-Chuan aut Tsai, Mong-Hsun aut Lu, Tzu-Pin aut Chuang, Eric Y. aut Enthalten in BMC bioinformatics London : BioMed Central, 2000 18(2017), 1 vom: 14. Jan. (DE-627)326644814 (DE-600)2041484-5 1471-2105 nnns volume:18 year:2017 number:1 day:14 month:01 https://dx.doi.org/10.1186/s12859-016-1438-2 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_31 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_370 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_4326 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4700 AR 18 2017 1 14 01
language	English
source	Enthalten in BMC bioinformatics 18(2017), 1 vom: 14. Jan. volume:18 year:2017 number:1 day:14 month:01
sourceStr	Enthalten in BMC bioinformatics 18(2017), 1 vom: 14. Jan. volume:18 year:2017 number:1 day:14 month:01
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Copy number alteration Gene expression R/Bioconductor
isfreeaccess_bool	true
container_title	BMC bioinformatics
authorswithroles_txt_mv	Lai, Yi-Pin @@aut@@ Wang, Liang-Bo @@aut@@ Wang, Wei-An @@aut@@ Lai, Liang-Chuan @@aut@@ Tsai, Mong-Hsun @@aut@@ Lu, Tzu-Pin @@aut@@ Chuang, Eric Y. @@aut@@
publishDateDaySort_date	2017-01-14T00:00:00Z
hierarchy_top_id	326644814
id	SPR026909049
language_de	englisch
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author	Lai, Yi-Pin
spellingShingle	Lai, Yi-Pin misc Copy number alteration misc Gene expression misc R/Bioconductor iGC—an integrated analysis package of gene expression and copy number alteration
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topic_title	iGC—an integrated analysis package of gene expression and copy number alteration Copy number alteration (dpeaa)DE-He213 Gene expression (dpeaa)DE-He213 R/Bioconductor (dpeaa)DE-He213
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title	iGC—an integrated analysis package of gene expression and copy number alteration
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title_full	iGC—an integrated analysis package of gene expression and copy number alteration
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author_browse	Lai, Yi-Pin Wang, Liang-Bo Wang, Wei-An Lai, Liang-Chuan Tsai, Mong-Hsun Lu, Tzu-Pin Chuang, Eric Y.
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title_sort	igc—an integrated analysis package of gene expression and copy number alteration
title_auth	iGC—an integrated analysis package of gene expression and copy number alteration
abstract	Background With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. Results We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. Conclusion The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package’s source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html. © The Author(s). 2017
abstractGer	Background With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. Results We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. Conclusion The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package’s source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html. © The Author(s). 2017
abstract_unstemmed	Background With the advancement in high-throughput technologies, researchers can simultaneously investigate gene expression and copy number alteration (CNA) data from individual patients at a lower cost. Traditional analysis methods analyze each type of data individually and integrate their results using Venn diagrams. Challenges arise, however, when the results are irreproducible and inconsistent across multiple platforms. To address these issues, one possible approach is to concurrently analyze both gene expression profiling and CNAs in the same individual. Results We have developed an open-source R/Bioconductor package (iGC). Multiple input formats are supported and users can define their own criteria for identifying differentially expressed genes driven by CNAs. The analysis of two real microarray datasets demonstrated that the CNA-driven genes identified by the iGC package showed significantly higher Pearson correlation coefficients with their gene expression levels and copy numbers than those genes located in a genomic region with CNA. Compared with the Venn diagram approach, the iGC package showed better performance. Conclusion The iGC package is effective and useful for identifying CNA-driven genes. By simultaneously considering both comparative genomic and transcriptomic data, it can provide better understanding of biological and medical questions. The iGC package’s source code and manual are freely available at https://www.bioconductor.org/packages/release/bioc/html/iGC.html. © The Author(s). 2017
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title_short	iGC—an integrated analysis package of gene expression and copy number alteration
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author2	Wang, Liang-Bo Wang, Wei-An Lai, Liang-Chuan Tsai, Mong-Hsun Lu, Tzu-Pin Chuang, Eric Y.
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iGC—an integrated analysis package of gene expression and copy number alteration

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