attract: A Method for Identifying Core Pathways That Define Cellular Phenotypes

被引：44

作者：

Mar, Jessica C. ^{[1
,2
]}

Matigian, Nicholas A. ^{[3
]}

Quackenbush, John ^{[1
,2
,3
,4
]}

Wells, Christine A. ^{[3
,5
]}

机构：

[1] Harvard Univ, Sch Publ Hlth, Dept Biostat, Boston, MA 02115 USA

[2] Dana Farber Canc Inst, Dept Biostat & Computat Biol, Boston, MA 02115 USA

[3] Griffith Univ, Natl Ctr Adult Stem Cell Res, Eskitis Inst Cell & Mol Therapies, Brisbane, Qld 4111, Australia

[4] Dana Farber Canc Inst, Dept Canc Biol, Boston, MA 02115 USA

[5] Univ Queensland, Australian Inst Bioengn & Nanotechnol, Brisbane, Qld, Australia

来源：

PLOS ONE | 2011年 / 6卷 / 10期

基金：

澳大利亚研究理事会; 英国医学研究理事会;

关键词：

SET ENRICHMENT ANALYSIS; GENE;

D O I：

10.1371/journal.pone.0025445

中图分类号：

O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];

学科分类号：

07 ; 0710 ; 09 ;

摘要：

attract is a knowledge-driven analytical approach for identifying and annotating the gene-sets that best discriminate between cell phenotypes. attract finds distinguishing patterns within pathways, decomposes pathways into meta-genes representative of these patterns, and then generates synexpression groups of highly correlated genes from the entire transcriptome dataset. attract can be applied to a wide range of biological systems and is freely available as a Bioconductor package and has been incorporated into the MeV software system.

引用

页数：7

共 16 条

[1] CONTROLLING THE FALSE DISCOVERY RATE - A PRACTICAL AND POWERFUL APPROACH TO MULTIPLE TESTING [J].

BENJAMINI, Y ;

HOCHBERG, Y .

JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY, 1995, 57 (01) :289-300

[2] DAVID: Database for annotation, visualization, and integrated discovery [J].

Dennis, G ;

Sherman, BT ;

Hosack, DA ;

Yang, J ;

Gao, W ;

Lane, HC ;

Lempicki, RA .

GENOME BIOLOGY, 2003, 4 (09)

[3] Geometric Interpretation of Gene Coexpression Network Analysis [J].

Horvath, Steve ;

Dong, Jun .

PLOS COMPUTATIONAL BIOLOGY, 2008, 4 (08)

[4] Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources [J].

Huang, Da Wei ;

Sherman, Brad T. ;

Lempicki, Richard A. .

NATURE PROTOCOLS, 2009, 4 (01) :44-57

[5] Gene set enrichment analysis made simple [J].

Irizarry, Rafael A. ;

Wang, Chi ;

Zhou, Yun ;

Speed, Terence P. .

STATISTICAL METHODS IN MEDICAL RESEARCH, 2009, 18 (06) :565-575

[6] Extensions to gene set enrichment [J].

Jiang, Zhen ;

Gentleman, Robert .

BIOINFORMATICS, 2007, 23 (03) :306-313

[7] KEGG: Kyoto Encyclopedia of Genes and Genomes [J].

Kanehisa, M ;

Goto, S .

NUCLEIC ACIDS RESEARCH, 2000, 28 (01) :27-30

[8] Defining an informativeness metric for clustering gene expression data [J].

Mar, Jessica C. ;

Wells, Christine A. ;

Quackenbush, John .

BIOINFORMATICS, 2011, 27 (08) :1094-1100

[9] Signed weighted gene co-expression network analysis of transcriptional regulation in murine embryonic stem cells [J].

Mason, Mike J. ;

Fan, Guoping ;

Plath, Kathrin ;

Zhou, Qing ;

Horvath, Steve .

BMC GENOMICS, 2009, 10

[10] Regulatory networks define phenotypic classes of human stem cell lines [J].

Mueller, Franz-Josef ;

Laurent, Louise C. ;

Kostka, Dennis ;

Ulitsky, Igor ;

Williams, Roy ;

Lu, Christina ;

Park, In-Hyun ;

Rao, Mahendra S. ;

Shamir, Ron ;

Schwartz, Philip H. ;

Schmidt, Nils O. ;

Loring, Jeanne F. .

NATURE, 2008, 455 (7211) :401-U55

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