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@ARTICLE{Ashburner2000,
  author = {Ashburner, M. and Ball, C. A. and Blake, J. A. and Botstein, D. and
	Butler, H. and Cherry, J. M. and Davis, A. P. and Dolinski, K. and
	Dwight, S. S. and Eppig, J. T. and Harris, M. A. and Hill, D. P.
	and Issel-Tarver, L. and Kasarskis, A. and Lewis, S. and Matese,
	J. C. and Richardson, J. E. and Ringwald, M. and Rubin, G. M. and
	Sherlock, G.},
  title = {Gene ontology: tool for the unification of biology. {T}he {G}ene
	{O}ntology {C}onsortium},
  journal = {Nat Genet},
  year = {2000},
  volume = {25},
  pages = {25--9},
  number = {1},
  note = {1061-4036 (Print)
	
	Journal Article
	
	
	},
  keywords = {Animals
	
	Computer Communication Networks
	
	Databases, Factual
	
	Eukaryotic Cells/*physiology
	
	*Genes
	
	Humans
	
	Metaphysics
	
	Mice
	
	Molecular Biology/*trends
	
	Research Support, Non-U.S. Gov't
	
	Research Support, U.S. Gov't, P.H.S.
	
	*Sequence Analysis, DNA
	
	*Terminology},
  owner = {lmarchionni},
  timestamp = {2006.12.17}
}

@ARTICLE{Benjamini1995,
  author = {Benjamini, Y. and Hochberg, Y.},
  title = {Controlling the false discovery rate: a practical and powerful approach
	to multiple testing},
  journal = {Journal of the Royal Statistical Society Series B},
  year = {1995},
  volume = {57},
  pages = {289--300},
  owner = {lmarchionni},
  timestamp = {2006.12.17}
}

@ARTICLE{Kanehisa2004,
  author = {Kanehisa, M. and Goto, S. and Kawashima, S. and Okuno, Y. and Hattori,
	M.},
  title = {The {KEGG} resource for deciphering the genome},
  journal = {Nucleic Acids Res},
  year = {2004},
  volume = {32},
  pages = {D277--80},
  number = {Database issue},
  note = {1362-4962 (Electronic)
	
	Journal Article},
  abstract = {A grand challenge in the post-genomic era is a complete computer representation
	of the cell and the organism, which will enable computational prediction
	of higher-level complexity of cellular processes and organism behavior
	from genomic information. Toward this end we have been developing
	a knowledge-based approach for network prediction, which is to predict,
	given a complete set of genes in the genome, the protein interaction
	networks that are responsible for various cellular processes. KEGG
	at http://www.genome.ad.jp/kegg/ is the reference knowledge base
	that integrates current knowledge on molecular interaction networks
	such as pathways and complexes (PATHWAY database), information about
	genes and proteins generated by genome projects (GENES/SSDB/KO databases)
	and information about biochemical compounds and reactions (COMPOUND/GLYCAN/REACTION
	databases). These three types of database actually represent three
	graph objects, called the protein network, the gene universe and
	the chemical universe. New efforts are being made to abstract knowledge,
	both computationally and manually, about ortholog clusters in the
	KO (KEGG Orthology) database, and to collect and analyze carbohydrate
	structures in the GLYCAN database.},
  date = {Jan 1},
  keywords = {Animals
	
	Carbohydrate Sequence
	
	*Chemistry
	
	Computational Biology
	
	*Databases, Factual
	
	Databases, Genetic
	
	Genes
	
	Genome
	
	*Genomics
	
	Humans
	
	Internet
	
	*Molecular Biology
	
	Molecular Sequence Data
	
	Protein Binding
	
	Proteins/genetics/metabolism
	
	Research Support, Non-U.S. Gov't},
  owner = {lmarchionni},
  timestamp = {2007.12.12},
  url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=14681412}
}

@ARTICLE{Mootha2003a,
  author = {Mootha, V. K. and Lindgren, C. M. and Eriksson, K. F. and Subramanian,
	A. and Sihag, S. and Lehar, J. and Puigserver, P. and Carlsson, E.
	and Ridderstrale, M. and Laurila, E. and Houstis, N. and Daly, M.
	J. and Patterson, N. and Mesirov, J. P. and Golub, T. R. and Tamayo,
	P. and Spiegelman, B. and Lander, E. S. and Hirschhorn, J. N. and
	Altshuler, D. and Groop, L. C.},
  title = {P{GC}-1alpha-responsive genes involved in oxidative phosphorylation
	are coordinately downregulated in human diabetes},
  journal = {Nat Genet},
  year = {2003},
  volume = {34},
  pages = {267--273},
  number = {3},
  note = {1061-4036 (Print)
	
	Journal Article},
  abstract = {DNA microarrays can be used to identify gene expression changes characteristic
	of human disease. This is challenging, however, when relevant differences
	are subtle at the level of individual genes. We introduce an analytical
	strategy, Gene Set Enrichment Analysis, designed to detect modest
	but coordinate changes in the expression of groups of functionally
	related genes. Using this approach, we identify a set of genes involved
	in oxidative phosphorylation whose expression is coordinately decreased
	in human diabetic muscle. Expression of these genes is high at sites
	of insulin-mediated glucose disposal, activated by PGC-1alpha and
	correlated with total-body aerobic capacity. Our results associate
	this gene set with clinically important variation in human metabolism
	and illustrate the value of pathway relationships in the analysis
	of genomic profiling experiments.},
  date = {Jul},
  keywords = {Animals
	
	Cells, Cultured
	
	Diabetes Mellitus, Type 2/*genetics/*metabolism
	
	Down-Regulation
	
	Gene Expression Profiling
	
	Glucose/metabolism
	
	Glucose Tolerance Test
	
	Humans
	
	Insulin/physiology
	
	Male
	
	Mice
	
	Muscle, Skeletal/*drug effects
	
	Myoblasts/physiology
	
	Oligonucleotide Array Sequence Analysis
	
	*Oxidative Phosphorylation
	
	RNA, Messenger/metabolism
	
	Research Support, Non-U.S. Gov't
	
	Transcription Factors/*pharmacology},
  owner = {lmarchionni},
  timestamp = {2007.12.12},
  url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve&db=PubMed&dopt=Citation&list_uids=12808457}
}

@INCOLLECTION{Smyth2005a,
  author = {Smyth, G. K.},
  title = {Limma: linear models for microarray data},
  booktitle = {Bioinformatics and Computational Biology Solutions using R and Bioconductor},
  publisher = {Springer},
  year = {2005},
  editor = {Gentleman, R. and Carey, R. V. and Dudoit, S. and Irizarry, R. and
	Huber, W.},
  pages = {397--420},
  address = {New York},
  owner = {lmarchionni},
  timestamp = {2006.12.17}
}

@ARTICLE{Smyth2004,
  author = {Smyth, G. K.},
  title = {Linear models and empirical {B}ayes methods for assessing differential
	expression in microarray experiments.},
  journal = {Statistical Applications in Genetics and Molecular Biology},
  year = {2004},
  volume = {3},
  number = {Article 3
	
	
	
	},
  owner = {lmarchionni},
  timestamp = {2006.12.17}
}

@ARTICLE{Smyth2005,
  author = {Smyth, G. K. and Michaud, J. and Scott, H. S.},
  title = {Use of within-array replicate spots for assessing differential expression
	in microarray experiments},
  journal = {Bioinformatics},
  year = {2005},
  note = {1367-4803 (Print)
	
	Evaluation Studies
	
	Journal Article
	
	Validation Studies
	
	
	},
  abstract = {MOTIVATION: Spotted arrays are often printed with probes in duplicate
	or triplicate, but current methods for assessing differential expression
	are not able to make full use of the resulting information. The usual
	practice is to average the duplicate or triplicate results for each
	probe before assessing differential expression. This results in the
	loss of valuable information about genewise variability. RESULTS:
	A method is proposed for extracting more information from within-array
	replicate spots in microarray experiments by estimating the strength
	of the correlation between them. The method involves fitting separate
	linear models to the expression data for each gene but with a common
	value for the between-replicate correlation. The method greatly improves
	the precision with which the genewise variances are estimated and
	thereby improves inference methods designed to identify differentially
	expressed genes. The method may be combined with empirical Bayes
	methods for moderating the genewise variances between genes. The
	method is validated using data from a microarray experiment involving
	calibration and ratio control spots in conjunction with spiked-in
	RNA. Comparing results for calibration and ratio control spots shows
	that the common correlation method results in substantially better
	discrimination of differentially expressed genes from those which
	are not. The spike-in experiment also confirms that the results may
	be further improved by empirical Bayes smoothing of the variances
	when the sample size is small. AVAILABILITY: The methodology is implemented
	in the limma software package for R, available from the CRAN repository
	http://www.r-project.org},
  keywords = {*Algorithms
	
	Comparative Study
	
	Gene Expression Profiling/*methods
	
	In Situ Hybridization, Fluorescence/*methods
	
	Linear Models
	
	*Models, Genetic
	
	Models, Statistical
	
	Oligonucleotide Array Sequence Analysis/*methods
	
	RNA, Messenger/*genetics
	
	Research Support, Non-U.S. Gov't
	
	Software
	
	Variation (Genetics)/*genetics},
  owner = {lmarchionni},
  timestamp = {2006.12.17}
}

@ARTICLE{Subramanian2005,
  author = {Aravind Subramanian and Pablo Tamayo and Vamsi K Mootha and Sayan
	Mukherjee and Benjamin L Ebert and Michael A Gillette and Amanda
	Paulovich and Scott L Pomeroy and Todd R Golub and Eric S Lander
	and Jill P Mesirov},
  title = {Gene set enrichment analysis: a knowledge-based approach for interpreting
	genome-wide expression profiles.},
  journal = {Proc Natl Acad Sci U S A},
  year = {2005},
  volume = {102},
  pages = {15545--15550},
  number = {43},
  month = {Oct},
  abstract = {Although genomewide RNA expression analysis has become a routine tool
	in biomedical research, extracting biological insight from such information
	remains a major challenge. Here, we describe a powerful analytical
	method called Gene Set Enrichment Analysis (GSEA) for interpreting
	gene expression data. The method derives its power by focusing on
	gene sets, that is, groups of genes that share common biological
	function, chromosomal location, or regulation. We demonstrate how
	GSEA yields insights into several cancer-related data sets, including
	leukemia and lung cancer. Notably, where single-gene analysis finds
	little similarity between two independent studies of patient survival
	in lung cancer, GSEA reveals many biological pathways in common.
	The GSEA method is embodied in a freely available software package,
	together with an initial database of 1,325 biologically defined gene
	sets.},
  doi = {10.1073/pnas.0506580102},
  keywords = {Cell Line, Tumor; Female; Gene Expression Profiling; Genes, p53; Genome;
	Humans; Leukemia, Lymphocytic, Acute; Leukemia, Myelocytic, Acute;
	Lung Neoplasms; Male; Oligonucleotide Array Sequence Analysis},
  owner = {gp},
  pii = {0506580102},
  pmid = {16199517},
  timestamp = {2007.11.02},
  url = {http://dx.doi.org/10.1073/pnas.0506580102}
}

@ARTICLE{Tyekucheva2011,
  author = {Svitlana Tyekucheva and Luigi Marchionni and Rachel Karchin and Giovanni
	Parmigiani},
  title = {Integrating diverse genomic data using gene sets},
  journal = {Genome Biology (in press)},
  year = {2011},
  abstract = {Gene sets analysis considers whether genes that form a set from a
	specific biological standpoint
	
	also behave in a related way in experimental data. It is commonly
	used in high throughput
	
	genomic experiments to help with interpretability of the results.
	In recent cancer genome
	
	projects, it has also been used to integrate information provided
	by multiple genome-wide assays.
	
	The diversity of genomic data collected in current cancer research
	is increasing and calls
	
	for scalable statistical methods that allow integration across data
	types. Gene sets analysis provides
	
	such solution, though a rigorous framework is still lacking. In this
	paper we introduce,
	
	compare, and systematically evaluate two set-based data integration
	approaches: computing
	
	integrated gene-to-phenotype association scores, followed by conventional
	gene sets analysis
	
	(Integration+GSA), and using a consensus significance score after
	all data types are analyzed
	
	individually (GSA+Integration). We then use our integrated analysis
	tools to jointly examine gene
	
	expression and copy number variation data about glioblastoma multiforme
	tumor samples, from
	
	The Cancer Genome Atlas.},
  owner = {marchion},
  timestamp = {2010.08.23}
}


