@article{Khatri2005,
	abstract = {Independent of the platform and the analysis methods used, the result of a microarray experiment is, in most cases, a list of differentially expressed genes. An automatic ontological analysis approach has been recently proposed to help with the biological interpretation of such results. Currently, this approach is the de facto standard for the secondary analysis of high throughput experiments and a large number of tools have been developed for this purpose. We present a detailed comparison of 14 such tools using the following criteria: scope of the analysis, visualization capabilities, statistical model(s) used, correction for multiple comparisons, reference microarrays available, installation issues and sources of annotation data. This detailed analysis of the capabilities of these tools will help researchers choose the most appropriate tool for a given type of analysis. More importantly, in spite of the fact that this type of analysis has been generally adopted, this approach has several important intrinsic drawbacks. These drawbacks are associated with all tools discussed and represent conceptual limitations of the current state-of-the-art in ontological analysis. We propose these as challenges for the next generation of secondary data analysis tools.},
	author = {Khatri, Purvesh and Drăghici, Sorin},
	doi = {10.1093/bioinformatics/bti565},
	file = {:D$\backslash$:/paper/Ontological analysis of gene expression data current tools, limitations, and open problems.pdf:pdf},
	issn = {1367-4803},
	journal = {Bioinformatics},
	keywords = {Algorithms,Computational Biology,Computational Biology: instrumentation,Computational Biology: methods,Computer Graphics,DNA,Data Interpretation,Database Management Systems,Databases,Gene Expression Profiling,Gene Expression Regulation,Genetic,Humans,Models,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: instrumen,Oligonucleotide Array Sequence Analysis: methods,Reference Standards,Sequence Analysis,Software,Statistical},
	month = sep,
	number = {18},
	pages = {3587--95},
	pmid = {15994189},
	title = {{Ontological analysis of gene expression data: current tools, limitations, and open problems.}},
	url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2435250\&tool=pmcentrez\&rendertype=abstract},
	volume = {21},
	year = {2005}
}
@article{Huang2009a,
	abstract = {Functional analysis of large gene lists, derived in most cases from emerging high-throughput genomic, proteomic and bioinformatics scanning approaches, is still a challenging and daunting task. The gene-annotation enrichment analysis is a promising high-throughput strategy that increases the likelihood for investigators to identify biological processes most pertinent to their study. Approximately 68 bioinformatics enrichment tools that are currently available in the community are collected in this survey. Tools are uniquely categorized into three major classes, according to their underlying enrichment algorithms. The comprehensive collections, unique tool classifications and associated questions/issues will provide a more comprehensive and up-to-date view regarding the advantages, pitfalls and recent trends in a simpler tool-class level rather than by a tool-by-tool approach. Thus, the survey will help tool designers/developers and experienced end users understand the underlying algorithms and pertinent details of particular tool categories/tools, enabling them to make the best choices for their particular research interests.},
	author = {Huang, Da Wei and Sherman, Brad T and Lempicki, Richard A},
	doi = {10.1093/nar/gkn923},
	file = {:C$\backslash$:/Users/Administrator/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Huang, Sherman, Lempicki - 2009 - Bioinformatics enrichment tools paths toward the comprehensive functional analysis of large gene lists..pdf:pdf},
	issn = {1362-4962},
	journal = {Nucleic acids research},
	keywords = {Algorithms,Computational Biology,Computational Biology: trends,Databases, Genetic,Genes,Software,Software: classification,Software: trends},
	month = jan,
	number = {1},
	pages = {1--13},
	pmid = {19033363},
	title = {{Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists.}},
	url = {http://nar.oxfordjournals.org/cgi/content/abstract/37/1/1},
	volume = {37},
	year = {2009}
}
@article{Huang2009b,
	abstract = {DAVID bioinformatics resources consists of an integrated biological knowledgebase and analytic tools aimed at systematically extracting biological meaning from large gene/protein lists. This protocol explains how to use DAVID, a high-throughput and integrated data-mining environment, to analyze gene lists derived from high-throughput genomic experiments. The procedure first requires uploading a gene list containing any number of common gene identifiers followed by analysis using one or more text and pathway-mining tools such as gene functional classification, functional annotation chart or clustering and functional annotation table. By following this protocol, investigators are able to gain an in-depth understanding of the biological themes in lists of genes that are enriched in genome-scale studies.},
	author = {Huang, Da Wei and Sherman, Brad T and Lempicki, Richard A},
	doi = {10.1038/nprot.2008.211},
	issn = {1750-2799},
	journal = {Nature protocols},
	keywords = {Computational Biology,Computational Biology: methods,Data Interpretation, Statistical,Genes,Genes: genetics,Genomics,Genomics: methods,Internet,Software},
	month = jan,
	number = {1},
	pages = {44--57},
	pmid = {19131956},
	title = {{Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources.}},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/19131956},
	volume = {4},
	year = {2009}
}
@Article{Falcon2007,
    author = {S Falcon and R Gentleman},
    title = {Using {G}{O}stats to test gene lists for {G}{O} term association.},
    journal = {Bioinformatics},
    year = {2007},
    volume = {23},
    number = {2},
    pages = {257-8},
  }
@article{Tusher2001,
	abstract = {Microarrays can measure the expression of thousands of genes to identify changes in expression between different biological states. Methods are needed to determine the significance of these changes while accounting for the enormous number of genes. We describe a method, Significance Analysis of Microarrays (SAM), that assigns a score to each gene on the basis of change in gene expression relative to the standard deviation of repeated measurements. For genes with scores greater than an adjustable threshold, SAM uses permutations of the repeated measurements to estimate the percentage of genes identified by chance, the false discovery rate (FDR). When the transcriptional response of human cells to ionizing radiation was measured by microarrays, SAM identified 34 genes that changed at least 1.5-fold with an estimated FDR of 12\%, compared with FDRs of 60 and 84\% by using conventional methods of analysis. Of the 34 genes, 19 were involved in cell cycle regulation and 3 in apoptosis. Surprisingly, four nucleotide excision repair genes were induced, suggesting that this repair pathway for UV-damaged DNA might play a previously unrecognized role in repairing DNA damaged by ionizing radiation.},
	author = {Tusher, V G and Tibshirani, R and Chu, G},
	doi = {10.1073/pnas.091062498},
	file = {::},
	issn = {0027-8424},
	journal = {Proceedings of the National Academy of Sciences of the United States of America},
	keywords = {Apoptosis,Apoptosis: genetics,Apoptosis: radiation effects,Cell Cycle,Cell Cycle: genetics,Cell Cycle: radiation effects,DNA Damage,DNA Damage: genetics,DNA Damage: radiation effects,DNA Repair,DNA Repair: genetics,Down-Regulation,Down-Regulation: radiation effects,Gene Expression Profiling,Gene Expression Regulation,Gene Expression Regulation: radiation effects,Humans,Oligonucleotide Array Sequence Analysis,RNA, Messenger,RNA, Messenger: genetics,RNA, Messenger: metabolism,Radiation, Ionizing,Reproducibility of Results,Statistics as Topic,Tumor Cells, Cultured,Up-Regulation,Up-Regulation: radiation effects},
	month = apr,
	number = {9},
	pages = {5116--21},
	pmid = {11309499},
	title = {{Significance analysis of microarrays applied to the ionizing radiation response.}},
	url = {http://www.pnas.org/cgi/content/abstract/98/9/5116},
	volume = {98},
	year = {2001}
}
@article{Smyth2005,
    title = {Limma: linear models for microarray data},
    author = {Gordon K. Smyth},
    booktitle = {Bioinformatics and Computational Biology Solutions using R and Bioconductor},
    editor = {R. Gentleman and V. Carey and S. Dudoit and R. Irizarry and W. Huber},
    publisher = {Springer},
    address = {New York},
    year = {2005},
    pages = {397--420},
  }
@article{Burchard2010,
	abstract = {Tumorigenesis involves multistep genetic alterations. To elucidate the microRNA (miRNA)-gene interaction network in carcinogenesis, we examined their genome-wide expression profiles in 96 pairs of tumor/non-tumor tissues from hepatocellular carcinoma (HCC). Comprehensive analysis of the coordinate expression of miRNAs and mRNAs reveals that miR-122 is under-expressed in HCC and that increased expression of miR-122 seed-matched genes leads to a loss of mitochondrial metabolic function. Furthermore, the miR-122 secondary targets, which decrease in expression, are good prognostic markers for HCC. Transcriptome profiling data from additional 180 HCC and 40 liver cirrhotic patients in the same cohort were used to confirm the anti-correlation of miR-122 primary and secondary target gene sets. The HCC findings can be recapitulated in mouse liver by silencing miR-122 with antagomir treatment followed by gene-expression microarray analysis. In vitro miR-122 data further provided a direct link between induction of miR-122-controlled genes and impairment of mitochondrial metabolism. In conclusion, miR-122 regulates mitochondrial metabolism and its loss may be detrimental to sustaining critical liver function and contribute to morbidity and mortality of liver cancer patients.},
	author = {Burchard, Julja and Zhang, Chunsheng and Liu, Angela M and Poon, Ronnie T P and Lee, Nikki P Y and Wong, Kwong-Fai and Sham, Pak C and Lam, Brian Y and Ferguson, Mark D and Tokiwa, George and Smith, Ryan and Leeson, Brendan and Beard, Rebecca and Lamb, John R and Lim, Lee and Mao, Mao and Dai, Hongyue and Luk, John M},
	doi = {10.1038/msb.2010.58},
	file = {:D$\backslash$:/paper/microRNA-122 as a regulator of mitochondrial metabolic gene network in hepatocellular carcinoma.pdf:pdf},
	issn = {1744-4292},
	journal = {Molecular systems biology},
	keywords = {Animals,Carcinoma, Hepatocellular,Carcinoma, Hepatocellular: genetics,Cell Line, Tumor,Down-Regulation,Down-Regulation: genetics,Energy Metabolism,Energy Metabolism: genetics,Gene Expression Profiling,Gene Expression Regulation, Neoplastic,Gene Regulatory Networks,Gene Regulatory Networks: genetics,Genes, Mitochondrial,Genes, Mitochondrial: genetics,Hepatocellular,Hepatocellular: genetics,Humans,Inbred C57BL,Liver,Liver Neoplasms,Liver Neoplasms: genetics,Liver: metabolism,Liver: pathology,Messenger,Messenger: genetics,Messenger: metabolism,Mice,Mice, Inbred C57BL,MicroRNAs,MicroRNAs: genetics,MicroRNAs: metabolism,Mitochondria,Mitochondria: genetics,Mitochondria: metabolism,Mitochondrial,Mitochondrial: genetics,Neoplastic,Nucleic Acid,RNA, Messenger,RNA, Messenger: genetics,RNA, Messenger: metabolism,Reproducibility of Results,Sequence Homology, Nucleic Acid,Signal Transduction,Signal Transduction: genetics,Survival Analysis,Tumor,Up-Regulation,Up-Regulation: genetics},
	month = aug,
	pages = {402},
	pmid = {20739924},
	publisher = {EMBO and Macmillan Publishers Limited},
	shorttitle = {Mol Syst Biol},
	title = {{microRNA-122 as a regulator of mitochondrial metabolic gene network in hepatocellular carcinoma.}},
	url = {http://dx.doi.org/10.1038/msb.2010.58},
	volume = {6},
	year = {2010}
}
@Article{Gu2012,
AUTHOR = {Gu, Zuguang and Liu, Jialin and Cao, Kunming and Zhang, Junfeng and Wang, Jin},
TITLE = {Centrality-based pathway enrichment: a systematic approach for finding significant pathways dominated by key genes},
JOURNAL = {BMC Systems Biology},
VOLUME = {6},
YEAR = {2012},
NUMBER = {1},
PAGES = {56},
URL = {http://www.biomedcentral.com/1752-0509/6/56},
DOI = {10.1186/1752-0509-6-56},
PubMedID = {22672776},
ISSN = {1752-0509},
ABSTRACT = {BACKGROUND:Biological pathways are important for understanding biological mechanisms. Thus, finding important pathways that underlie biological problems helps researchers to focus on the most relevant sets of genes. Pathways resemble networks with complicated structures, but most of the existing pathway enrichment tools ignore topological information embedded within pathways, which limits their applicability.RESULTS:A systematic and extensible pathway enrichment method in which nodes are weighted by network centrality was proposed. We demonstrate how choice of pathway structure and centrality measurement, as well as the presence of key genes, affects pathway significance. We emphasize two improvements of our method over current methods. First, allowing for the diversity of genes' characters and the difficulty of covering gene importance from all aspects, we set centrality as an optional parameter in the model. Second, nodes rather than genes form the basic unit of pathways, such that one node can be composed of several genes and one gene may reside in different nodes. By comparing our methodology to the original enrichment method using both simulation data and real-world data, we demonstrate the efficacy of our method in finding new pathways from biological perspective.CONCLUSIONS:Our method can benefit the systematic analysis of biological pathways and help to extract more meaningful information from gene expression data. The algorithm has been implemented as an R package CePa, and also a web-based version of CePa is provided.},
}
@article{Ackermann2009,
abstract = {BACKGROUND: Analysis of microarray and other high-throughput data on the basis of gene sets, rather than individual genes, is becoming more important in genomic studies. Correspondingly, a large number of statistical approaches for detecting gene set enrichment have been proposed, but both the interrelations and the relative performance of the various methods are still very much unclear. RESULTS: We conduct an extensive survey of statistical approaches for gene set analysis and identify a common modular structure underlying most published methods. Based on this finding we propose a general framework for detecting gene set enrichment. This framework provides a meta-theory of gene set analysis that not only helps to gain a better understanding of the relative merits of each embedded approach but also facilitates a principled comparison and offers insights into the relative interplay of the methods. CONCLUSION: We use this framework to conduct a computer simulation comparing 261 different variants of gene set enrichment procedures and to analyze two experimental data sets. Based on the results we offer recommendations for best practices regarding the choice of effective procedures for gene set enrichment analysis.},
author = {Ackermann, Marit and Strimmer, Korbinian},
doi = {10.1186/1471-2105-10-47},
file = {:C$\backslash$:/Users/Administrator/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Ackermann, Strimmer - 2009 - A general modular framework for gene set enrichment analysis..pdf:pdf},
issn = {1471-2105},
journal = {BMC bioinformatics},
keywords = {Algorithms,Animals,Computer Simulation,Databases,Genetic,Humans,Models,Oligonucleotide Array Sequence Analysis,Oligonucleotide Array Sequence Analysis: methods,Statistical},
month = jan,
pages = {47},
pmid = {19192285},
title = {{A general modular framework for gene set enrichment analysis.}},
url = {http://www.pubmedcentral.nih.gov/articlerender.fcgi?artid=2661051\&tool=pmcentrez\&rendertype=abstract},
volume = {10},
year = {2009}
}
@article{Subramanian2005,
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.},
author = {Subramanian, Aravind and Tamayo, Pablo and Mootha, Vamsi K and Mukherjee, Sayan and Ebert, Benjamin L and Gillette, Michael A and Paulovich, Amanda and Pomeroy, Scott L and Golub, Todd R and Lander, Eric S and Mesirov, Jill P},
doi = {10.1073/pnas.0506580102},
file = {:C$\backslash$:/Users/Administrator/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Subramanian et al. - 2005 - Gene set enrichment analysis a knowledge-based approach for interpreting genome-wide expression profiles..pdf:pdf},
issn = {0027-8424},
journal = {Proceedings of the National Academy of Sciences of the United States of America},
keywords = {Cell Line, Tumor,Female,Gene Expression Profiling,Gene Expression Profiling: methods,Genes, p53,Genes, p53: physiology,Genome,Humans,Leukemia, Myeloid, Acute,Leukemia, Myeloid, Acute: genetics,Lung Neoplasms,Lung Neoplasms: genetics,Lung Neoplasms: mortality,Male,Oligonucleotide Array Sequence Analysis,Precursor Cell Lymphoblastic Leukemia-Lymphoma,Precursor Cell Lymphoblastic Leukemia-Lymphoma: ge},
month = oct,
number = {43},
pages = {15545--50},
pmid = {16199517},
title = {{Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles.}},
url = {http://www.pnas.org/cgi/content/abstract/102/43/15545},
volume = {102},
year = {2005}
}
@Manual{Song2006,
title = {pcot2: Principal Coordinates and Hotelling's T-Square method},
    author = {Sarah Song and Mik Black},
    note = {R package version 1.24.0},
}
@article{Hummel2008,
abstract = {MOTIVATION: Several authors have studied expression in gene sets with specific goals: overrepresentation of interesting genes in functional groups, predictive power for class membership and searches for groups where the constituent genes show coordinated changes in expression under the experimental conditions. The purpose of this article is to follow the third direction. One important aspect is that the gene sets under analysis are known a priori and are not determined from the experimental data at hand. Our goal is to provide a methodology that helps to identify the relevant structural constituents (phenotypical, experimental design, biological component) that determine gene expression in a group. RESULTS: Gene-wise linear models are used to formalize the structural aspects of a study. The full model is contrasted with a reduced model that lacks the relevant design component. A comparison with respect to goodness of fit is made and quantified. An asymptotic test and a permutation test are derived to test the null hypothesis that the reduced model sufficiently explains the observed expression within the gene group of interest. Graphical tools are available to illustrate and interpret the results of the analysis. Examples demonstrate the wide range of application. AVAILABILITY: The R-package GlobalAncova (http://www.bioconductor.org) offers data and functions as well as a vignette to guide the user through specific analysis steps.},
author = {Hummel, Manuela and Meister, Reinhard and Mansmann, Ulrich},
doi = {10.1093/bioinformatics/btm531},
file = {:C$\backslash$:/Users/Administrator/AppData/Local/Mendeley Ltd./Mendeley Desktop/Downloaded/Hummel, Meister, Mansmann - 2008 - GlobalANCOVA exploration and assessment of gene group effects.pdf:pdf},
issn = {1367-4811},
journal = {Bioinformatics},
keywords = {Algorithms,Biological,Computer Simulation,Gene Expression Profiling,Gene Expression Profiling: methods,Models,Multigene Family,Multigene Family: physiology,Proteome,Proteome: metabolism,Signal Transduction,Signal Transduction: physiology,Software,User-Computer Interface},
month = jan,
number = {1},
pages = {78--85},
pmid = {18024976},
title = {{GlobalANCOVA: exploration and assessment of gene group effects.}},
url = {http://bioinformatics.oxfordjournals.org/cgi/content/abstract/24/1/78},
volume = {24},
year = {2008}
}
@article{Lopes15092010,
author = {Lopes, Christian T. and Franz, Max and Kazi, Farzana and Donaldson, Sylva L. and Morris, Quaid and Bader, Gary D.}, 
title = {Cytoscape Web: an interactive web-based network browser}, 
volume = {26}, 
number = {18}, 
pages = {2347-2348}, 
year = {2010}, 
doi = {10.1093/bioinformatics/btq430}, 
abstract ={Summary: Cytoscape Web is a web-based network visualization tool–modeled after Cytoscape–which is open source, interactive, customizable and easily integrated into web sites. Multiple file exchange formats can be used to load data into Cytoscape Web, including GraphML, XGMML and SIF.Availability and Implementation: Cytoscape Web is implemented in Flex/ActionScript with a JavaScript API and is freely available at http://cytoscapeweb.cytoscape.org/Contact: gary.bader@utoronto.caSupplementary information: Supplementary data are available at Bioinformatics online.}, 
URL = {http://bioinformatics.oxfordjournals.org/content/26/18/2347.abstract}, 
eprint = {http://bioinformatics.oxfordjournals.org/content/26/18/2347.full.pdf+html}, 
journal = {Bioinformatics} 
}

