@article{GSVA_submitted,
	Author = {Sonja H\"anzelmann and Robert Castelo and Justin Guinney},
	Journal = {submitted},
	Title = {{GSVA}: Gene Set Variation Analysis},
	Year = {2011}}

@Article{pmid11747917,
   Author="Thompson, J. R.  and Gerald, P. F.  and Willoughby, M. L.  and Armstrong, B. K. ",
   Title="{{M}aternal folate supplementation in pregnancy and protection against acute lymphoblastic leukaemia in childhood: a case-control study}",
   Journal="Lancet",
   Year="2001",
   Volume="358",
   Pages="1935--1940",
   Month="Dec"
}


@article{cahoy_transcriptome_2008,
	title = {A Transcriptome Database for Astrocytes, Neurons, and Oligodendrocytes: A New Resource for Understanding Brain Development and Function},
	volume = {28},
	shorttitle = {A Transcriptome Database for Astrocytes, Neurons, and Oligodendrocytes},
	url = {http://www.jneurosci.org/cgi/content/abstract/28/1/264},
	doi = {10.1523/JNEUROSCI.4178-07.2008},
	abstract = {Understanding the cell-cell interactions that control {CNS} development and function has long been limited by the lack of methods to cleanly separate neural cell types. Here we describe methods for the prospective isolation and purification of astrocytes, neurons, and oligodendrocytes from developing and mature mouse forebrain. We used {FACS} (fluorescent-activated cell sorting) to isolate astrocytes from transgenic mice that express enhanced green fluorescent protein {(EGFP)} under the control of an S100beta promoter. Using Affymetrix {GeneChip} Arrays, we then created a transcriptome database of the expression levels of {\textbackslash}textbackslashtextgreater20,000 genes by gene profiling these three main {CNS} neural cell types at various postnatal ages between postnatal day 1 {(P1)} and P30. This database provides a detailed global characterization and comparison of the genes expressed by acutely isolated astrocytes, neurons, and oligodendrocytes. We found that {Aldh1L1} is a highly specific antigenic marker for astrocytes with a substantially broader pattern of astrocyte expression than the traditional astrocyte marker {GFAP.} Astrocytes were enriched in specific metabolic and lipid synthetic pathways, as well as the {draper/Megf10} and Mertk/integrin alphavbeta5 phagocytic pathways suggesting that astrocytes are professional phagocytes. Our findings call into question the concept of a "glial" cell class as the gene profiles of astrocytes and oligodendrocytes are as dissimilar to each other as they are to neurons. This transcriptome database of acutely isolated purified astrocytes, neurons, and oligodendrocytes provides a resource to the neuroscience community by providing improved cell-type-specific markers and for better understanding of neural development, function, and disease.},
	number = {1},
	journal = {J. Neurosci.},
	author = {John D. Cahoy and Ben Emery and Amit Kaushal and Lynette C. Foo and Jennifer L. Zamanian and Karen S. Christopherson and Yi Xing and Jane L. Lubischer and Paul A. Krieg and Sergey A. Krupenko and Wesley J. Thompson and Ben A. Barres},
	month = jan,
	year = {2008},
	pages = {264-78}
}

@article{mootha_pgc_1alpha_responsive_2003,
	title = {{PGC-1alpha-responsive} genes involved in oxidative phosphorylation are coordinately downregulated in human diabetes},
	volume = {34},
	issn = {1061-4036},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/12808457},
	doi = {10.1038/ng1180},
	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.},
	number = {3},
	journal = {Nature Genet.},
	author = {Vamsi K Mootha and Cecilia M Lindgren and {Karl-Fredrik} Eriksson and Aravind Subramanian and Smita Sihag and Joseph Lehar and Pere Puigserver and Emma Carlsson and Martin Ridderstrile and Esa Laurila and Nicholas Houstis and Mark J Daly and Nick Patterson and Jill P Mesirov and Todd R Golub and Pablo Tamayo and Bruce Spiegelman and Eric S Lander and Joel N Hirschhorn and David Altshuler and Leif C Groop},
	month = jul,
	year = {2003},
	keywords = {Animals, Cells, Cultured, Diabetes Mellitus, {Down-Regulation,} Gene Expression Profiling, Glucose, Glucose Tolerance Test, Humans, Insulin, Male, Messenger, Mice, Muscle, Myoblasts, Oligonucleotide Array Sequence Analysis, Oxidative Phosphorylation, {RNA,} Skeletal, Transcription Factors, Type 2},
	pages = {267-73}
}

@article{subramanian_gene_2005,
	title = {Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles},
	volume = {102},
	shorttitle = {Gene set enrichment analysis},
	url = {http://www.pnas.org/content/102/43/15545.abstract},
	doi = {10.1073/pnas.0506580102},
	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.},
	number = {43},
	journal = {Proc. Natl. Acad. Sci. {U.S.A.}},
	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},
	month = oct,
	year = {2005},
	pages = {15545-50}
}

@article{verhaak_integrated_2010,
	title = {Integrated genomic analysis identifies clinically relevant subtypes of glioblastoma characterized by abnormalities in {PDGFRA,} {IDH1,} {EGFR,} and {NF1}},
	volume = {17},
	issn = {1878-3686},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/20129251},
	doi = {10.1016/j.ccr.2009.12.020},
	abstract = {The Cancer Genome Atlas Network recently cataloged recurrent genomic abnormalities in glioblastoma multiforme {(GBM).} We describe a robust gene expression-based molecular classification of {GBM} into Proneural, Neural, Classical, and Mesenchymal subtypes and integrate multidimensional genomic data to establish patterns of somatic mutations and {DNA} copy number. Aberrations and gene expression of {EGFR,} {NF1,} and {PDGFRA/IDH1} each define the Classical, Mesenchymal, and Proneural subtypes, respectively. Gene signatures of normal brain cell types show a strong relationship between subtypes and different neural lineages. Additionally, response to aggressive therapy differs by subtype, with the greatest benefit in the Classical subtype and no benefit in the Proneural subtype. We provide a framework that unifies transcriptomic and genomic dimensions for {GBM} molecular stratification with important implications for future studies.},
	number = {1},
	journal = {Cancer Cell},
	author = {Roel G W Verhaak and Katherine A Hoadley and Elizabeth Purdom and Victoria Wang and Yuan Qi and Matthew D Wilkerson and C Ryan Miller and Li Ding and Todd Golub and Jill P Mesirov and Gabriele Alexe and Michael Lawrence and Michael {O'Kelly} and Pablo Tamayo and Barbara A Weir and Stacey Gabriel and Wendy Winckler and Supriya Gupta and Lakshmi Jakkula and Heidi S Feiler and J Graeme Hodgson and C David James and Jann N Sarkaria and Cameron Brennan and Ari Kahn and Paul T Spellman and Richard K Wilson and Terence P Speed and Joe W Gray and Matthew Meyerson and Gad Getz and Charles M Perou and D Neil Hayes},
	month = jan,
	year = {2010},
	keywords = {Adult, Aged, Brain Neoplasms, {DNA} Mutational Analysis, Epidermal Growth Factor, Factor Analysis, Gene Dosage, Gene Expression, Gene Expression Profiling, Glioblastoma, Humans, Isocitrate Dehydrogenase, Middle Aged, Mutation, Neurofibromatosis 1, Oligonucleotide Array Sequence Analysis, {Platelet-Derived} Growth Factor alpha, Prognosis, Receptor, Statistical},
	pages = {98-110}
}

@article{lightfoot_folate_pathway_leukemia_2010,
	Abstract = {Studies of childhood leukemia and the potential etiologic role of genetic variation in folate metabolism have produced conflicting findings and have often been based on small numbers. We investigated the association between polymorphisms in key folate metabolism enzymes (MTHFR 677 C>T, MTHFR 1298 A>C, SHMT1 1420 C>T, MTR 2756 A>G, TS 1494del6, and TS 28bp repeat) in 939 cases of childhood acute lymphoblastic leukemia (ALL) and 89 cases of acute myeloid leukemia (AML) recruited into the United Kingdom Childhood Cancer Study. We also examined the maternal genotypes of 752 of these cases. Data from 824 noncancer controls recruited were used for comparison. No evidence of an association with MTHFR 677 was observed for ALL or AML, either in children or their mothers. However, in children an increased risk of ALL (odds ratio [OR] = 1.88; 95\% confidence interval [CI], 1.16-3.07; P = .010) and AML (OR = 2.74; 95\% CI, 1.07-7.01; P = .036) was observed with the MTR 2756 GG genotype; the association was most pronounced for cases with the MLL translocation (OR = 4.90; 95\% CI, 1.30-18.45; P = .019). These data suggest that genetic variation in methionine synthase could mediate risk of childhood leukemia, either via effects on DNA methylation or via effects on fetal growth and development.},
	Author = {Lightfoot, Tracy J and Johnston, W Thomas and Painter, Dan and Simpson, Jill and Roman, Eve and Skibola, Chris F and Smith, Martyn T and Allan, James M and Taylor, G Malcolm and {United Kingdom Childhood Cancer Study}},
	Date-Added = {2011-02-08 08:51:59 +0100},
	Date-Modified = {2011-02-08 08:51:59 +0100},
	Doi = {10.1182/blood-2009-10-249722},
	Journal = {Blood},
	Journal-Full = {Blood},
	Mesh = {Case-Control Studies; Folic Acid; Genetic Variation; Glycine Hydroxymethyltransferase; Humans; Leukemia, Myeloid, Acute; Methylenetetrahydrofolate Reductase (NADPH2); Phosphotransferases (Alcohol Group Acceptor); Precursor Cell Lymphoblastic Leukemia-Lymphoma},
	Month = {May},
	Number = {19},
	Pages = {3923-9},
	Pmc = {PMC2869556},
	Pmid = {20101025},
	Pst = {ppublish},
	Title = {Genetic variation in the folate metabolic pathway and risk of childhood leukemia},
	Volume = {115},
	Year = {2010},
	Bdsk-Url-1 = {http://dx.doi.org/10.1182/blood-2009-10-249722}
}

@article{Castelo_qpgraph_2006,
	Abstract = {Learning of large-scale networks of interactions from microarray data is an important and challenging problem in bioinformatics. A widely used approach is to assume that the available data constitute a random sample from a multivariate distribution belonging to a Gaussian graphical model. As a consequence, the prime objects of inference are full-order partial correlations which are partial correlations between two variables given the remaining ones. In the context of microarray data the number of variables exceed the sample size and this precludes the application of traditional structure learning procedures because a sampling version of full-order partial correlations does not exist. In this paper we consider limited-order partial correlations, these are partial correlations computed on marginal distributions of manageable size, and provide a set of rules that allow one to assess the usefulness of these quantities to derive the independence structure of the underlying Gaussian graphical model. Furthermore, we introduce a novel structure learning procedure based on a quantity, obtained from limited-order partial correlations, that we call the non-rejection rate. The applicability and usefulness of the procedure are demonstrated by both simulated and real data.},
	Author = {Castelo, R. and Roverato, A.},
	Date-Added = {2008-07-21 19:27:53 +0200},
	Date-Modified = {2008-09-30 10:29:55 +0200},
	Issn = {1532-4435},
	Journal = {J Mach Learn Res},
	Pages = {2621--2650},
	Title = {A robust procedure for Gaussian graphical model search from microarray data with p larger than n},
	Volume = {7},
	Year = {2006}}

@article{Castelo_qpgraph_2009,
	Abstract = {Reverse engineering bioinformatic procedures applied to high-throughput experimental data have become instrumental in generating new hypotheses about molecular regulatory mechanisms. This has been particularly the case for gene expression microarray data, where a large number of statistical and computational methodologies have been developed in order to assist in building network models of transcriptional regulation. A major challenge faced by every different procedure is that the number of available samples n for estimating the network model is much smaller than the number of genes p forming the system under study. This compromises many of the assumptions on which the statistics of the methods rely, often leading to unstable performance figures. In this work, we apply a recently developed novel methodology based in the so-called q-order limited partial correlation graphs, qp-graphs, which is specifically tailored towards molecular network discovery from microarray expression data with p >> n. Using experimental and functional annotation data from Escherichia coli, here we show how qp-graphs yield more stable performance figures than other state-of-the-art methods when the ratio of genes to experiments exceeds one order of magnitude. More importantly, we also show that the better performance of the qp-graph method on such a gene-to-sample ratio has a decisive impact on the functional coherence of the reverse-engineered transcriptional regulatory modules and becomes crucial in such a challenging situation in order to enable the discovery of a network of reasonable confidence that includes a substantial number of genes relevant to the essayed conditions. An R package, called qpgraph implementing this method is part of the Bioconductor project and can be downloaded from (www.bioconductor.org). A parallel standalone version for the most computationally expensive calculations is available from (http://functionalgenomics.upf.xsedu/qpgraph).},
	Author = {Castelo, Robert and Roverato, Alberto},
	Date-Added = {2010-08-02 15:30:34 +0200},
	Date-Modified = {2010-08-02 15:30:34 +0200},
	Doi = {10.1089/cmb.2008.08TT},
	Journal = {J Comput Biol},
	Journal-Full = {Journal of computational biology : a journal of computational molecular cell biology},
	Mesh = {Algorithms; Computational Biology; Computer Simulation; Escherichia coli; Gene Regulatory Networks; Metabolic Networks and Pathways; Models, Biological; Oligonucleotide Array Sequence Analysis; Software},
	Month = {Feb},
	Number = {2},
	Pages = {213-27},
	Pmid = {19178140},
	Pst = {ppublish},
	Title = {Reverse engineering molecular regulatory networks from microarray data with qp-graphs},
	Volume = {16},
	Year = {2009},
	Bdsk-Url-1 = {http://dx.doi.org/10.1089/cmb.2008.08TT}}

@book{Lauritzen_1996,
        Author = {Lauritzen, S.L.},
        Date-Added = {2008-09-17 11:38:10 +0200},
        Date-Modified = {2008-09-17 11:38:10 +0200},
        Publisher = {Oxford University Press},
        Title = {Graphical models},
        Year = {1996}}

@article{Roverato_1996,
	Author = {Roverato, A. and Whittaker, J.},
	Journal = {Stat Comput},
	Pages = {297--302},
	Title = {Standard errors for the parameters of graphical Gaussian models},
	Volume = {6},
	Year = {1996}}

@book{Edwards_2000,
  title={{Introduction to graphical modelling}},
  author={Edwards, D.},
  year={2000},
  publisher={Springer New York}
}

@article{Smyth_2004,
	Abstract = {The problem of identifying differentially expressed genes in designed microarray experiments is considered. Lonnstedt and Speed (2002) derived an expression for the posterior odds of differential expression in a replicated two-color experiment using a simple hierarchical parametric model. The purpose of this paper is to develop the hierarchical model of Lonnstedt and Speed (2002) into a practical approach for general microarray experiments with arbitrary numbers of treatments and RNA samples. The model is reset in the context of general linear models with arbitrary coefficients and contrasts of interest. The approach applies equally well to both single channel and two color microarray experiments. Consistent, closed form estimators are derived for the hyperparameters in the model. The estimators proposed have robust behavior even for small numbers of arrays and allow for incomplete data arising from spot filtering or spot quality weights. The posterior odds statistic is reformulated in terms of a moderated t-statistic in which posterior residual standard deviations are used in place of ordinary standard deviations. The empirical Bayes approach is equivalent to shrinkage of the estimated sample variances towards a pooled estimate, resulting in far more stable inference when the number of arrays is small. The use of moderated t-statistics has the advantage over the posterior odds that the number of hyperparameters which need to estimated is reduced; in particular, knowledge of the non-null prior for the fold changes are not required. The moderated t-statistic is shown to follow a t-distribution with augmented degrees of freedom. The moderated t inferential approach extends to accommodate tests of composite null hypotheses through the use of moderated F-statistics. The performance of the methods is demonstrated in a simulation study. Results are presented for two publicly available data sets.},
	Author = {Smyth, Gordon K},
	Date-Added = {2009-12-11 15:29:40 +0100},
	Date-Modified = {2009-12-11 15:29:40 +0100},
	Doi = {10.2202/1544-6115.1027},
	Journal = {Stat Appl Genet Mol Biol},
	Journal-Full = {Statistical applications in genetics and molecular biology},
	Pages = {Article3},
	Pmid = {16646809},
	Title = {Linear models and empirical bayes methods for assessing differential expression in microarray experiments},
	Volume = {3},
	Year = {2004},
	Bdsk-Url-1 = {http://dx.doi.org/10.2202/1544-6115.1027}}


@article{Besley_lysosomes_leukemia_1983,
	Abstract = {Lysosomal enzyme activities were studied in cells derived from the following types of leukaemia: chronic myeloid, acute myeloid, acute myelomonocytic, acute monocytic, non-T, non-B cell acute lymphoblastic, T-cell acute lymphoblastic, B-cell chronic lymphocytic and T-cell chronic lymphocytic. Activities of beta-hexosaminidase and alpha-mannosidase were significantly higher in cells from acute monocytic and acute myelomonocytic leukaemias, and somewhat higher in the other myeloid leukaemias, when compared with control granulocytes. Activities of beta-hexosaminidase, alpha-mannosidase, alpha-fucosidase, beta-glucuronidase and acid phosphatase were markedly lower in B cells of chronic lymphocytic leukaemia when compared with control or other leukaemic lymphoid cells. On isoelectric focusing abnormal patterns of beta-hexosaminidase, alpha-mannosidase and beta-glucuronidase activities were commonly found in myeloid and non-T, non-B cell leukaemias. All patients with acute myeloid leukaemia exhibited a relative decrease in the B form of beta-hexosaminidase activity. The results described show that studies on lysosomal enzymes may assist in the classification of different types of leukaemia.},
	Author = {Besley, G T and Moss, S E and Bain, A D and Dewar, A E},
	Date-Added = {2011-02-09 15:30:05 +0100},
	Date-Modified = {2011-02-09 15:30:05 +0100},
	Journal = {J Clin Pathol},
	Journal-Full = {Journal of clinical pathology},
	Mesh = {Adult; Hexosaminidases; Humans; Hydrolases; Isoelectric Focusing; Leukemia; Lysosomes; Mannosidases; alpha-Mannosidase; beta-N-Acetylhexosaminidases},
	Month = {Sep},
	Number = {9},
	Pages = {1000-4},
	Pmc = {PMC498459},
	Pmid = {6224822},
	Pst = {ppublish},
	Title = {Correlation of lysosomal enzyme abnormalities in various forms of adult leukaemia},
	Volume = {36},
	Year = {1983}}

@article{armstrong_mll_2002,
	title = {{MLL} translocations specify a distinct gene expression profile that distinguishes a unique leukemia},
	volume = {30},
	issn = {1061-4036},
	url = {http://www.ncbi.nlm.nih.gov/pubmed/11731795},
	doi = {10.1038/ng765},
	abstract = {Acute lymphoblastic leukemias carrying a chromosomal translocation involving the mixed-lineage leukemia gene {(MLL,} {ALL1,} {HRX)} have a particularly poor prognosis. Here we show that they have a characteristic, highly distinct gene expression profile that is consistent with an early hematopoietic progenitor expressing select multilineage markers and individual {HOX} genes. Clustering algorithms reveal that lymphoblastic leukemias with {MLL} translocations can clearly be separated from conventional acute lymphoblastic and acute myelogenous leukemias. We propose that they constitute a distinct disease, denoted here as {MLL,} and show that the differences in gene expression are robust enough to classify leukemias correctly as {MLL,} acute lymphoblastic leukemia or acute myelogenous leukemia. Establishing that {MLL} is a unique entity is critical, as it mandates the examination of selectively expressed genes for urgently needed molecular targets.},
	number = {1},
	journal = {Nature Gen.},
	author = {Scott A Armstrong and Jane E Staunton and Lewis B Silverman and Rob Pieters and Monique L den Boer and Mark D Minden and Stephen E Sallan and Eric S Lander and Todd R Golub and Stanley J Korsmeyer},
	month = jan,
	year = {2002},
	keywords = {Acute Disease, Cell Lineage, {DNA-Binding} Proteins, Fusion, Gene Expression Profiling, Gene Expression Regulation, Genes, Genetic, Hematopoietic Stem Cells, Homeobox, Homeodomain Proteins, Humans, Immunophenotyping, Leukemia, Leukemic, Messenger, Myeloid, {Myeloid-Lymphoid} Leukemia Protein, Neoplasm, Neoplasm Proteins, Neoplastic Stem Cells, Oligonucleotide Array Sequence Analysis, Oncogene Proteins, Precursor Cell Lymphoblastic {Leukemia-Lymphoma,} {Proto-Oncogenes,} {RNA,} Transcription Factors, Translocation},
	pages = {41--7}
}

@book{silverman_density_1986,
	title = {Density Estimation for Statistics and Data Analysis},
	isbn = {0 412 24620 1},
	publisher = {Chapman and Hall},
	author = {{B.W.} Silverman},
	year = {1986}
}
