@article{haenzelmann2013gsva,
	author = {Sonja H{\"a}nzelmann and Robert Castelo and Justin Guinney},
	title = {{GSVA}: gene set variation analysis for microarray and {RNA-Seq} data},
	journal = {BMC Bioinformatics},
  volume = {14},
  pages = {7},
	year = {2013},
  url = {http://www.biomedcentral.com/1471-2105/14/7},
  doi = {10.1186/1471-2105-14-7}}

@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{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 {G}aussian 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 {G}aussian 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}
}

@article{gronemeyer_principles_2004,
	title = {Principles for modulation of the nuclear receptor superfamily},
	volume = {3},
	issn = {1474-1776},
	url = {http://dx.doi.org/10.1038/nrd1551},
	doi = {10.1038/nrd1551},
	number = {11},
	journal = {Nat Rev Drug Discov},
	author = {Gronemeyer, Hinrich and Gustafsson, {Jan-Ake} and Laudet, Vincent},
	month = nov,
	year = {2004},
	pages = {950--964}
}

@article{thompson_maternal_2001,
	title = {Maternal folate supplementation in pregnancy and protection against acute lymphoblastic leukaemia in childhood: a case-control study},
	volume = {358},
	journal = {Lancet},
	author = {Thompson, J. R and Gerald, P. F and Willoughby, M. L and Armstrong, B. K},
	month = dec,
	year = {2001},
	pages = {1935--1940}
}

@article{mullican_abrogation_2007,
	title = {Abrogation of nuclear receptors Nr4a3 {andNr4a1} leads to development of acute myeloid leukemia},
	volume = {13},
	issn = {1078-8956},
	url = {http://dx.doi.org/10.1038/nm1579},
	doi = {10.1038/nm1579},
	number = {6},
	journal = {Nat Med},
	author = {Mullican, Shannon E and Zhang, Shuo and Konopleva, Marina and Ruvolo, Vivian and Andreeff, Michael and Milbrandt, Jeffrey and Conneely, Orla M},
	month = jun,
	year = {2007},
	pages = {730--735}
}

@article{glasow_dna_2008,
	title = {{DNA} methylation-independent loss of {RARA} gene expression in acute myeloid leukemia},
	volume = {111},
	url = {http://bloodjournal.hematologylibrary.org/content/111/4/2374.abstract},
	doi = {10.1182/blood-2007-05-088344},
	abstract = {The retinoic acid receptor {(RAR)} α gene {(RARA)} encodes 2 major isoforms and mediates positive effects of all-trans retinoic acid {(ATRA)} on myelomonocytic differentiation. Expression of the {ATRA-inducible} {(RARα2)} isoform increases with myelomonocytic differentiation and appears to be down-regulated in many acute myeloid leukemia {(AML)} cell lines. Here, we demonstrate that relative to normal myeloid stem/progenitor cells, {RARα2} expression is dramatically reduced in primary {AML} blasts. Expression of the {RARα1} isoform is also significantly reduced in primary {AML} cells, but not in {AML} cell lines. Although the promoters directing expression of {RARα1} and {RARα2} are respectively unmethylated and methylated in {AML} cell lines, these regulatory regions are unmethylated in all the {AML} patient cell samples analyzed. Moreover, in primary {AML} cells, histones associated with the {RARα2} promoter possessed diminished levels of H3 acetylation and lysine 4 methylation. These results underscore the complexities of the mechanisms responsible for deregulation of gene expression in {AML} and support the notion that diminished {RARA} expression contributes to leukemogenesis.},
	number = {4},
	journal = {Blood},
	author = {Glasow, Annegret and Barrett, Angela and Petrie, Kevin and Gupta, Rajeev and {Boix-Chornet}, Manuel and Zhou, {Da-Cheng} and Grimwade, David and Gallagher, Robert and von Lindern, Marieke and Waxman, Samuel and Enver, Tariq and Hildebrandt, Guido and Zelent, Arthur},
	month = feb,
	year = {2008},
	pages = {2374 --2377}
}

@article{ansari_mixed_2010,
	title = {Mixed lineage leukemia: roles in gene expression, hormone signaling and {mRNA} processing},
	volume = {277},
	issn = {1742-4658},
	shorttitle = {Mixed lineage leukemia},
	url = {http://onlinelibrary.wiley.com/doi/10.1111/j.1742-4658.2010.07606.x/abstract},
	doi = {10.1111/j.1742-4658.2010.07606.x},
	abstract = {Mixed lineage leukemias {(MLLs)} are an evolutionarily conserved trithorax family of human genes that play critical roles in {HOX} gene regulation and embryonic development. {MLL1} is well known to be rearranged in myeloid and lymphoid leukemias in children and adults. There are several {MLL} family proteins such as {MLL1}, {MLL2}, {MLL3}, {MLL4}, {MLL5}, {Set1A} and {Set1B}, and each possesses histone H3 lysine 4 {(H3K4)-specific} methyltransferase activity and has critical roles in gene activation and epigenetics. Although {MLLs} are recognized as major regulators of gene activation, their mechanism of action, target genes and the distinct functions of different {MLLs} remain elusive. Recent studies demonstrate that besides {H3K4} methylation and {HOX} gene regulation, {MLLs} have much wider roles in gene activation and regulate diverse other genes. Interestingly, several {MLLs} interact with nuclear receptors and have critical roles in steroid-hormone-mediated gene activation and signaling. In this minireview, we summarize recent advances in understanding the roles of {MLLs} in gene regulation and hormone signaling and highlight their potential roles in {mRNA} processing.},
	number = {8},
	journal = {{FEBS} Journal},
	author = {Ansari, Khairul I and Mandal, Subhrangsu S},
	month = apr,
	year = {2010},
	keywords = {epigenetics, estrogen receptor, gene expression, histone methyltransferase, hormone signaling, mixed lineage leukemia, {mRNA} processing, {NR‐box}, nuclear receptor, {SET} domain},
	pages = {1790--1804}
}

@article{turner_fibroblast_2010,
	title = {Fibroblast growth factor signalling: from development to cancer},
	volume = {10},
	issn = {{1474-175X}},
	shorttitle = {Fibroblast growth factor signalling},
	url = {http://dx.doi.org/10.1038/nrc2780},
	doi = {10.1038/nrc2780},
	number = {2},
	journal = {Nat Rev Cancer},
	author = {Turner, Nicholas and Grose, Richard},
	month = feb,
	year = {2010},
	pages = {116--129}
}

@article{greulich_targeting_2011,
	title = {Targeting mutant fibroblast growth factor receptors in cancer},
	volume = {17},
	issn = {1471-4914},
	url = {http://www.sciencedirect.com/science/article/pii/S147149141100013X},
	doi = {16/j.molmed.2011.01.012},
	abstract = {{{\textless}p{\textgreater}{\textless}br/{\textgreater}Fibroblast} growth factor receptors {(FGFRs)} play diverse roles in the control of cell proliferation, cell differentiation, angiogenesis and development. Activating the mutations of {FGFRs} in the germline has long been known to cause a variety of skeletal developmental disorders, but it is only recently that a similar spectrum of somatic {FGFR} mutations has been associated with human cancers. Many of these somatic mutations are gain-of-function and oncogenic and create dependencies in tumor cell lines harboring such mutations. A combination of knockdown studies and pharmaceutical inhibition in preclinical models has further substantiated genomically altered {FGFR} as a therapeutic target in cancer, and the oncology community is responding with clinical trials evaluating multikinase inhibitors with {anti-FGFR} activity and a new generation of specific {pan-FGFR} inhibitors.{\textless}/p{\textgreater}},
	number = {5},
	journal = {Trends in Molecular Medicine},
	author = {Greulich, Heidi and Pollock, Pamela M.},
	month = may,
	year = {2011},
	pages = {283--292}
}

@article{lightfoot_genetic_2010,
	title = {Genetic variation in the folate metabolic pathway and risk of childhood leukemia},
	volume = {115},
	doi = {10.1182/blood-2009-10-249722},
	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{\textbackslash}textgreaterT}, {MTHFR} 1298 {A{\textbackslash}textgreaterC}, {SHMT1} 1420 {C{\textbackslash}textgreaterT}, {MTR} 2756 {A{\textbackslash}textgreaterG}, {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.},
	number = {19},
	journal = {Blood},
	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 Study, United Kingdom Childhood Cancer},
	month = may,
	year = {2010},
	pages = {3923--9}
}

@inproceedings{Roverato_gnrr_2010,
	Author = {Roverato, Alberto and Castelo, Robert},
	Title = {Learning undirected graphical models from multiple datasets with the generalized non-rejection rate},
  Booktitle = {Proceedings of the Fifth European Workshop on Probabilistic Graphical Models},
  Editor = {Myllym\"aki, Peter and Roos, Temu and Jaakkola, Tomi},
	Pages = {249-56},
  Publisher = {HIIT Publications},
  Address = {Helsinki},
	Year = {2010},
}

@article{zilliox_gene_2007,
  title = {A gene expression bar code for microarray data},
  volume = {4},
  url = {http://dx.doi.org/10.1038/nmeth1102},
  number = {11},
  journal = {Nat Meth},
  author = {Zilliox, Michael J and Irizarry, Rafael A},
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}

@article{hansen_removing_2012,
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}

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}

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@article{edelman_analysis_2006,
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}

@article{pearson_comparison_1963,
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}

@article{lee_inferring_2008,
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}

@article{barbie_systematic_2009,
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}

@article{huang_genome-wide_2007,
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}

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}

@article{carrel_x-inactivation_2005,
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}

@article{skaletsky_male-specific_2003,
  title = {The male-specific region of the human Y chromosome is a mosaic of discrete sequence classes},
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  author = {Skaletsky, Helen and {Kuroda-Kawaguchi}, Tomoko and Minx, Patrick J. and Cordum, Holland S. and Hillier, {LaDeana} and Brown, Laura G. and Repping, Sjoerd and Pyntikova, Tatyana and Ali, Johar and Bieri, Tamberlyn and Chinwalla, Asif and Delehaunty, Andrew and Delehaunty, Kim and Du, Hui and Fewell, Ginger and Fulton, Lucinda and Fulton, Robert and Graves, Tina and Hou, {Shun-Fang} and Latrielle, Philip and Leonard, Shawn and Mardis, Elaine and Maupin, Rachel and {McPherson}, John and Miner, Tracie and Nash, William and Nguyen, Christine and Ozersky, Philip and Pepin, Kymberlie and Rock, Susan and Rohlfing, Tracy and Scott, Kelsi and Schultz, Brian and Strong, Cindy and {Tin-Wollam}, Aye and Yang, {Shiaw-Pyng} and Waterston, Robert H. and Wilson, Richard K. and Rozen, Steve and Page, David C.},
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}

@article{tomfohr_pathway_2005,
	title = {Pathway level analysis of gene expression using singular value decomposition},
	volume = {6},
	issn = {1471-2105},
	url = {http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1261155/},
	doi = {10.1186/1471-2105-6-225},
	abstract = {Background
A promising direction in the analysis of gene expression focuses on the changes in expression of specific predefined sets of genes that are known in advance to be related (e.g., genes coding for proteins involved in cellular pathways or complexes). Such an analysis can reveal features that are not easily visible from the variations in the individual genes and can lead to a picture of expression that is more biologically transparent and accessible to interpretation. In this article, we present a new method of this kind that operates by quantifying the level of 'activity' of each pathway in different samples. The activity levels, which are derived from singular value decompositions, form the basis for statistical comparisons and other applications.

Results
We demonstrate our approach using expression data from a study of type 2 diabetes and another of the influence of cigarette smoke on gene expression in airway epithelia. A number of interesting pathways are identified in comparisons between smokers and non-smokers including ones related to nicotine metabolism, mucus production, and glutathione metabolism. A comparison with results from the related approach, 'gene-set enrichment analysis', is also provided.

Conclusion
Our method offers a flexible basis for identifying differentially expressed pathways from gene expression data. The results of a pathway-based analysis can be complementary to those obtained from one more focused on individual genes. A web program {PLAGE} {(Pathway} Level Analysis of Gene Expression) for performing the kinds of analyses described here is accessible at .},
	urldate = {2012-09-14},
	journal = {{BMC} Bioinformatics},
	author = {Tomfohr, John and Lu, Jun and Kepler, Thomas B},
	month = sep,
	year = {2005},
	note = {{PMID:} 16156896
{PMCID:} {PMC1261155}},
	pages = {225},
	file = {PubMed Central Full Text PDF:/home/sonja/.mozilla/firefox/m25utlhy.default/zotero/storage/CQQRZ2UB/Tomfohr et al. - 2005 - Pathway level analysis of gene expression using si.pdf:application/pdf}
}

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  journal={The annals of applied statistics},
  volume={10},
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  pages={946},
  year={2016},
  doi={10.1214/16-AOAS920}
}

@article{costa2021genome,
  title={Genome-wide postnatal changes in immunity following fetal inflammatory response},
  author={Costa, Daniel and Bonet, N{\'u}ria and Sol{\'e}, Amanda and Gonz{\'a}lez de Aledo-Castillo, Jos{\'e} Manuel and Sabid{\'o}, Eduard and Casals, Ferran and Rovira, Carlota and Nadal, Alfons and Marin, Jose Luis and Cobo, Teresa and others},
  journal={The FEBS Journal},
  volume={288},
  number={7},
  pages={2311--2331},
  year={2021},
  publisher={Wiley Online Library},
  doi={10.1111/febs.15578}
}

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  title={Capturing heterogeneity in gene expression studies by surrogate variable analysis},
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  doi={10.1371/journal.pgen.0030161}
}

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  title={Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R},
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  publisher={Oxford University Press}
}

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  title={Evaluation of methods to assign cell type labels to cell clusters from single-cell RNA-sequencing data},
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  year={2019}
}

@article{newman2015robust,
  title={Robust enumeration of cell subsets from tissue expression profiles},
  author={Newman, Aaron M and Liu, Chih Long and Green, Michael R and Gentles, Andrew J and Feng, Weiguo and Xu, Yue and Hoang, Chuong D and Diehn, Maximilian and Alizadeh, Ash A},
  journal={Nature methods},
  volume={12},
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  pages={453--457},
  year={2015},
  publisher={Nature Publishing Group US New York}
}

@article{zheng2017massively,
  title={Massively parallel digital transcriptional profiling of single cells},
  author={Zheng, Grace XY and Terry, Jessica M and Belgrader, Phillip and Ryvkin, Paul and Bent, Zachary W and Wilson, Ryan and Ziraldo, Solongo B and Wheeler, Tobias D and McDermott, Geoff P and Zhu, Junjie and others},
  journal={Nature communications},
  volume={8},
  number={1},
  pages={14049},
  year={2017},
  publisher={Nature Publishing Group UK London}
}

@article{lun2016step,
  title={A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor},
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}

@article{csardi2006igraph,
    title = {The igraph software package for complex network research},
    author = {Gabor Csardi and Tamas Nepusz},
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    volume = {Complex Systems},
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    url = {https://igraph.org},
}

@article{amezquita2020orchestrating,
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  year={2020},
  publisher={Nature Publishing Group US New York}
}
