@ARTICLE{Eisenberg2003Human,
  author = {Eli Eisenberg and Erez Y Levanon},
  title = {Human housekeeping genes are compact.},
  journal = {Trends Genet},
  year = {2003},
  volume = {19},
  pages = {362--365},
  number = {7},
  month = {Jul},
  doi = {10.1016/S0168-9525(03)00140-9},
  institution = {Compugen Ltd, 72 Pinchas Rosen Street, Tel Aviv 69512, Israel. elie@compugen.co.il},
  keywords = {Chromosome Mapping; Humans; Introns; Metabolism, genetics; Selection,
	Genetic},
  language = {eng},
  medline-pst = {ppublish},
  owner = {laurent},
  pii = {S0168-9525(03)00140-9},
  pmid = {12850439},
  timestamp = {2014.06.03},
  url = {http://dx.doi.org/10.1016/S0168-9525(03)00140-9}
}

@TECHREPORT{Jacob2012Correcting,
  author = {Jacob, L. and Gagnon-Bartsch, J. and Speed, T. P.},
  title = {Correcting gene expression data when neither the unwanted variation nor the factor of interest are observed},
  institution = {arXiv},
  year = {2012},
  url = {http://arxiv.org/abs/1211.4259}
}

@ARTICLE{Cardoso2007MINDACT,
  author = {Cardoso, Fatima and Piccart-Gebhart, Martine and Van't Veer, Laura
	and Rutgers, Emiel and {TRANSBIG Consortium}},
  title = {{The MINDACT trial: the first prospective clinical validation of
	a genomic tool.}},
  journal = {Molecular oncology},
  year = {2007},
  volume = {1},
  pages = {246--251},
  number = {3},
  month = {Dec},
  abstract = {{One of the main challenges in oncology today has become to distinguish
	accurately between those patients who need adjuvant treatment and
	those who do not. This, together with the identification of the best
	type of therapy for the individual patient and the development of
	drugs targeting specific characteristics of tumour cells, are the
	goals of treatment tailoring or personalized medicine. The MINDACT
	trial (Microarray In Node negative Disease may Avoid ChemoTherapy)
	was recently launched with the aim of prospectively validating the
	superior performance of a new prognostic RNA-based tool--the Amsterdam
	70-gene profiler MammaPrint, in order to implement its use in clinical
	practice later on. This manuscript shortly reviews the rational,
	design and logistics of MINDACT.}},
  citeulike-article-id = {6487675},
  citeulike-linkout-0 = {http://dx.doi.org/10.1016/j.molonc.2007.10.004},
  citeulike-linkout-1 = {http://view.ncbi.nlm.nih.gov/pubmed/19383299},
  citeulike-linkout-2 = {http://www.hubmed.org/display.cgi?uids=19383299},
  doi = {10.1016/j.molonc.2007.10.004},
  issn = {1878-0261},
  keywords = {mindact, transbig},
  pmid = {19383299},
  posted-at = {2010-01-05 14:20:52},
  priority = {2},
  url = {http://dx.doi.org/10.1016/j.molonc.2007.10.004}
}

@ARTICLE{Network2008Comprehensive,
  author = {{Cancer Genome Atlas Research Network}},
  title = {Comprehensive genomic characterization defines human glioblastoma
	genes and core pathways},
  journal = {Nature},
  year = {2008},
  volume = {455},
  pages = {1061--1068},
  number = {7216},
  month = {Oct},
  abstract = {Human cancer cells typically harbour multiple chromosomal aberrations,
	nucleotide substitutions and epigenetic modifications that drive
	malignant transformation. The Cancer Genome Atlas (TCGA) pilot project
	aims to assess the value of large-scale multi-dimensional analysis
	of these molecular characteristics in human cancer and to provide
	the data rapidly to the research community. Here we report the interim
	integrative analysis of DNA copy number, gene expression and DNA
	methylation aberrations in 206 glioblastomas--the most common type
	of adult brain cancer--and nucleotide sequence aberrations in 91
	of the 206 glioblastomas. This analysis provides new insights into
	the roles of ERBB2, NF1 and TP53, uncovers frequent mutations of
	the phosphatidylinositol-3-OH kinase regulatory subunit gene PIK3R1,
	and provides a network view of the pathways altered in the development
	of glioblastoma. Furthermore, integration of mutation, DNA methylation
	and clinical treatment data reveals a link between MGMT promoter
	methylation and a hypermutator phenotype consequent to mismatch repair
	deficiency in treated glioblastomas, an observation with potential
	clinical implications. Together, these findings establish the feasibility
	and power of TCGA, demonstrating that it can rapidly expand knowledge
	of the molecular basis of cancer.},
  doi = {10.1038/nature07385},
  file = {Network2008Comprehensive.pdf:Network2008Comprehensive.pdf:PDF},
  owner = {jp},
  pii = {nature07385},
  pmid = {18772890},
  timestamp = {2008.11.26},
  url = {http://dx.doi.org/10.1038/nature07385}
}

@ARTICLE{Johnson2007Adjusting,
  author = {W. Evan Johnson and Cheng Li and Department Biostatistics and Computational
	Biology and Ariel Rabinovic},
  title = {Adjusting batch effects in microarray expression data using empirical
	Bayes methods},
  journal = {Biostatistics},
  year = {2007},
  volume = {1},
  pages = {118--127},
  number = {8}
}

@ARTICLE{Kang2008Accurate,
  author = {Hyun Min Kang and Chun Ye and Eleazar Eskin},
  title = {Accurate discovery of expression quantitative trait loci under confounding
	from spurious and genuine regulatory hotspots.},
  journal = {Genetics},
  year = {2008},
  volume = {180},
  pages = {1909--1925},
  number = {4},
  month = {Dec},
  abstract = {In genomewide mapping of expression quantitative trait loci (eQTL),
	it is widely believed that thousands of genes are trans-regulated
	by a small number of genomic regions called "regulatory hotspots,"
	resulting in "trans-regulatory bands" in an eQTL map. As several
	recent studies have demonstrated, technical confounding factors such
	as batch effects can complicate eQTL analysis by causing many spurious
	associations including spurious regulatory hotspots. Yet little is
	understood about how these technical confounding factors affect eQTL
	analyses and how to correct for these factors. Our analysis of data
	sets with biological replicates suggests that it is this intersample
	correlation structure inherent in expression data that leads to spurious
	associations between genetic loci and a large number of transcripts
	inducing spurious regulatory hotspots. We propose a statistical method
	that corrects for the spurious associations caused by complex intersample
	correlation of expression measurements in eQTL mapping. Applying
	our intersample correlation emended (ICE) eQTL mapping method to
	mouse, yeast, and human identifies many more cis associations while
	eliminating most of the spurious trans associations. The concordances
	of cis and trans associations have consistently increased between
	different replicates, tissues, and populations, demonstrating the
	higher accuracy of our method to identify real genetic effects.},
  doi = {10.1534/genetics.108.094201},
  institution = {Department of Human Genetics, University of California, Los Angeles,
	California 90095, USA.},
  keywords = {Animals; Computer Simulation; Databases, Genetic; Gene Expression;
	Gene Regulatory Networks; Genome; Humans; Mice; Mice, Inbred Strains;
	Quantitative Trait Loci; Saccharomyces cerevisiae},
  owner = {laurent},
  pii = {genetics.108.094201},
  pmid = {18791227},
  timestamp = {2012.07.23},
  url = {http://dx.doi.org/10.1534/genetics.108.094201}
}

@ARTICLE{Leek2007Capturing,
  author = {Jeffrey T Leek and John D Storey},
  title = {Capturing heterogeneity in gene expression studies by surrogate variable
	analysis.},
  journal = {PLoS Genet},
  year = {2007},
  volume = {3},
  pages = {1724--1735},
  number = {9},
  month = {Sep},
  abstract = {It has unambiguously been shown that genetic, environmental, demographic,
	and technical factors may have substantial effects on gene expression
	levels. In addition to the measured variable(s) of interest, there
	will tend to be sources of signal due to factors that are unknown,
	unmeasured, or too complicated to capture through simple models.
	We show that failing to incorporate these sources of heterogeneity
	into an analysis can have widespread and detrimental effects on the
	study. Not only can this reduce power or induce unwanted dependence
	across genes, but it can also introduce sources of spurious signal
	to many genes. This phenomenon is true even for well-designed, randomized
	studies. We introduce "surrogate variable analysis" (SVA) to overcome
	the problems caused by heterogeneity in expression studies. SVA can
	be applied in conjunction with standard analysis techniques to accurately
	capture the relationship between expression and any modeled variables
	of interest. We apply SVA to disease class, time course, and genetics
	of gene expression studies. We show that SVA increases the biological
	accuracy and reproducibility of analyses in genome-wide expression
	studies.},
  doi = {10.1371/journal.pgen.0030161},
  institution = {Department of Biostatistics, University of Washington, Seattle, Washington,
	USA.},
  keywords = {Algorithms; Breast Neoplasms; Computer Simulation; Data Interpretation,
	Statistical; Epigenesis, Genetic; Female; Gene Expression; Genes,
	BRCA1; Genes, BRCA2; Genetic Heterogeneity; Genome, Fungal; Genome,
	Human; Humans; Kidney; Linear Models; Linkage (Genetics); Mutation;
	Oligonucleotide Array Sequence Analysis; Quantitative Trait, Heritable;
	Reproducibility of Results; Saccharomyces cerevisiae; Time Factors},
  owner = {laurent},
  pii = {07-PLGE-RA-0237},
  pmid = {17907809},
  timestamp = {2010.07.01},
  url = {http://dx.doi.org/10.1371/journal.pgen.0030161}
}

@ARTICLE{Listgarten2010Correction,
  author = {Jennifer Listgarten and Carl Kadie and Eric E Schadt and David Heckerman},
  title = {Correction for hidden confounders in the genetic analysis of gene
	expression.},
  journal = {Proc Natl Acad Sci U S A},
  year = {2010},
  volume = {107},
  pages = {16465--16470},
  number = {38},
  month = {Sep},
  abstract = {Understanding the genetic underpinnings of disease is important for
	screening, treatment, drug development, and basic biological insight.
	One way of getting at such an understanding is to find out which
	parts of our DNA, such as single-nucleotide polymorphisms, affect
	particular intermediary processes such as gene expression. Naively,
	such associations can be identified using a simple statistical test
	on all paired combinations of genetic variants and gene transcripts.
	However, a wide variety of confounders lie hidden in the data, leading
	to both spurious associations and missed associations if not properly
	addressed. We present a statistical model that jointly corrects for
	two particular kinds of hidden structure--population structure (e.g.,
	race, family-relatedness), and microarray expression artifacts (e.g.,
	batch effects), when these confounders are unknown. Applying our
	method to both real and synthetic, human and mouse data, we demonstrate
	the need for such a joint correction of confounders, and also the
	disadvantages of other possible approaches based on those in the
	current literature. In particular, we show that our class of models
	has maximum power to detect eQTL on synthetic data, and has the best
	performance on a bronze standard applied to real data. Lastly, our
	software and the associations we found with it are available at http://www.microsoft.com/science.},
  doi = {10.1073/pnas.1002425107},
  institution = {Microsoft Research, 1100 Glendon Avenue, Suite PH1, Los Angeles,
	CA, USA. jennl@microsoft.com},
  keywords = {Animals; Databases, Genetic; Gene Expression; Genome-Wide Association
	Study; Genomics; Humans; Linear Models; Mice; Models, Genetic; Models,
	Statistical; Oligonucleotide Array Sequence Analysis; Polymorphism,
	Single Nucleotide; Quantitative Trait Loci; Software},
  owner = {laurent},
  pii = {1002425107},
  pmid = {20810919},
  timestamp = {2012.07.30},
  url = {http://dx.doi.org/10.1073/pnas.1002425107}
}

@ARTICLE{Gagnon-Bartsch2012Using,
  author = {Gagnon-Bartsch, Johann A. and Speed, Terence P.},
  title = {{Using control genes to correct for unwanted variation in microarray
	data}},
  journal = {Biostatistics},
  year = {2012},
  volume = {13},
  pages = {539--552},
  number = {3},
  month = {Jul},
  abstract = {{Microarray expression studies suffer from the problem of batch effects
	and other unwanted variation. Many methods have been proposed to
	adjust microarray data to mitigate the problems of unwanted variation.
	Several of these methods rely on factor analysis to infer the unwanted
	variation from the data. A central problem with this approach is
	the difficulty in discerning the unwanted variation from the biological
	variation that is of interest to the researcher. We present a new
	method, intended for use in differential expression studies, that
	attempts to overcome this problem by restricting the factor analysis
	to negative control genes. Negative control genes are genes known
	a priori not to be differentially expressed with respect to the biological
	factor of interest. Variation in the expression levels of these genes
	can therefore be assumed to be unwanted variation. We name this method
	"Remove Unwanted Variation, 2-step" (RUV-2). We discuss various techniques
	for assessing the performance of an adjustment method and compare
	the performance of RUV-2 with that of other commonly used adjustment
	methods such as Combat and Surrogate Variable Analysis (SVA). We
	present several example studies, each concerning genes differentially
	expressed with respect to gender in the brain and find that RUV-2
	performs as well or better than other methods. Finally, we discuss
	the possibility of adapting RUV-2 for use in studies not concerned
	with differential expression and conclude that there may be promise
	but substantial challenges remain.}},
  day = {01},
  doi = {10.1093/biostatistics/kxr034},
  issn = {1468-4357},
  keywords = {estimation, expression, testing, variation},
  pmid = {22101192},
  publisher = {Oxford University Press},
  url = {http://dx.doi.org/10.1093/biostatistics/kxr034}
}

@ARTICLE{Alter2000Singular,
  author = {O. Alter and P. O. Brown and D. Botstein},
  title = {Singular value decomposition for genome-wide expression data processing
	and modeling.},
  journal = {Proc Natl Acad Sci U S A},
  year = {2000},
  volume = {97},
  pages = {10101--10106},
  number = {18},
  month = {Aug},
  abstract = {We describe the use of singular value decomposition in transforming
	genome-wide expression data from genes x arrays space to reduced
	diagonalized "eigengenes" x "eigenarrays" space, where the eigengenes
	(or eigenarrays) are unique orthonormal superpositions of the genes
	(or arrays). Normalizing the data by filtering out the eigengenes
	(and eigenarrays) that are inferred to represent noise or experimental
	artifacts enables meaningful comparison of the expression of different
	genes across different arrays in different experiments. Sorting the
	data according to the eigengenes and eigenarrays gives a global picture
	of the dynamics of gene expression, in which individual genes and
	arrays appear to be classified into groups of similar regulation
	and function, or similar cellular state and biological phenotype,
	respectively. After normalization and sorting, the significant eigengenes
	and eigenarrays can be associated with observed genome-wide effects
	of regulators, or with measured samples, in which these regulators
	are overactive or underactive, respectively.},
  institution = {Departments of Genetics and Biochemistry, Stanford University, Stanford,
	CA 94305, USA. orly@genome.stanford.edu},
  keywords = {Cell Cycle; Genome; Genome, Fungal; Mathematics; Models, Biological;
	Models, Genetic; Models, Statistical; Oligonucleotide Array Sequence
	Analysis; Open Reading Frames; Saccharomyces cerevisiae},
  owner = {laurent},
  pii = {97/18/10101},
  pmid = {10963673},
  timestamp = {2010.06.23}
}

@ARTICLE{Benito2004Adjustment,
  author = {Monica Benito and Joel Parker and Quan Du and Junyuan Wu and Dong
	Xiang and Charles M Perou and J. S. Marron},
  title = {Adjustment of systematic microarray data biases.},
  journal = {Bioinformatics},
  year = {2004},
  volume = {20},
  pages = {105-14},
  number = {1},
  month = {Jan},
  abstract = {M{OTIVATION}: {S}ystematic differences due to experimental features
	of microarray experiments are present in most large microarray data
	sets. {M}any different experimental features can cause biases including
	different sources of {RNA}, different production lots of microarrays
	or different microarray platforms. {T}hese systematic effects present
	a substantial hurdle to the analysis of microarray data. {RESULTS}:
	{W}e present here a new method for the identification and adjustment
	of systematic biases that are present within microarray data sets.
	{O}ur approach is based on modern statistical discrimination methods
	and is shown to be very effective in removing systematic biases present
	in a previously published breast tumor c{DNA} microarray data set.
	{T}he new method of '{D}istance {W}eighted {D}iscrimination ({DWD})'
	is shown to be better than {S}upport {V}ector {M}achines and {S}ingular
	{V}alue {D}ecomposition for the adjustment of systematic microarray
	effects. {I}n addition, it is shown to be of general use as a tool
	for the discrimination of systematic problems present in microarray
	data sets, including the merging of two breast tumor data sets completed
	on different microarray platforms. {AVAILABILITY}: {M}atlab software
	to perform {DWD} can be retrieved from https://genome.unc.edu/pubsup/dwd/},
  keywords = {80 and over, Abstracting and Indexing, Aged, Algorithms, Amino Acid
	Sequence, Amino Acids, Animals, Antibody, Antigens, Aromatic, Artificial
	Intelligence, Atrophy, Automated, BRCA1, Bibliographic, Binding,
	Binding Sites, Brain, Brain Mapping, Breast, Breast Neoplasms, Carcinoma,
	Carrier Proteins, Cell Cycle Proteins, Chemical, Cluster Analysis,
	Comparative Study, Competitive, Complementary, Computational Biology,
	Computer Simulation, Computer-Assisted, Computers, Computing Methodologies,
	Controlled, DNA, DNA Damage, DNA-Binding Proteins, Data Interpretation,
	Database Management Systems, Databases, Diagnosis, Discriminant Analysis,
	Drug Design, Ductal, Electrostatics, Endoscopy, Epitopes, Evolution,
	Female, Fibroblasts, Fluorescence, Fourier Analysis, Gene Expression
	Profiling, Gene Expression Regulation, Genes, Genetic, HLA-DR Antigens,
	Heterozygote, Histocompatibility Antigens Class I, Humans, Hydrophobicity,
	Image Processing, Imaging, Information Storage and Retrieval, Isoelectric
	Point, Linear Models, Linguistics, Longitudinal Studies, MEDLINE,
	Magnetic Resonance Imaging, Male, Membrane Proteins, Middle Aged,
	Models, Molecular, Molecular Sequence Data, Multivariate Analysis,
	Mutation, Nasopharyngeal Neoplasms, Natural Language Processing,
	Neoplastic, Neural Networks (Computer), Non-P.H.S., Non-U.S. Gov't,
	Nonlinear Dynamics, Numerical Analysis, Oligonucleotide Array Sequence
	Analysis, Oligopeptides, P.H.S., Pattern Recognition, Peptide Fragments,
	Periodicals, Principal Component Analysis, Protein, Protein Binding,
	Protein Conformation, Protein Sorting Signals, Protein Structure,
	Protein-Serine-Threonine Kinases, Proteins, Proteome, Proteomics,
	Quality Control, RNA-Binding Proteins, ROC Curve, Randomized Controlled
	Trials, Reproducibility of Results, Research Support, Saccharomyces
	cerevisiae Proteins, Selection Bias, Sensitivity and Specificity,
	Sequence Alignment, Sequence Analysis, Software, Solvents, Spectrometry,
	Statistical, Structural Homology, Structure-Activity Relationship,
	T-Lymphocyte, Terminology, Tertiary, Three-Dimensional, Tumor Suppressor
	Proteins, U.S. Gov't, Variation (Genetics), Vocabulary, 14693816}
}

@ARTICLE{Vawter2004Gender,
  author = {Vawter, Marquis P. and Evans, Simon and Choudary, Prabhakara and
	Tomita, Hiroaki and Meador-Woodruff, Jim and Molnar, Margherita and
	Li, Jun and Lopez, Juan F. and Myers, Rick and Cox, David and Watson,
	Stanley J. and Akil, Huda and Jones, Edward G. and Bunney, William
	E.},
  title = {{Gender-specific gene expression in post-mortem human brain: localization
	to sex chromosomes.}},
  journal = {Neuropsychopharmacology},
  year = {2004},
  volume = {29},
  pages = {373--384},
  number = {2},
  month = {Feb},
  abstract = {{Gender differences in brain development and in the prevalence of
	neuropsychiatric disorders such as depression have been reported.
	Gender differences in human brain might be related to patterns of
	gene expression. Microarray technology is one useful method for investigation
	of gene expression in brain. We investigated gene expression, cell
	types, and regional expression patterns of differentially expressed
	sex chromosome genes in brain. We profiled gene expression in male
	and female dorsolateral prefrontal cortex, anterior cingulate cortex,
	and cerebellum using the Affymetrix oligonucleotide microarray platform.
	Differentially expressed genes between males and females on the Y
	chromosome (DBY, SMCY, UTY, RPS4Y, and USP9Y) and X chromosome (XIST)
	were confirmed using real-time PCR measurements. In situ hybridization
	confirmed the differential expression of gender-specific genes and
	neuronal expression of XIST, RPS4Y, SMCY, and UTY in three brain
	regions examined. The XIST gene, which silences gene expression on
	regions of the X chromosome, is expressed in a subset of neurons.
	Since a subset of neurons express gender-specific genes, neural subpopulations
	may exhibit a subtle sexual dimorphism at the level of differences
	in gene regulation and function. The distinctive pattern of neuronal
	expression of XIST, RPS4Y, SMCY, and UTY and other sex chromosome
	genes in neuronal subpopulations may possibly contribute to gender
	differences in prevalence noted for some neuropsychiatric disorders.
	Studies of the protein expression of these sex-chromosome-linked
	genes in brain tissue are required to address the functional consequences
	of the observed gene expression differences.}},
  citeulike-article-id = {4145483},
  citeulike-linkout-0 = {http://dx.doi.org/10.1038/sj.npp.1300337},
  citeulike-linkout-1 = {http://dx.doi.org/10.1038/1300337a},
  citeulike-linkout-2 = {http://view.ncbi.nlm.nih.gov/pubmed/14583743},
  citeulike-linkout-3 = {http://www.hubmed.org/display.cgi?uids=14583743},
  day = {01},
  doi = {10.1038/sj.npp.1300337},
  issn = {0893-133X},
  keywords = {sex},
  pmid = {14583743},
  posted-at = {2010-05-31 22:11:09},
  priority = {2},
  publisher = {Nature Publishing Group},
  url = {http://dx.doi.org/10.1038/sj.npp.1300337}
}

@ARTICLE{Mairal2010Sparse,
  author = {Julien Mairal and Francis Bach and Jean Ponce and Guillermo Sapiro},
  title = {Online Learning for Matrix Factorization and Sparse Coding},
  journal = {Journal of Machine Learning Research},
  year = {2010},
  volume = {11},
  pages = {19-60},
  bibsource = {DBLP, http://dblp.uni-trier.de},
  ee = {http://doi.acm.org/10.1145/1756006.1756008}
}
