author = {{C}. {F}raley and {A}. {E}. {R}aftery},
  title = {{MCLUST} {V}ersion 3 for {R}: {N}ormal {M}ixture {M}odeling and {M}odel-{B}ased
  institution = {University of Washington},
  year = {2006},
  type = {Technical Report},
  number = {504},
  address = {Department of Statistics},
  note = {revised 2009}

  author = {Marcus Schmidt and Daniel Böhm and Christian von Törne and Eric Steiner
	and Alexander Puhl and Henryk Pilch and Hans-Anton Lehr and Jan G
	Hengstler and Heinz Kölbl and Mathias Gehrmann},
  title = {The humoral immune system has a key prognostic impact in node-negative
	breast cancer.},
  journal = {Cancer Res},
  year = {2008},
  volume = {68},
  pages = {5405--5413},
  number = {13},
  month = {Jul},
  __markedentry = {[anne:6]},
  abstract = {Estrogen receptor (ER) expression and proliferative activity are established
	prognostic factors in breast cancer. In a search for additional prognostic
	motifs, we analyzed the gene expression patterns of 200 tumors of
	patients who were not treated by systemic therapy after surgery using
	a discovery approach. After performing hierarchical cluster analysis,
	we identified coregulated genes related to the biological process
	of proliferation, steroid hormone receptor expression, as well as
	B-cell and T-cell infiltration. We calculated metagenes as a surrogate
	for all genes contained within a particular cluster and visualized
	the relative expression in relation to time to metastasis with principal
	component analysis. Distinct patterns led to the hypothesis of a
	prognostic role of the immune system in tumors with high expression
	of proliferation-associated genes. In multivariate Cox regression
	analysis, the proliferation metagene showed a significant association
	with metastasis-free survival of the whole discovery cohort [hazard
	ratio (HR), 2.20; 95\% confidence interval (95\% CI), 1.40-3.46].
	The B-cell metagene showed additional independent prognostic information
	in carcinomas with high proliferative activity (HR, 0.66; 95\% CI,
	0.46-0.97). A prognostic influence of the B-cell metagene was independently
	confirmed by multivariate analysis in a first validation cohort enriched
	for high-grade tumors (n = 286; HR, 0.78; 95\% CI, 0.62-0.98) and
	a second validation cohort enriched for younger patients (n = 302;
	HR, 0.83; 95\% CI, 0.7-0.97). Thus, we could show in three cohorts
	of untreated, node-negative breast cancer patients that the humoral
	immune system plays a pivotal role in metastasis-free survival of
	carcinomas of the breast.},
  doi = {10.1158/0008-5472.CAN-07-5206},
  institution = {Department of Obstetrics and Gynecology, Medical School, Johannes
	Gutenberg University, Mainz, Germany.},
  keywords = {Adult; Aged; Aged, 80 and over; Antibody Formation, physiology; Breast
	Neoplasms, diagnosis/genetics/immunology/pathology; Carcinoma, diagnosis/genetics/immunology/pathology;
	Cell Proliferation; Cluster Analysis; Cohort Studies; Female; Gene
	Expression Profiling; Gene Expression Regulation, Neoplastic; Genes,
	Neoplasm; Humans; Lymph Nodes, immunology/pathology; Lymphatic Metastasis;
	Middle Aged; Neutrophil Infiltration, genetics; Oligonucleotide Array
	Sequence Analysis; Prognosis},
  language = {eng},
  medline-pst = {ppublish},
  owner = {anne},
  pii = {68/13/5405},
  pmid = {18593943},
  timestamp = {2012.12.13},
  url = {http://dx.doi.org/10.1158/0008-5472.CAN-07-5206}

  author = {{C}. {F}raley and {A}. {E}. {R}aftery},
  title = {{M}odel-{B}ased {C}lustering, {D}iscriminant {A}nalysis and {D}ensity
  journal = {{J}ournal of the {A}merican {S}tatistical {A}ssociation},
  year = {2002},
  volume = {97},
  pages = {611-631}

  author = {Falcon, S. and Gentleman, R.},
  title = {{{U}sing {G}{O}stats to test gene lists for {G}{O} term association}},
  journal = {Bioinformatics},
  year = {2007},
  volume = {23},
  pages = {257--258},
  month = {Jan},
  abstract = {MOTIVATION: Functional analyses based on the association of Gene Ontology
	(GO) terms to genes in a selected gene list are useful bioinformatic
	tools and the GOstats package has been widely used to perform such
	computations. In this paper we report significant improvements and
	extensions such as support for conditional testing. RESULTS: We discuss
	the capabilities of GOstats, a Bioconductor package written in R,
	that allows users to test GO terms for over or under-representation
	using either a classical hypergeometric test or a conditional hypergeometric
	that uses the relationships among GO terms to decorrelate the results.
	AVAILABILITY: GOstats is available as an R package from the Bioconductor
	project: http://bioconductor.org},
  pmid = {17098774},
  url = {http://bioinformatics.oxfordjournals.org/cgi/content/full/23/2/257?view=long&pmid=17098774}

  author = {Su-In Lee and Serafim Batzoglou},
  title = {Application of independent component analysis to microarrays.},
  journal = {Genome Biol},
  year = {2003},
  volume = {4},
  pages = {R76},
  number = {11},
  abstract = {We apply linear and nonlinear independent component analysis (ICA)
	to project microarray data into statistically independent components
	that correspond to putative biological processes, and to cluster
	genes according to over- or under-expression in each component. We
	test the statistical significance of enrichment of gene annotations
	within clusters. ICA outperforms other leading methods, such as principal
	component analysis, k-means clustering and the Plaid model, in constructing
	functionally coherent clusters on microarray datasets from Saccharomyces
	cerevisiae, Caenorhabditis elegans and human.},
  doi = {10.1186/gb-2003-4-11-r76},
  institution = {Department of Computer Science, Stanford University, Stanford, CA94305-9010,
  keywords = {Algorithms; Animals; Caenorhabditis elegans; Cluster Analysis; Gene
	Expression Profiling; Humans; Models, Genetic; Oligonucleotide Array
	Sequence Analysis; Saccharomyces cerevisiae; Statistics as Topic,
	Independent Component Analysis},
  owner = {abiton},
  pii = {gb-2003-4-11-r76},
  pmid = {14611662},
  timestamp = {2010.11.09},
  url = {http://dx.doi.org/10.1186/gb-2003-4-11-r76}

  author = {{S}anchez-{C}arbayo, {M}. and {S}occi, {N}. {D}. and {L}ozano, {J}.
	and {S}aint, {F}. and {C}ordon-{C}ardo, {C}.},
  title = {{Defining molecular profiles of poor outcome in patients with invasive
	bladder cancer using oligonucleotide microarrays}},
  journal = {{J}. {C}lin. {O}ncol.},
  year = {2006},
  volume = {24},
  pages = {778--789},
  month = {Feb},
  abstract = {PURPOSE: Bladder cancer is a common malignancy characterized by a
	poor clinical outcome when tumors progress into invasive disease.
	We sought to define genetic signatures characteristic of aggressive
	clinical behavior in advanced bladder tumors. METHODS: Oligonucleotide
	arrays were utilized to analyze the transcript profiles of 105 bladder
	tumors: 33 superficial, 72 invasive lesions, and 52 normal urothelium.
	Hierarchical clustering and supervised algorithms were used to classify
	and stratify bladder tumors on the basis of stage, node metastases,
	and overall survival. Immunohistochemical analyses on bladder cancer
	tissue arrays (n = 294 cases) served to validate associations between
	marker expression, staging and outcome. RESULTS: Hierarchical clustering
	classified normal urothelium, superficial, and invasive tumors with
	82.2% accuracy, and stratified bladder tumors on the basis of clinical
	outcome. Predictive algorithms rendered an 89%-correct rate for tumor
	staging using genes differentially expressed between superficial
	and invasive tumors. Accuracies of 82% and 90% were obtained for
	predicting overall survival when considering all patients with bladder
	cancer or only patients with invasive disease, respectively. A genetic
	profile consisting of 174 probes was identified in those patients
	with positive lymph nodes and poor survival. Two independent Global
	Test runs confirmed the robust association of this profile with lymph
	node metastases (P = 7.3(-13)) and overall survival (P = 1.9(-14))
	simultaneously. Immunohistochemical analyses on tissue arrays sustained
	the significant association of synuclein with tumor staging and clinical
	outcome (P = .002). CONCLUSION: Gene profiling provides a genomic-based
	classification scheme of diagnostic and prognostic utility for stratifying
	advanced bladder cancer. Identification of this poor outcome profile
	could assist in selecting patients who may benefit from more aggressive
	therapeutic intervention.},
  url = {http://jco.ascopubs.org/content/24/5/778.long}

  author = {{T}eschendorff, {A}. {E}. and {J}ournee, {M}. and {A}bsil, {P}. {A}.
	and {S}epulchre, {R}. and {C}aldas, {C}.},
  title = {{Elucidating the altered transcriptional programs in breast cancer
	using independent component analysis}},
  journal = {{PL}o{S} {C}omput. {B}iol.},
  year = {2007},
  volume = {3},
  pages = {e161},
  month = {Aug},
  abstract = {The quantity of mRNA transcripts in a cell is determined by a complex
	interplay of cooperative and counteracting biological processes.
	Independent Component Analysis (ICA) is one of a few number of unsupervised
	algorithms that have been applied to microarray gene expression data
	in an attempt to understand phenotype differences in terms of changes
	in the activation/inhibition patterns of biological pathways. While
	the ICA model has been shown to outperform other linear representations
	of the data such as Principal Components Analysis (PCA), a validation
	using explicit pathway and regulatory element information has not
	yet been performed. We apply a range of popular ICA algorithms to
	six of the largest microarray cancer datasets and use pathway-knowledge
	and regulatory-element databases for validation. We show that ICA
	outperforms PCA and clustering-based methods in that ICA components
	map closer to known cancer-related pathways, regulatory modules,
	and cancer phenotypes. Furthermore, we identify cancer signalling
	and oncogenic pathways and regulatory modules that play a prominent
	role in breast cancer and relate the differential activation patterns
	of these to breast cancer phenotypes. Importantly, we find novel
	associations linking immune response and epithelial-mesenchymal transition
	pathways with estrogen receptor status and histological grade, respectively.
	In addition, we find associations linking the activity levels of
	biological pathways and transcription factors (NF1 and NFAT) with
	clinical outcome in breast cancer. ICA provides a framework for a
	more biologically relevant interpretation of genomewide transcriptomic
	data. Adopting ICA as the analysis tool of choice will help understand
	the phenotype-pathway relationship and thus help elucidate the molecular
	taxonomy of heterogeneous cancers and of other complex genetic diseases.},
  keywords = {Independent Component Analysis},
  url = {http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.0030161}

  author = {{S}aidi, {S}. {A}. and {H}olland, {C}. {M}. and {K}reil, {D}. {P}.
	and {M}ac{K}ay, {D}. {J}. and {C}harnock-{J}ones, {D}. {S}. and {P}rint,
	{C}. {G}. and {S}mith, {S}. {K}.},
  title = {{Independent component analysis of microarray data in the study of
	endometrial cancer}},
  journal = {{O}ncogene},
  year = {2004},
  volume = {23},
  pages = {6677--6683},
  month = {Aug},
  abstract = {Gene microarray technology is highly effective in screening for differential
	gene expression and has hence become a popular tool in the molecular
	investigation of cancer. When applied to tumours, molecular characteristics
	may be correlated with clinical features such as response to chemotherapy.
	Exploitation of the huge amount of data generated by microarrays
	is difficult, however, and constitutes a major challenge in the advancement
	of this methodology. Independent component analysis (ICA), a modern
	statistical method, allows us to better understand data in such complex
	and noisy measurement environments. The technique has the potential
	to significantly increase the quality of the resulting data and improve
	the biological validity of subsequent analysis. We performed microarray
	experiments on 31 postmenopausal endometrial biopsies, comprising
	11 benign and 20 malignant samples. We compared ICA to the established
	methods of principal component analysis (PCA), Cyber-T, and SAM.
	We show that ICA generated patterns that clearly characterized the
	malignant samples studied, in contrast to PCA. Moreover, ICA improved
	the biological validity of the genes identified as differentially
	expressed in endometrial carcinoma, compared to those found by Cyber-T
	and SAM. In particular, several genes involved in lipid metabolism
	that are differentially expressed in endometrial carcinoma were only
	found using this method. This report highlights the potential of
	ICA in the analysis of microarray data.},
  keywords = {Independent Component Analysis}

  author = {{F}rigyesi, {A}. and {V}eerla, {S}. and {L}indgren, {D}. and {H}oglund,
  title = {{Independent component analysis reveals new and biologically significant
	structures in micro array data}},
  journal = {{BMC} {B}ioinformatics},
  year = {2006},
  volume = {7},
  pages = {290},
  abstract = {BACKGROUND: An alternative to standard approaches to uncover biologically
	meaningful structures in micro array data is to treat the data as
	a blind source separation (BSS) problem. BSS attempts to separate
	a mixture of signals into their different sources and refers to the
	problem of recovering signals from several observed linear mixtures.
	In the context of micro array data, "sources" may correspond to specific
	cellular responses or to co-regulated genes. RESULTS: We applied
	independent component analysis (ICA) to three different microarray
	data sets; two tumor data sets and one time series experiment. To
	obtain reliable components we used iterated ICA to estimate component
	centrotypes. We found that many of the low ranking components indeed
	may show a strong biological coherence and hence be of biological
	significance. Generally ICA achieved a higher resolution when compared
	with results based on correlated expression and a larger number of
	gene clusters with significantly enriched for gene ontology (GO)
	categories. In addition, components characteristic for molecular
	subtypes and for tumors with specific chromosomal translocations
	were identified. ICA also identified more than one gene clusters
	significant for the same GO categories and hence disclosed a higher
	level of biological heterogeneity, even within coherent groups of
	genes. CONCLUSION: Although the ICA approach primarily detects hidden
	variables, these surfaced as highly correlated genes in time series
	data and in one instance in the tumor data. This further strengthens
	the biological relevance of latent variables detected by ICA.},
  keywords = {Independent Component Analysis},
  url = {http://www.ncbi.nlm.nih.gov/pubmed/16762055}

  author = {{J}ohan {H}imberg and {A}apo {H}yvärinen and {F}abrizio {E}sposito},
  title = {{V}alidating the independent components of neuroimaging time series
	via clustering and visualization.},
  journal = {{N}euroimage},
  year = {2004},
  volume = {22},
  pages = {1214--1222},
  number = {3},
  month = {Jul},
  abstract = {Recently, independent component analysis (ICA) has been widely used
	in the analysis of brain imaging data. An important problem with
	most ICA algorithms is, however, that they are stochastic; that is,
	their results may be somewhat different in different runs of the
	algorithm. Thus, the outputs of a single run of an ICA algorithm
	should be interpreted with some reserve, and further analysis of
	the algorithmic reliability of the components is needed. Moreover,
	as with any statistical method, the results are affected by the random
	sampling of the data, and some analysis of the statistical significance
	or reliability should be done as well. Here we present a method for
	assessing both the algorithmic and statistical reliability of estimated
	independent components. The method is based on running the ICA algorithm
	many times with slightly different conditions and visualizing the
	clustering structure of the obtained components in the signal space.
	In experiments with magnetoencephalographic (MEG) and functional
	magnetic resonance imaging (fMRI) data, the method was able to show
	that expected components are reliable; furthermore, it pointed out
	components whose interpretation was not obvious but whose reliability
	should incite the experimenter to investigate the underlying technical
	or physical phenomena. The method is implemented in a software package
	called Icasso.},
  doi = {10.1016/j.neuroimage.2004.03.027},
  institution = {Neural Networks Research Centre, Helsinki University of Technology,
	Helsinki, Finland.},
  keywords = {Algorithms; Brain; Cluster Analysis; Data Interpretation, Statistical;
	Fingers; Humans; Image Processing, Computer-Assisted; Magnetic Resonance
	Imaging; Magnetoencephalography; Models, Neurological; Motor Activity;
	Software, Independent Component Analysis},
  owner = {abiton},
  pii = {S1053811904001661},
  pmid = {15219593},
  timestamp = {2010.11.10},
  url = {http://dx.doi.org/10.1016/j.neuroimage.2004.03.027}

  author = {{M}elissa {S} {C}line and {M}ichael {S}moot and {E}than {C}erami
	and {A}llan {K}uchinsky and {N}erius {L}andys and {C}hris {W}orkman
	and {R}owan {C}hristmas and {I}liana {A}vila-{C}ampilo and {M}ichael
	{C}reech and {B}enjamin {G}ross and {K}ristina {H}anspers and {R}uth
	{I}sserlin and {R}yan {K}elley and {S}arah {K}illcoyne and {S}amad
	{L}otia and {S}teven {M}aere and {J}ohn {M}orris and {K}eiichiro
	{O}no and {V}uk {P}avlovic and {A}lexander {R} {P}ico and {A}ditya
	{V}ailaya and {P}eng-{L}iang {W}ang and {A}nnette {A}dler and {B}ruce
	{R} {C}onklin and {L}eroy {H}ood and {M}artin {K}uiper and {C}hris
	{S}ander and {I}lya {S}chmulevich and {B}enno {S}chwikowski and {G}uy
	{J} {W}arner and {T}rey {I}deker and {G}ary {D} {B}ader},
  title = {{I}ntegration of biological networks and gene expression data using
  journal = {{N}at {P}rotoc},
  year = {2007},
  volume = {2},
  pages = {2366--2382},
  number = {10},
  abstract = {Cytoscape is a free software package for visualizing, modeling and
	analyzing molecular and genetic interaction networks. This protocol
	explains how to use Cytoscape to analyze the results of mRNA expression
	profiling, and other functional genomics and proteomics experiments,
	in the context of an interaction network obtained for genes of interest.
	Five major steps are described: (i) obtaining a gene or protein network,
	(ii) displaying the network using layout algorithms, (iii) integrating
	with gene expression and other functional attributes, (iv) identifying
	putative complexes and functional modules and (v) identifying enriched
	Gene Ontology annotations in the network. These steps provide a broad
	sample of the types of analyses performed by Cytoscape.},
  doi = {10.1038/nprot.2007.324},
  institution = {Institut Pasteur, 25-28 rue du Docteur Roux, 75724 Paris cedex 15,
  keywords = {Computational Biology; Gene Expression Profiling; Gene Regulatory
	Networks; Genomics; Proteomics; RNA, Messenger; Software},
  owner = {abiton},
  pii = {nprot.2007.324},
  pmid = {17947979},
  timestamp = {2011.01.04},
  url = {http://dx.doi.org/10.1038/nprot.2007.324}