@article {17597009,
	title = {CLEAR-test: combining inference for differential expression and variability in microarray data analysis},
	journal = {J Biomed Inform},
	volume = {41},
	number = {1},
	year = {2008},
	note = {<p>Valls, Joan Grau, Monica Sole, Xavier Hernandez, Pilar Montaner, David Dopazo, Joaquin Peinado, Miguel A Capella, Gabriel Moreno, Victor Pujana, Miguel Angel Comparative Study Research Support, Non-U.S. Gov{\textquoteright}t United States Journal of biomedical informatics J Biomed Inform. 2008 Feb;41(1):33-45. Epub 2007 May 17.</p>},
	pages = {33-45},
	abstract = {<p>A common goal of microarray experiments is to detect genes that are differentially expressed under distinct experimental conditions. Several statistical tests have been proposed to determine whether the observed changes in gene expression are significant. The t-test assigns a score to each gene on the basis of changes in its expression relative to its estimated variability, in such a way that genes with a higher score (in absolute values) are more likely to be significant. Most variants of the t-test use the complete set of genes to influence the variance estimate for each single gene. However, no inference is made in terms of the variability itself. Here, we highlight the problem of low observed variances in the t-test, when genes with relatively small changes are declared differentially expressed. Alternatively, the z-test could be used although, unlike the t-test, it can declare differentially expressed genes with high observed variances. To overcome this, we propose to combine the z-test, which focuses on large changes, with a chi(2) test to evaluate variability. We call this procedure CLEAR-test and we provide a combined p-value that offers a compromise between both aspects. Analysis of three publicly available microarray datasets reveals the greater performance of the CLEAR-test relative to the t-test and alternative methods. Finally, empirical and simulated data analyses demonstrate the greater reproducibility and statistical power of the CLEAR-test and z-test with respect to current alternative methods. In addition, the CLEAR-test improves the z-test by capturing reproducible genes with high variability.</p>},
	keywords = {*Algorithms Artificial Intelligence *Data Interpretation, Statistical Gene Expression Profiling/*methods Gene Expression Regulation/*physiology Oligonucleotide Array Sequence Analysis/*methods Proteome/*metabolism Signal Transduction/*physiology},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=17597009},
	author = {Valls, J. and Grau, M. and Sole, X. and Hernandez, P. and Montaner, D. and Dopazo, J. and Peinado, M. A. and Capella, G. and Moreno, V. and Pujana, M. A.}
}
@article {17584915,
	title = {Evidence for systems-level molecular mechanisms of tumorigenesis},
	journal = {BMC Genomics},
	volume = {8},
	year = {2007},
	note = {Hernandez, Pilar Huerta-Cepas, Jaime Montaner, David Al-Shahrour, Fatima Valls, Joan Gomez, Laia Capella, Gabriel Dopazo, Joaquin Pujana, Miguel Angel Research Support, Non-U.S. Gov{\textquoteright}t England BMC genomics BMC Genomics. 2007 Jun 20;8:185.},
	pages = {185},
	abstract = {BACKGROUND: Cancer arises from the consecutive acquisition of genetic alterations. Increasing evidence suggests that as a consequence of these alterations, molecular interactions are reprogrammed in the context of highly connected and regulated cellular networks. Coordinated reprogramming would allow the cell to acquire the capabilities for malignant growth. RESULTS: Here, we determine the coordinated function of cancer gene products (i.e., proteins encoded by differentially expressed genes in tumors relative to healthy tissue counterparts, hereafter referred to as "CGPs") defined as their topological properties and organization in the interactome network. We show that CGPs are central to information exchange and propagation and that they are specifically organized to promote tumorigenesis. Centrality is identified by both local (degree) and global (betweenness and closeness) measures, and systematically appears in down-regulated CGPs. Up-regulated CGPs do not consistently exhibit centrality, but both types of cancer products determine the overall integrity of the network structure. In addition to centrality, down-regulated CGPs show topological association that correlates with common biological processes and pathways involved in tumorigenesis. CONCLUSION: Given the current limited coverage of the human interactome, this study proposes that tumorigenesis takes place in a specific and organized way at the molecular systems-level and suggests a model that comprises the precise down-regulation of groups of topologically-associated proteins involved in particular functions, orchestrated with the up-regulation of specific proteins.},
	keywords = {*Cell Transformation, Biological Models, Genetic Models, Messenger/metabolism Signal Transduction Systems Biology, Neoplastic *Gene Expression Profiling *Gene Expression Regulation, Neoplastic Humans Male Models, Statistical Neoplasm Proteins/*physiology Neoplasms/etiology/*genetics Prostatic Neoplasms/genetics Protein Interaction Mapping RNA},
	url = {http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?cmd=Retrieve\&db=PubMed\&dopt=Citation\&list_uids=17584915},
	author = {Hernandez, P. and Huerta-Cepas, J. and Montaner, D. and Fatima Al-Shahrour and Valls, J. and Gomez, L. and Capella, G. and Dopazo, J. and Pujana, M. A.}
}
