Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nucleic Acids Research Advance Access published May 16, 2012 Nucleic Acids Research, 2012, 1–7 doi:10.1093/nar/gks420 Cyber-T b server: differential analysis of high-throughput data Matthew A. Kayala and Pierre Baldi* Department of Computer Science and Institute for Genomics and Bioinformatics, University of California, Irvine. Irvine, CA 92697, USA Received February 18, 2012; Revised April 16, 2012; Accepted April 23, 2012 INTRODUCTION The Bayesian regularization method for high- throughput differential analysis, described in Baldi and Long (A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 2001: 17: 509-519) and implemented in the Cyber-T b server, is one of the most widely validated. Cyber-T implements a t-test using a Bayesian framework to compute a regularized variance of the measurements associated with each probe under each condition. This regularized estimate is derived by flexibly combining the empir- ical measurements with a prior, or background, derived from pooling measurements associated with probes in the same neighborhood. This approach flexibly addresses problems associated with low replication levels and technology biases, not only for DNA microarrays, but also for other technologies, such as protein arrays, quantitative mass spectrometry and next-generation sequenc- ing (RNA-seq). Here  present an update to the Cyber-T b server, incorporating several useful new additions and improvements. Several prepro- cessing data normalization options including log- arithmic and (Variance Stabilizing Normalization) VSN transforms are included. To augment two- sample t-tests, a one-way analysis of variance is implemented. Several methods for multiple tests correction, including standard frequentist methods and a probabilistic mixture model treatment, are available. Diagnostic plots allow visual assessment of the results. The b server provides comprehen- sive documentation and example data sets. The Cyber-T b server, with R source code and data sets, is publicly available at http://cybert.ics.uci. edu/. One of the most fundamental problems in bioinfor- matics and other sciences is the problem of differential analysis. Speciﬁcally, in its most typical bioinformatics form, this is the problem of identifying which RNA molecules, proteins, metabolites or other species behave differently beten two or more different condi- tions or treatments, given a set of corresponding meas- urements. Not only is this a fundamental problem as a prerequisite for identifying the underlying driving mech- anisms or deriving new diagnostic approaches, but it is also a recurrent problem both within and across differ- ent high-throughput technologies, ranging from DNA and protein microarrays, to mass spectrometry, to high-throughput sequencing in its various forms and ap- plications (e.g. ChIP-seq, RNA-seq). While, in general, the concentrations of the molecular species within one condition are not independent of each other, during a ﬁrst level of differential analysis, it is still useful to treat them as if they re independent to some extent. Furthermore, it is generally accepted that a simple fold-change approach to inference of differential behavior is not viable; rather, one must take into account the scale, i.e. the SDs, of the quantities being considered (1,2). The Cyber-T program and b server, originally de- veloped for DNA microarray data (3), addresses the problem of differential analysis in a way that is both ﬂexible and efﬁcient, using the entire set of measurements to overcome instrumental or experimental biases. The key to Cyber-T is an approach, derived from a Bayesian model, to robustly estimate the SDs, which can then be used to perform a regularized t-test. t-test and variance regularization More precisely, assume that, for instance, in the case of a gene X,  have a set of measurements x c 1 ; . . . ; x cn c and x t 1 ; . . . ; x tn t representing expression levels x c and x t , or rather their logarithms [or other normalized value (4)], in both a control and treatment situation. It is natural to assess the difference beten the two groups by the *To whom correspondence should be addressed. Tel: +1 949 824 5809; Fax: +1 949 824 9813; Email: pfbaldi@ics.uci.edu s The Author(s) 2012. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Downloaded from http://nar.oxfordjournals.org/ at University of California, Irvine on July 9, 2014 ABSTRACT
['Matthew A. Kayala', 'Pierre Baldi']
I am writing a research paper on a idea Cyber-T web server: differential analysis of high-throughput data
[{"from": "human", "value": "I am writing a research paper on a idea Cyber-T web server: differential analysis of high-throughput data"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely: Nucleic Acids Research Advance Access published May 16, 2012 Nucleic Acids Research, 2012, 1\u20137 doi:10.1093/nar/gks420 Cyber-T b server: differential analysis of high-throughput data Matthew A. Kayala and Pierre Baldi* Department of Computer Science and Institute for Genomics and Bioinformatics, University of California, Irvine. Irvine, CA 92697, USA Received February 18, 2012; Revised April 16, 2012; Accepted April 23, 2012 INTRODUCTION The Bayesian regularization method for high- throughput differential analysis, described in Baldi and Long (A Bayesian framework for the analysis of microarray expression data: regularized t-test and statistical inferences of gene changes. Bioinformatics 2001: 17: 509-519) and implemented in the Cyber-T b server, is one of the most widely validated. Cyber-T implements a t-test using a Bayesian framework to compute a regularized variance of the measurements associated with each probe under each condition. This regularized estimate is derived by flexibly combining the empir- ical measurements with a prior, or background, derived from pooling measurements associated with probes in the same neighborhood. This approach flexibly addresses problems associated with low replication levels and technology biases, not only for DNA microarrays, but also for other technologies, such as protein arrays, quantitative mass spectrometry and next-generation sequenc- ing (RNA-seq). Here  present an update to the Cyber-T b server, incorporating several useful new additions and improvements. Several prepro- cessing data normalization options including log- arithmic and (Variance Stabilizing Normalization) VSN transforms are included. To augment two- sample t-tests, a one-way analysis of variance is implemented. Several methods for multiple tests correction, including standard frequentist methods and a probabilistic mixture model treatment, are available. Diagnostic plots allow visual assessment of the results. The b server provides comprehen- sive documentation and example data sets. The Cyber-T b server, with R source code and data sets, is publicly available at http://cybert.ics.uci. edu/. One of the most fundamental problems in bioinfor- matics and other sciences is the problem of differential analysis. Speci\ufb01cally, in its most typical bioinformatics form, this is the problem of identifying which RNA molecules, proteins, metabolites or other species behave differently beten two or more different condi- tions or treatments, given a set of corresponding meas- urements. Not only is this a fundamental problem as a prerequisite for identifying the underlying driving mech- anisms or deriving new diagnostic approaches, but it is also a recurrent problem both within and across differ- ent high-throughput technologies, ranging from DNA and protein microarrays, to mass spectrometry, to high-throughput sequencing in its various forms and ap- plications (e.g. ChIP-seq, RNA-seq). While, in general, the concentrations of the molecular species within one condition are not independent of each other, during a \ufb01rst level of differential analysis, it is still useful to treat them as if they re independent to some extent. Furthermore, it is generally accepted that a simple fold-change approach to inference of differential behavior is not viable; rather, one must take into account the scale, i.e. the SDs, of the quantities being considered (1,2). The Cyber-T program and b server, originally de- veloped for DNA microarray data (3), addresses the problem of differential analysis in a way that is both \ufb02exible and ef\ufb01cient, using the entire set of measurements to overcome instrumental or experimental biases. The key to Cyber-T is an approach, derived from a Bayesian model, to robustly estimate the SDs, which can then be used to perform a regularized t-test. t-test and variance regularization More precisely, assume that, for instance, in the case of a gene X,  have a set of measurements x c 1 ; . . . ; x cn c and x t 1 ; . . . ; x tn t representing expression levels x c and x t , or rather their logarithms [or other normalized value (4)], in both a control and treatment situation. It is natural to assess the difference beten the two groups by the *To whom correspondence should be addressed. Tel: +1 949 824 5809; Fax: +1 949 824 9813; Email: pfbaldi@ics.uci.edu s The Author(s) 2012. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/ by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. Downloaded from http://nar.oxfordjournals.org/ at University of California, Irvine on July 9, 2014 ABSTRACT"}]