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A multivariate empirical Bayes statistic for replicated microarray time course data

Yu Chuan Tai, Terence P. Speed
2006 Annals of Statistics  
time course experiments.  ...  In this paper we derive one- and two-sample multivariate empirical Bayes statistics (the $\mathit{MB}$-statistics) to rank genes in order of interest from longitudinal replicated developmental microarray  ...  Finally, we would like to acknowledge John Ngai, Karen Vranizan, Mary Wildermuth, Moriah Szpara, Carina Howell, and Jason Dugas for their helpful discussions on biological background of time course experiments  ... 
doi:10.1214/009053606000000759 fatcat:5hymugswjvhuzfdtoyo5ra5mwe

A computational toxicogenomics approach identifies a list of highly hepatotoxic compounds from a large microarray database

Héctor A. Rueda-Zárate, Iván Imaz-Rosshandler, Roberto A. Cárdenas-Ovando, Juan E. Castillo-Fernández, Julieta Noguez-Monroy, Claudia Rangel-Escareño, Alok Deoraj
2017 PLoS ONE  
In this study, a large collection of microarray data is used to investigate gene expression changes associated with hepatotoxicity.  ...  We combined machine learning algorithms with time series analysis to identify genes capable of classifying compounds by FDA-approved labeling as DILI-concern toxic.  ...  Acknowledgments The authors want to thank Edith Fernandez-Figueroa and Said Muñoz-Montero for their valuable feedback in writing this manuscript. Methodology: CRE HRZ IIR.  ... 
doi:10.1371/journal.pone.0176284 pmid:28448553 pmcid:PMC5407788 fatcat:knm3scmm6vbrnahh5b6lrsa3de

METHODS USED FOR IDENTIFICATION OF DIFFERENTIALLY EXPRESSING GENES (DEGS) FROM MICROARRAY GENE DATASET: A REVIEW

Chanda Panse, Manali Kshirsagar, Dhananjay Raje3, Dipak Wajgi
2020 Zenodo  
Advanced technology like microarray plays an important role in gene analysis as it captures expressions of thousands of genes under different conditions simultaneously.  ...  They help in planning therapeutic strategies for a disease through Gene Regulatory Network (GRN) constructed from them.  ...  In order to extract useful biological knowledge from large microarray data, multivariate data analysis is needed as it reduces dimensions.  ... 
doi:10.5281/zenodo.4275146 fatcat:rik66npjmfegpfqmwuhwmdwqv4

An improved empirical bayes approach to estimating differential gene expression in microarray time-course data: BETR (Bayesian Estimation of Temporal Regulation)

Martin J Aryee, José A Gutiérrez-Pabello, Igor Kramnik, Tapabrata Maiti, John Quackenbush
2009 BMC Bioinformatics  
Acknowledgements We thank Thomas Hoffmann for assistance with microarray data preprocessing, Louise Ryan for helpful discussions about statistical methodology, and James Sissons and Alexander Pichugin  ...  for valuable insights into tuberculosis pathogenesis.  ...  Background The analysis of microarray time-course data presents a number of challenges.  ... 
doi:10.1186/1471-2105-10-409 pmid:20003283 pmcid:PMC2801687 fatcat:lca25r4k3rgf3a7e4gusgiqtwq

Reduced Expression of CAMTA1 Correlates with Adverse Outcome in Neuroblastoma Patients

K.-O. Henrich
2006 Clinical Cancer Research  
Conclusions: Our data suggest that assessment of CAMTA1 expression may improve the prognostic models for neuroblastoma and that it will be important to define the biological function of CAMTA1in this disease  ...  Experimental Design: Candidate genes localized within the deleted region were identified by sequence data analysis.Their expression was assessed in a cohort of 49 primary neuroblastomas using cDNA microarray  ...  Acknowledgments We thank Yvonne Kahlert for excellent technical assistance.  ... 
doi:10.1158/1078-0432.ccr-05-1431 pmid:16397034 fatcat:qkmu7lsrevdb7dnxvziczs2ole

tigaR: integrative significance analysis of temporal differential gene expression induced by genomic abnormalities

Viktorian Miok, Saskia M Wilting, Mark A van de Wiel, Annelieke Jaspers, Paula I van Noort, Ruud H Brakenhoff, Peter JF Snijders, Renske DM Steenbergen, Wessel N van Wieringen
2014 BMC Bioinformatics  
Finally, the proposed method is able to handle count (RNAseq) data from time course experiments as is shown on a real data set.  ...  Conclusion: With the proposed method for analysis of time-course multilevel molecular data, more profound insight may be gained through the identification of temporal differential expression induced by  ...  Tai and Speed [9] use multivariate empirical Bayes statistics to rank time-course gene expression profiles.  ... 
doi:10.1186/1471-2105-15-327 pmid:25278371 pmcid:PMC4288633 fatcat:3xdmvvgttbdezamzkmaoor66y4

A Three-Gene Expression Signature Model for Risk Stratification of Patients with Neuroblastoma

I. Garcia, G. Mayol, J. Rios, G. Domenech, N.-K. V. Cheung, A. Oberthuer, M. Fischer, J. M. Maris, G. M. Brodeur, B. Hero, E. Rodriguez, M. Sunol (+4 others)
2012 Clinical Cancer Research  
Experimental Design: The model was developed using real-time PCR gene expression data from 96 samples and tested on separate expression data sets obtained from real-time PCR and microarray studies comprising  ...  Multivariate analysis showed that the model was an independent marker for survival (P < 0.001, for all).  ...  P erez-Mart nez and I. de Prada Vicente (Hospital Niño Jes us, Madrid, Spain) for annotated neuroblastoma specimens.  ... 
doi:10.1158/1078-0432.ccr-11-2483 pmid:22328561 pmcid:PMC4240975 fatcat:ybfdfu2hnbf2de5bnv3rnjjwse

Correspondence analysis of microarray time-course data in case–control design

Qihua Tan, Klaus Brusgaard, Torben A. Kruse, Edward Oakeley, Brian Hemmings, Henning Beck-Nielsen, Lars Hansen, Michael Gaster
2004 Journal of Biomedical Informatics  
In this paper, we introduce a new multivariate data analyzing technique, the correspondence analysis, to analyze the high dimensional microarray time-course data in case-control design.  ...  Although different statistical approaches have been proposed for analyzing microarray time-course data, method for analyzing such data collected using the popular case-control design in clinical investigations  ...  We also thank Irene Lynfort for providing excellent technical assistance and Kurt Højlund for the muscle biopsies.  ... 
doi:10.1016/j.jbi.2004.06.001 pmid:15488749 fatcat:hhprjvmsszgwngaymcmyz4mox4

m:Explorer: multinomial regression models reveal positive and negative regulators of longevity in yeast quiescence

Jüri Reimand, Anu Aun, Jaak Vilo, Juan M Vaquerizas, Juhan Sedman, Nicholas M Luscombe
2012 Genome Biology  
We predicted and experimentally tested regulators of quiescence (G 0 ), a model of ageing, over a six-week time-course.  ...  We developed m:Explorer for identifying process-specific transcription factors (TFs) from multiple genome-wide sources, including transcriptome, DNA-binding and chromatin data. m:Explorer robustly outperforms  ...  Acknowledgements We are grateful to Gary Bader, Richard Bourgon, John Marioni, Gabriella Rustici, and Annabel Todd for their valuable advice, and to Tambet Arak for his work on the m:Explorer website.  ... 
doi:10.1186/gb-2012-13-6-r55 pmid:22720667 pmcid:PMC3446321 fatcat:saf2ggraffbwjnijrpb7nea6s4

Pareto-Optimal Methods for Gene Ranking

Alfred O. Hero, Gilles Fleury
2004 Journal of VLSI Signal Processing Systems for Signal, Image and Video Technology  
Both a model-driven Bayesian Pareto method and a data-driven non-parametric Pareto method, based on rank-order statistics, are presented.  ...  The massive scale and variability of microarray gene data creates new and challenging problems of signal extraction, gene clustering, and data mining, especially for temporal gene profiles.  ...  The authors also thank Terry Speed, UC Berkeley, and Fred Wright, UCLA, for their comments on this work.  ... 
doi:10.1023/b:vlsi.0000042491.03225.cf fatcat:vwg2v24rbzhmfouhsz7jg2qxgq

Time-course analysis of genome-wide gene expression data from hormone-responsive human breast cancer cells

Margherita Mutarelli, Luigi Cicatiello, Lorenzo Ferraro, Olì MV Grober, Maria Ravo, Angelo M Facchiano, Claudia Angelini, Alessandro Weisz
2008 BMC Bioinformatics  
Results: We compared here four different methods to analyze data derived from a time course mRNA expression profiling experiment which consisted in the study of the effects of estrogen on hormone-responsive  ...  The potential use of the statistical analysis of microarray data in time series has not been fully exploited so far, due to the fact that only few methods are available which take into proper account temporal  ...  The second method uses a novel multivariate empirical Bayes approach to rank genes in the order of interest from longitudinal replicated microarray time course experiments [16] (implemented in the Bioconductor  ... 
doi:10.1186/1471-2105-9-s2-s12 pmid:18387200 pmcid:PMC2323661 fatcat:q2xhlo5zm5ceddwnscgmrgubq4

Analysing time course microarray data using Bioconductor: a case study using yeast2 Affymetrix arrays

Colin S Gillespie, Guiyuan Lei, Richard J Boys, Amanda Greenall, Darren J Wilkinson
2010 BMC Research Notes  
However, proper statistical analysis of time-course data requires the use of more sophisticated tools and complex statistical models.  ...  Findings: Using the open source CRAN and Bioconductor repositories for R, we provide example analysis and protocol which illustrate a variety of methods that can be used to analyse time-course microarray  ...  Acknowledgements We wish to thank Dan Swan (Newcastle University Bioinformatics Support Unit) and David Lydall for helpful discussions.  ... 
doi:10.1186/1756-0500-3-81 pmid:20302631 pmcid:PMC2880961 fatcat:tt7p3ykvfng7loqal6egqc4iji

A feature selection strategy for gene expression time series experiments with hidden Markov models [article]

Roberto A. Cárdenas-Ovando, Edith A. Fernández-Figueroa, Héctor R. Rueda-Zárate, Julieta Noguez, Claudia Rangel-Escareño
2018 bioRxiv   pre-print
We propose a feature selection algorithm embedded in a hidden Markov model applied to gene expression time course data on either single or even multiple biological conditions.  ...  However, when it comes to genomic data, time points are sparse creating the need for a constant search for methods capable of extracting information out of experiments of this kind.  ...  (genes) from high parameters, evaluate time course expression profiles and select relevant features 108 providing a ranking score for such genes.  ... 
doi:10.1101/392761 fatcat:sn2u6rngkrcejj7kitaxg5laga

Iterative Bayesian Model Averaging: a method for the application of survival analysis to high-dimensional microarray data

Amalia Annest, Roger E Bumgarner, Adrian E Raftery, Ka Yee Yeung
2009 BMC Bioinformatics  
The main goal in applying survival analysis to microarray data is to determine a highly predictive model of patients' time to event (such as death, relapse, or metastasis) using a small number of selected  ...  Here, we extend the iterative BMA algorithm for application to survival analysis on high-dimensional microarray data.  ...  These genes are then used to build a statistical model for the continuous time to event data [32] .  ... 
doi:10.1186/1471-2105-10-72 pmid:19245714 pmcid:PMC2657791 fatcat:s6ykd6uxn5fv3bfqhd2fvpwu2u

Modeling considerations for using expression data from multiple species

Elizabeth Siewert, Katerina J. Kechris
2013 Statistics in Medicine  
Using a multivariate regression model, the phylogenetic relationships among the species were accounted for in two ways: 1) a repeated-measures model, where the error term is constrained, and 2) a Bayesian  ...  We suggest a possible explanation for the better performance of the model with the constrained error term.  ...  We thank Itay Tirosh of the Naama Barkai lab for the expression data, Randy Wu, formerly of the Hao Li lab, for the sequence alignments, and John Siewert for computer run-time assistance.  ... 
doi:10.1002/sim.5850 pmid:23703923 pmcid:PMC4964853 fatcat:gqxybrxv2jbfzjzsifgbhwf6au
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