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Spot Identification and Quality Control in Cell-Based Microarrays

Michael Bauer, Keekyoung Kim, Yiling Qiu, Blaise Calpe, Ali Khademhosseini, Ronglih Liao, Ian Wheeldon
2012 ACS Combinatorial Science  
While high content image analysis, cell counting, and cell pattern recognition methods are established, there is a need for new post-processing and quality control methods for cell-based microarrays used  ...  Cell-based microarrays are being increasingly used as a tool for combinatorial and high throughput screening of cellular microenvironments.  ...  The authors thank the mechanical Turks for helping on manually counting and scoring microarray images.  ... 
doi:10.1021/co300039w pmid:22850537 pmcid:PMC3495599 fatcat:uiqc67jppzhchnuc2n76zn5bwq

An adaptive empirical Bayesian thresholding procedure for analysing microarray experiments with replication

Rebecca E. Walls, Stuart Barber, John T. Kent, Mark S. Gilthorpe
2007 Journal of the Royal Statistical Society, Series C: Applied Statistics  
In this paper, we model the data using as a two-component mixture model and develop an empirical Bayesian thresholding procedure, originally introduced for thresholding wavelet coefficients, as an alternative  ...  A typical microarray experiment attempts to ascertain which genes display differential expression in different samples.  ...  Acknowledgements We would like to thank the referees for their comments, Richard Bean for the help regarding his computer package and the EPSRC for the funding for this work.  ... 
doi:10.1111/j.1467-9876.2007.00577.x fatcat:g2pw3mfgbrh2ratrf4wz5ip4ra

A Bayesian Hierarchical Model for Signal Extraction from Protein Microarrays [article]

Sophie Berube, Tamaki Kobayashi, Douglas E. Norris, Ingo Ruczinski, William J. Moss, Amy Wesolowski, Thomas A. Louis
2022 bioRxiv   pre-print
We develop and evaluate a Bayesian model to extract a full posterior distribution of normalized fluorescent signals and associated ranks for protein microarrays, and show that it fits well to data from  ...  Such ranking methods require Bayesian modeling that produces full posterior distributions for parameters of interest.  ...  The authors also acknowledge Jianbo Pan and Heng Zhu for sharing the protein microarray data from their lung cancer study.  ... 
doi:10.1101/2022.02.16.480698 fatcat:35u56jesmfhnrfyg5k65tyokiq

Are data from different gene expression microarray platforms comparable?

Anna-Kaarina Järvinen, Sampsa Hautaniemi, Henrik Edgren, Petri Auvinen, Janna Saarela, Olli-P. Kallioniemi, Outi Monni
2004 Genomics  
Variability of the data represents a challenge for developing future diagnostic applications of microarrays. D 2004 Elsevier Inc. All rights reserved.  ...  cDNA microarrays (Agilent Human 1 cDNA), and custom-made cDNA microarrays from a sequence-validated 13K cDNA library.  ...  Acknowledgments We thank Tuula Airaksinen for excellent technical assistance.  ... 
doi:10.1016/j.ygeno.2004.01.004 pmid:15177569 fatcat:r7oe6vbm4zcgdhlgvmb6mz5gii

Calculation of Spot Reliability Evaluation Scores (SRED) for DNA Microarray Data

K. Shimokawa, R. Kodzius, Y. Matsumura, Y. Hayashizaki
2008 Cold Spring Harbor Protocols  
ACKNOWLEDGMENTS We would like to thank Albin Sandelin and, for help with editing, Ann Karlsson.  ...  This work was supported (in part) by a grant from the Genome Network Project from the Ministry of Education, Culture, Sports, Science and Technology, Japan.  ...  Spot reliability evaluation score for DNA microarrays (SRED) offers a reliability value for each spot in the microarray.  ... 
doi:10.1101/pdb.prot4937 pmid:21356768 fatcat:7qp73tfuhjbznpbcbjfjkahb6m

A Bayesian Model for Pooling Gene Expression Studies That Incorporates Co-Regulation Information

Erin M. Conlon, Bradley L. Postier, Barbara A. Methé, Kelly P. Nevin, Derek R. Lovley, Ying Xu
2012 PLoS ONE  
Here, we introduce a new Bayesian model for pooling gene expression studies that incorporates operon information into the model.  ...  Current Bayesian microarray models that pool multiple studies assume gene expression is independent of other genes.  ...  Acknowledgments We thank the editors and two anonymous reviewers for helpful and insightful comments which improved the manuscript. Author Contributions  ... 
doi:10.1371/journal.pone.0052137 pmid:23284902 pmcid:PMC3532429 fatcat:7jg7bowjijaetikt7pe5pazp5y

Bayesian models for pooling microarray studies with multiple sources of replications

Erin M Conlon, Joon J Song, Jun S Liu
2006 BMC Bioinformatics  
Our method provides a cohesive framework for combining multiple but not identical microarray studies with several sources of replication, with data produced from the same platform.  ...  We introduce a Bayesian hierarchical model to pool cDNA microarray data across multiple independent studies to identify highly expressed genes.  ...  Acknowledgements We thank George Tseng, Jeffrey Townsend and John Staudenmayer for helpful discussion, and Patrick Eichenberger and the laboratory of Richard Losick for the B. subtilis microarray data  ... 
doi:10.1186/1471-2105-7-247 pmid:16677390 pmcid:PMC1534062 fatcat:urdmwb2usje43jk7d4p52ld7ma

A supervised data-driven approach for microarray spot quality classification

Manuele Bicego, Maria Del Rosario Martinez, Vittorio Murino
2005 Pattern Analysis and Applications  
In this paper, the problem of classifying the quality of microarray data spots is addressed, using concepts derived from the supervised learning theory.  ...  The proposed method, after extracting spots from the microarray image, computes several features, which take into account shape, color and variability.  ...  Sampsa Hautaniemi of Tampere University of Technology (Finland), for kindly supplied the microarray data and the features used for testing. The authors would like to thank also Dr. S. Barbi and Prof.  ... 
doi:10.1007/s10044-005-0254-5 fatcat:jnlk256objchrfay7ca7hsfuca

Bayesian meta-analysis models for microarray data: a comparative study

Erin M Conlon, Joon J Song, Anna Liu
2007 BMC Bioinformatics  
Here, we compare two Bayesian meta-analysis models that are analogous to these methods. Results : Two Bayesian meta-analysis models for microarray data have recently been introduced.  ...  With the growing abundance of microarray data, statistical methods are increasingly needed to integrate results across studies.  ...  Acknowledgements We thank Joseph Horowitz for helpful discussion, and Patrick Eichenberger and the laboratory of Richard Losick for the B. subtilis microarray data and helpful advice.  ... 
doi:10.1186/1471-2105-8-80 pmid:17343745 pmcid:PMC1851021 fatcat:pfb2q5tcmbgu3a4tpwkngaxiuq

Bayesian Normalization and Identification for Differential Gene Expression Data

Dabao Zhang, Martin T. Wells, Christine D. Smart, William E. Fry
2005 Journal of Computational Biology  
The simulation study and an application to real microarray data demonstrate promising results.  ...  A Bayesian framework is proposed for the analysis of the proposed measurement-error model to avoid the potential risk of using the common two-step procedure.  ...  bad quality spots) in a spotted microarray study of potato late blight (see Fig. 1 for the M-A plot) .  ... 
doi:10.1089/cmb.2005.12.391 pmid:15882138 fatcat:natlncdvb5byplcv2w4uiupy3q

Bayesian Hierarchical Model for Identifying Changes in Gene Expression from Microarray Experiments

Philippe Broët, Sylvia Richardson, François Radvanyi
2002 Journal of Computational Biology  
The aim of this paper is to propose a methodological framework for studies that investigate differential gene expression through microarrays technology that is based on a fully Bayesian mixture approach  ...  This new technology is often used to assess changes in mRNA expression upon a speci ed transfection for a cell line in order to identify target genes.  ...  ACKNOWLEDGMENTS The authors wish to thank Peter Green (Bristol University) for useful discussion on the method and the implementation.  ... 
doi:10.1089/106652702760277381 pmid:12323100 fatcat:hrpheal2lvc5dgnunpx7iqxx5a

Fundamentals of cDNA microarray data analysis

Yuk Fai Leung, Duccio Cavalieri
2003 Trends in Genetics  
Acknowledgements YFL is supported by a Croucher Foundation Postdoctoral Fellowship. We thank Alice Yu Ming Lee and Abel Chiu Shun Chun for their critical comments on this manuscript.  ...  Statistical methods such as Student's t-test and its variants [17, 18] , ANOVA [19, 20] , Bayesian method [17, 20, 21] , or Mann -Whitney test [22] , can be used to rank the genes from replicated data  ...  This review is focused on the data analysis of the spotted cDNA microarrays, the most accessible microarray platform for general biologists.  ... 
doi:10.1016/j.tig.2003.09.015 pmid:14585617 fatcat:coiymseew5cxbpaykzu2632jyi

Using a calibration experiment to assess gene-specific information: full Bayesian and empirical Bayesian models for two-channel microarray data

M. Blangiardo, S. Toti, B. Giusti, R. Abbate, A. Magi, F. Poggi, L. Rossi, F. Torricelli, A. Biggeri
2005 Bioinformatics  
A difficulty when analysing expression measures is how to model variability for the whole set of genes. It is usually unrealistic to assume a common variance for each gene.  ...  Motivation: Microarray studies permit to quantify expression levels on a global scale by measuring transcript abundance of thousands of genes simultaneously.  ...  METHODS In this section we present the two methods we used to analyse the data.  ... 
doi:10.1093/bioinformatics/bti750 pmid:16267084 fatcat:mk4bsk22gzdedhhskv4e3vb4tm


John R. Stevens, R. W. Doerge
2005 Conference on Applied Statistics in Agriculture  
By adjusting for known differences between laboratories through the use of covariates and employing a Bayesian framework to effectively account for between-laboratory variability, the results of multiple  ...  A simulation model based on the Barley Affymetrix GeneChip microarray demonstrates the utility of this approach. Further illustration is provided from a mouse model for multiple sclerosis.  ...  Ibrahim (University of Rostock), as well as their colleagues, for providing access to their raw Affymetrix data.  ... 
doi:10.4148/2475-7772.1141 fatcat:7u2ietgjozhujggxd7iikkdhrq
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