Bayesian Models for the Multi-sample Time-Course Microarray Experiments

2012 ◽  
pp. 21-35
Author(s):  
Claudia Angelini ◽  
Daniela De Canditiis ◽  
Marianna Pensky ◽  
Naomi Brownstein
Keyword(s):  
Time Course ◽  
Bayesian Models ◽  
Microarray Experiments ◽  
Sample Time

Bayesian models for two-sample time-course microarray experiments

2009 ◽  
Vol 53 (5) ◽  
pp. 1547-1565 ◽  
Author(s):  
Claudia Angelini ◽  
Daniela De Canditiis ◽  
Marianna Pensky
Keyword(s):  
Time Course ◽  
Bayesian Models ◽  
Microarray Experiments ◽  
Sample Time

scholarly journals A Bayesian Approach to Estimation and Testing in Time-course Microarray Experiments

2007 ◽  
Vol 6 (1) ◽  
Author(s):  
Claudia Angelini ◽  
Daniela De Canditiis ◽  
Margherita Mutarelli ◽  
Marianna Pensky
Keyword(s):  
Time Course ◽  
Expression Profiles ◽  
Cancer Cell Line ◽  
Real Data ◽  
Microarray Experiment ◽  
Computational Effort ◽  
Microarray Experiments ◽  
Analytic Expressions ◽  
Sampling Intervals ◽  
Nonparametric Techniques

The objective of the present paper is to develop a truly functional Bayesian method specifically designed for time series microarray data. The method allows one to identify differentially expressed genes in a time-course microarray experiment, to rank them and to estimate their expression profiles. Each gene expression profile is modeled as an expansion over some orthonormal basis, where the coefficients and the number of basis functions are estimated from the data. The proposed procedure deals successfully with various technical difficulties that arise in typical microarray experiments such as a small number of observations, non-uniform sampling intervals and missing or replicated data. The procedure allows one to account for various types of errors and offers a good compromise between nonparametric techniques and techniques based on normality assumptions. In addition, all evaluations are performed using analytic expressions, so the entire procedure requires very small computational effort. The procedure is studied using both simulated and real data, and is compared with competitive recent approaches. Finally, the procedure is applied to a case study of a human breast cancer cell line stimulated with estrogen. We succeeded in finding new significant genes that were not marked in an earlier work on the same dataset.

scholarly journals Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference

2003 ◽  
Vol 19 (7) ◽  
pp. 834-841 ◽  
Author(s):  
S. D. Peddada ◽  
E. K. Lobenhofer ◽  
L. Li ◽  
C. A. Afshari ◽  
C. R. Weinberg ◽  
...  
Keyword(s):  
Dose Response ◽  
Time Course ◽  
Gene Selection ◽  
Order Restricted Inference ◽  
Microarray Experiments ◽  
Order Restricted

scholarly journals Factorial and time course designs for cDNA microarray experiments

Biostatistics ◽  
2004 ◽  
Vol 5 (1) ◽  
pp. 89-111 ◽  
Author(s):  
G. F. V. Glonek ◽  
P. J. Solomon
Keyword(s):  
Cdna Microarray ◽  
Time Course ◽  
Microarray Experiments

scholarly journals Discovering gene expression patterns in time course microarray experiments by ANOVA–SCA

2007 ◽  
Vol 23 (14) ◽  
pp. 1792-1800 ◽  
Author(s):  
María José Nueda ◽  
Ana Conesa ◽  
Johan A. Westerhuis ◽  
Huub C. J. Hoefsloot ◽  
Age K. Smilde ◽  
...  
Keyword(s):  
Gene Expression ◽  
Time Course ◽  
Expression Patterns ◽  
Gene Expression Patterns ◽  
Microarray Experiments

scholarly journals Computational Challenges in miRNA Target Predictions: To Be or Not to Be a True Target?

2009 ◽  
Vol 2009 ◽  
pp. 1-9 ◽  
Author(s):  
Christian Barbato ◽  
Ivan Arisi ◽  
Marcos E. Frizzo ◽  
Rossella Brandi ◽  
Letizia Da Sacco ◽  
...  
Keyword(s):  
Time Course ◽  
Mirna Target ◽  
Target Genes ◽  
Throughput Capacity ◽  
Microarray Experiments ◽  
Mirna Targets ◽  
Mrna Targets ◽  
True Target ◽  
Candidate Target ◽  
Mrna Microarray

All microRNA (miRNA) target—finder algorithms return lists of candidate target genes. How valid is that output in a biological setting? Transcriptome analysis has proven to be a useful approach to determine mRNA targets. Time course mRNA microarray experiments may reliably identify downregulated genes in response to overexpression of specific miRNA. The approach may miss some miRNA targets that are principally downregulated at the protein level. However, the high-throughput capacity of the assay makes it an effective tool to rapidly identify a large number of promising miRNA targets. Finally, loss and gain of function miRNA genetics have the clear potential of being critical in evaluating the biological relevance of thousands of target genes predicted by bioinformatic studies and to test the degree to which miRNA-mediated regulation of any “validated” target functionally matters to the animal or plant.

scholarly journals Robust test method for time-course microarray experiments

2010 ◽  
Vol 11 (1) ◽  
Author(s):  
Insuk Sohn ◽  
Kouros Owzar ◽  
Stephen L George ◽  
Sujong Kim ◽  
Sin-Ho Jung
Keyword(s):  
Time Course ◽  
Test Method ◽  
Robust Test ◽  
Microarray Experiments

scholarly journals Statistical tests for identifying differentially expressed genes in time-course microarray experiments

2003 ◽  
Vol 19 (6) ◽  
pp. 694-703 ◽  
Author(s):  
T. Park ◽  
S.-G. Yi ◽  
S. Lee ◽  
S. Y. Lee ◽  
D.-H. Yoo ◽  
...  
Keyword(s):  
Differentially Expressed Genes ◽  
Time Course ◽  
Statistical Tests ◽  
Differentially Expressed ◽  
Microarray Experiments
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