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 Answer to the comments of K. Dobbin, J. Shih and R. Simon on the paper 'Evaluation of the gene-specific dye-bias in cDNA microarray experiments'

2005 ◽  
Vol 21 (14) ◽  
pp. 3065-3065 ◽  
Author(s):  
M.-L. Martin-Magniette ◽  
J. Aubert ◽  
E. Cabannes ◽  
J.-J. Daudin
Keyword(s):  
Cdna Microarray ◽  
Microarray Experiments

scholarly journals An a posteriori strategy for enhancing gene discovery in anonymous cDNA microarray experiments

2004 ◽  
Vol 20 (11) ◽  
pp. 1721-1727 ◽  
Author(s):  
R. S. Anderssen ◽  
Y. Wu ◽  
R. Dolferus ◽  
I. Saunders
Keyword(s):  
Cdna Microarray ◽  
Gene Discovery ◽  
A Posteriori ◽  
Microarray Experiments

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

Design issues for cDNA microarray experiments

2002 ◽  
Vol 3 (8) ◽  
pp. 579-588 ◽  
Author(s):  
Yee Hwa Yang ◽  
Terry Speed
Keyword(s):  
Cdna Microarray ◽  
Design Issues ◽  
Microarray Experiments
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