Comparison of Commonly Used Methods for Testing Interaction Effect in Time-Course Microarray Experiments

2017 ◽  
Vol 9 (1) ◽  
pp. 35-44 ◽  
Author(s):  
Nhu Quynh TRAN ◽  
Mehmet KOÇAK ◽  
Mehmet MENDEŞ
Keyword(s):  
Interaction Effect ◽  
Time Course ◽  
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

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

RANK-BASED CLUSTERING ANALYSIS FOR THE TIME-COURSE MICROARRAY DATA

2009 ◽  
Vol 07 (01) ◽  
pp. 75-91 ◽  
Author(s):  
SUNG-GON YI ◽  
YOON-JEONG JOO ◽  
TAESUNG PARK
Keyword(s):  
Microarray Data ◽  
Clustering Analysis ◽  
Time Course ◽  
Testing Procedure ◽  
Second Step ◽  
Expression Data ◽  
Clustering Methods ◽  
Microarray Experiments ◽  
Cancer Data ◽  
Temporal Profiles

Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. In time-course microarray experiments in which gene expression is monitored over time, we are interested in clustering genes that show similar temporal profiles and identifying genes that show a pre-specified candidate profile. Unfortunately, many traditional clustering methods used for analyzing microarray data do not effectively detect temporal profiles for the time-course microarray data. We propose a rank-based clustering analysis for the time-course microarray data. Our clustering method consists of two steps: the first step discretizes the expression data into groups and then transform them into the rank data, the second step performs the rank-based clustering analysis. Our testing procedure uses the bootstrap samples to select the genes that show similar patterns for the candidate profiles. Simulation study is performed to evaluate the performance of the proposed rank-based method. The results are illustrated with the breast cancer data and the Arabidopsis cold stress data.

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