An Effective Interwoven Loop Design Application for Two-Channel Microarray Experiments

Microarray technology is widely applied to address complex scientific questions. However, there remain fundamental issues of how to design experiments to ensure that the resulting data enables robust statistical analysis. Interwoven loop design has several advantages over other designs. However it suffers in the complexity of design. We have implemented an online web application which allows users to find optimal loop designs for two-color microarray experiments. Given a number of conditions (such as treatments or time points) and replicates, the application will find the best possible design of the experiment and output experimental parameters. It is freely available from http://mcbc.usm.edu/iloop.

[1]  Susanna-Assunta Sansone,et al.  Defining best practice for microarray analyses in nutrigenomic studies. , 2005, The British journal of nutrition.

[2]  G. Churchill,et al.  Experimental design for gene expression microarrays. , 2001, Biostatistics.

[3]  Edgar Brunner,et al.  Efficient two-sample designs for microarray experiments with biological replications , 2004, Silico Biol..

[4]  G. Churchill,et al.  Variation in gene expression within and among natural populations , 2002, Nature Genetics.

[5]  Mehdi Pirooznia,et al.  Toxicogenomic analysis provides new insights into molecular mechanisms of the sublethal toxicity of 2,4,6-trinitrotoluene in Eisenia fetida. , 2007, Environmental science & technology.

[6]  Jean Yee Hwa Yang,et al.  Analysis of CDNA Microarray Images , 2001, Briefings Bioinform..

[7]  Jonas S. Almeida,et al.  Optimal cDNA microarray design using expressed sequence tags for organisms with limited genomic information , 2004, BMC Bioinformatics.

[8]  T P Speed,et al.  Experimental design and low-level analysis of microarray data. , 2004, International review of neurobiology.

[9]  Jun Hua,et al.  Extending the loop design for two-channel microarray experiments. , 2006, Genetical research.

[10]  Arnold J. Stromberg,et al.  Statistical implications of pooling RNA samples for microarray experiments , 2003, BMC Bioinform..

[11]  G F V Glonek,et al.  Factorial and time course designs for cDNA microarray experiments. , 2004, Biostatistics.

[12]  R. A. Bailey Designs for two‐colour microarray experiments , 2007 .

[13]  Ernst Wit,et al.  Statistics for Microarrays : Design, Analysis and Inference , 2004 .

[14]  Steven G. Gilmour,et al.  Design of Microarray Experiments for Genetical Genomics Studies , 2006, Genetics.

[15]  Gary A. Churchill,et al.  Analysis of Variance for Gene Expression Microarray Data , 2000, J. Comput. Biol..

[16]  R. Khanin,et al.  Simulated annealing for near-optimal dual-channel microarray designs , 2004 .

[17]  R. Tempelman Assessing statistical precision, power, and robustness of alternative experimental designs for two color microarray platforms based on mixed effects models. , 2005, Veterinary immunology and immunopathology.

[18]  Guilherme J M Rosa,et al.  Review of microarray experimental design strategies for genetical genomics studies. , 2006, Physiological genomics.

[19]  Xiaohui Liu,et al.  An experimental evaluation of a loop versus a reference design for two-channel microarrays , 2005, Bioinform..

[20]  G. Churchill Fundamentals of experimental design for cDNA microarrays , 2002, Nature Genetics.