microarray experiment

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In many microarray experiments, gene expressions are examined in multiple (more than two) conditions.
Extending the loop design for two-channel microarray experiments use shrinkage estimation to improve the estimation of all the variance components.
Chapter 3 deals with the design of microarray experiments, including a number of designs for two-colour arrays and statistical power calculations.
The objective of two-channel microarray experiments is comparative analysis.
The same computations can be used to determine the relative efficiency of different experimental designs and to determine optimal designs for microarray experiments.
Bootstrapping cluster analysis : assessing the reliability of conclusions from microarray experiments.
Of course, microarray experiments are considerably more complicated than the simple agricultural experiment just described.
This may be appropriate because many microarray experiments are exploratory.
There is another class of microarray experiments in which different conditions refer to different subjects (or individuals) selected based on their phenotypes.
Extending the loop design for two-channel microarray experiments.
Unlike the latter, there is, as yet, no general consensus on optimum statistical strategies for microarray experiments but this book puts in context many of the options.
The contributors' experience seems to be biased a little towards medical applications where microarray experiments are often much simpler than would typically be found in a plant or animal institute.
Use of within-array replicate spots for assessing differential expression in microarray experiments.
Gene selection and clustering for time-course and dose-response microarray experiments using order-restricted inference.
One usage is in bioinformatics to locate genes that are differential expressed on microarray experiments.
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