Experimental design for gene expression microarrays.

By M. Kerr, Bar Harbor, Gary A. Churchill. Biostatistics, 2001

744 citations recorded by the source corpus.

Subfields: Management Science and Operations Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1d2e55d466f8d8d7fcd8f8225b67502da6031cd6

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In Management Science and Operations Research

In Molecular Biology

Citations in the corpus

Design issues for cDNA microarray experiments (2002, background): “In most cases, neither of these extremes will apply, but careful attention to experimental design will ensure that good use is made of the available resources, obvious biases will be avoided and it will be possible to answer the primary questions of interes”
Assessing Gene Significance from cDNA Microarray Expression Data via Mixed Models (2001, positive): “Replications indicates the number of times half of the 12 arrays are repeated experimentally. replicated samples are paired with each other in a circular fashion as described by Kerr and Churchill (2001b).”
STATISTICAL METHODS FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN REPLICATED cDNA MICROARRAY EXPERIMENTS (2002, background): “Clearly more research is needed on the design of microarray experiments; preliminary work on this subject can be found in (Kerr and Churchill (2001) and Lin et al.”
Linear Models and Empirical Bayes Methods for Assessing Differential Expression in Microarray Experiments (2004, neutral): “Figure 1 displays some examples of simple designs with two-color arrays using arrow notation as in Kerr and Churchill (2001). Each arrow represents a microarray.”
A random variance model for detection of differential gene expression in small microarray experiments (2003, methodology): “Kerr and Churchill (2001) suggested the application of ANOVA to the red and green log intensity values as an alternative to the use of log ratios.”
The balance of reproducibility, sensitivity, and specificity of lists of differentially expressed genes in microarray studies (2008, methodology): “They include the popular permutation-based "SAM" procedure [5,12,49,50], Bayesian-based approaches [43,51] and others [52].”
Bayesian infinite mixture model based clustering of gene expression profiles (2002, background): “Choosing a statistically optimal experimental design and the appropriate statistical analysis of produced data is increasingly important aspect of such experiments (Kerr and Churchill, 2001; Baldi and Long, 2001).”
Cluster analysis for gene expression data: a survey (2004, background): “nearest neighbors (KNNimpute), and row average. Kerr et al. [ Kerr 2001 ] stressed the”