TY - JOUR
T1 - Empirical bayes gene screening tool for time-course or dose-response microarray data
AU - Eckel, J. E.
AU - Gennings, C.
AU - Chinchilli, V. M.
AU - Burgoon, L. D.
AU - Zacharewski, T. R.
N1 - Funding Information:
This publication was made possible by grant number T32 ES07334-01A1 from the National Institute of Environmental Health Sciences (NIEHS), NIH, and by grant number R01 ES11271-01 from the NIEHS. The authors would like to acknowledge Ken Kwan’s contributions in the generation of the microarray data presented as well as helpful comments from the guest editor and two anonymous reviewers.
PY - 2004
Y1 - 2004
N2 - An efficient method to reduce the dimensionality of microarray gene expression data from thousands or tens of thousands of cDNA clones down to a subset of the most differentially expressed cDNA clones is essential in order to simplify the massive amount of data generated from microarray experiments. An extension to the methods of Efron et al. [Efron, B., Tibshirani, R., Storey, J., Tusher, V. (2001). Empirical Bayes analysis of a microarray experiment. J. Am. Statist. Assoc. 96:1151-1160] is applied to a differential time-course experiment to determine a subset of cDNAs that have the largest probability of being differentially expressed with respect to treatment conditions across a set of unequally spaced time points. The proposed extension, which is advocated to be a screening tool, allows for inference across a continuous variable in addition to incorporating a more complex experimental design and allowing for multiple design replications. With the current data the focus is on a time-course experiment; however, the proposed methods can easily be implemented on a dose-response experiment, or any other microarray experiment that contains a continuous variable of interest. The proposed empirical Bayes gene-screening tool is compared with the Efron et al. (2001) method in addition to an adjusted model-based t-value using a time-course data set where the toxicological effect of a specific mixture of chemicals is being studied.
AB - An efficient method to reduce the dimensionality of microarray gene expression data from thousands or tens of thousands of cDNA clones down to a subset of the most differentially expressed cDNA clones is essential in order to simplify the massive amount of data generated from microarray experiments. An extension to the methods of Efron et al. [Efron, B., Tibshirani, R., Storey, J., Tusher, V. (2001). Empirical Bayes analysis of a microarray experiment. J. Am. Statist. Assoc. 96:1151-1160] is applied to a differential time-course experiment to determine a subset of cDNAs that have the largest probability of being differentially expressed with respect to treatment conditions across a set of unequally spaced time points. The proposed extension, which is advocated to be a screening tool, allows for inference across a continuous variable in addition to incorporating a more complex experimental design and allowing for multiple design replications. With the current data the focus is on a time-course experiment; however, the proposed methods can easily be implemented on a dose-response experiment, or any other microarray experiment that contains a continuous variable of interest. The proposed empirical Bayes gene-screening tool is compared with the Efron et al. (2001) method in addition to an adjusted model-based t-value using a time-course data set where the toxicological effect of a specific mixture of chemicals is being studied.
KW - Mixture model
KW - Model-based t-value
KW - Modified loop design
KW - Normalization and gene models
KW - Positive false discovery rate
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U2 - 10.1081/BIP-200025656
DO - 10.1081/BIP-200025656
M3 - Article
C2 - 15468757
AN - SCOPUS:4644240907
SN - 1054-3406
VL - 14
SP - 647
EP - 670
JO - Journal of Biopharmaceutical Statistics
JF - Journal of Biopharmaceutical Statistics
IS - 3
ER -