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Abstract:
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In genomics applications, we are interested to measure the differential expression level of genes between case and control groups, which may be improved by shrinking toward a null-hypothesis value to the extent of a probability that the null hypothesis is true. In order to determine that level, we estimate the value of parameter of interest (POI) which is a fold change between those two groups. There are several estimators of the POI, among them unshrunken and shrunken estimators. The empirical Bayes theory used to estimate the shrunken estimators by computing the posterior mean of POI, which is based on the unshrunken estimator and an estimator of local false discovery rate(LFDR). The LFDR is a null-hypothesis posterior probability given a statistic test. Our aim is to study and compare the different estimators of POI by using the different estimators of LFDR like: HBE, BBE, MLE, CFDR, RFDR, etc...These POI estimators will be applied on breast cancer and colon cancer data. Our simulation study show that, two shrunken estimators of POI which are BBE and RFDR may be preferred since their mean square error (MSE) performs better than the others estimators.
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