Gene Selection for Sample Classifications in Microarray Experiments
نویسندگان
چکیده
منابع مشابه
Gene selection for sample classifications in microarray experiments.
DNA microarray technology provides useful tools for profiling global gene expression patterns in different cell/tissue samples. One major challenge is the large number of genes relative to the number of samples. The use of all genes can suppress or reduce the performance of a classification rule due to the noise of nondiscriminatory genes. Selection of an optimal subset from the original gene s...
متن کاملSample size for gene expression microarray experiments
MOTIVATION Microarray experiments often involve hundreds or thousands of genes. In a typical experiment, only a fraction of genes are expected to be differentially expressed; in addition, the measured intensities among different genes may be correlated. Depending on the experimental objectives, sample size calculations can be based on one of the three specified measures: sensitivity, true disco...
متن کاملSample Size Estimation for Microarray Experiments
RNAExpressionMicroarray technology is widely applied in biomedical and pharmaceutical research. The huge number of RNA concentrations estimated for each sample make it difficult to apply traditional sample size calculation techniques and has left most practitioners to rely on rule-of-thumb techniques. In this paper, we describe and demonstrate a simple method for performing and visualizing samp...
متن کاملSample Size Choice for Microarray Experiments
We review Bayesian sample size arguments for microarray experiments, focusing on a decision theoretic approach. We start by introducing a choice based on minimizing expected loss as theoretical ideal. Practical limitations of this approach quickly lead us to consider a compromise solution that combines this idealized solution with a sensitivity argument. The finally proposed approach relies on ...
متن کاملSample size for detecting differentially expressed genes in microarray experiments
Background: Microarray experiments are often performed with a small number of biological replicates, resulting in low statistical power for detecting differentially expressed genes and concomitant high false positive rates. While increasing sample size can increase statistical power and decrease error rates, with too many samples, valuable resources are not used efficiently. The issue of how ma...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: DNA and Cell Biology
سال: 2004
ISSN: 1044-5498,1044-5498
DOI: 10.1089/1044549042476947