Statistical methods for ranking differentially expressed genes.
In the analysis of microarray data the identification of differential expression is paramount. Here I outline a method for finding an optimal test statistic with which to rank genes with respect to differential expression. Tests of the method show that it allows generation of top gene lists that give few false positives and few false negatives. Estimation of the false-negative as well as the false-positive rate lies at the heart of the method.
Broberg
Per
P
Molecular Sciences, AstraZeneca Research and Development Lund, S-221 87 Lund, Sweden. per.broberg@astrazeneca.com
D019295:Computational Biology / Q000379:methods / Q000706:statistics & numerical data; D003198:Computer Simulation / Q000706:statistics & numerical data; D018076:DNA, Complementary / Q000096:biosynthesis; D005188:False Negative Reactions; D005189:False Positive Reactions; D020869:Gene Expression Profiling / Q000379:methods* / Q000706:statistics & numerical data*; D015972:Gene Expression Regulation, Neoplastic / Q000235:genetics; D006801:Humans; D007938:Leukemia / Q000145:classification / Q000235:genetics; D015233:Models, Statistical
D018076:DNA, Complementary