title: Ranking analysis for identifying differentially expressed genes.
abstract: Microarrays allow researchers to examine the expression of thousands of genes simultaneously. However, identification of genes differentially expressed in microarray experiments is challenging. With an optimal test statistic, we rank genes and estimate a threshold above which genes are considered to be differentially expressed genes (DE). This overcomes the embarrassing shortcoming of many statistical methods to determine the cut-off values in ranking analysis. Experiments demonstrate that our method is a good performance and avoids the problems with graphical examination and multiple hypotheses testing that affect alternative approaches. Comparing to those well known methods, our method is more sensitive to data sets with small differentially expressed values and not biased in favor of data sets based on certain distribution models.
authors.affiliation: School of Computer Science and Technology, Nanjing University of Science and Technology, Nanjing 210094, China. qys@ujs.edu.cn
authors.forename: Yunsong
authors.initials: Y
authors.lastname: Qi
authors.forename: Huaijiang
authors.initials: H
authors.lastname: Sun
authors.forename: Quansen
authors.initials: Q
authors.lastname: Sun
authors.forename: Lei
authors.initials: L
authors.lastname: Pan
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods* / Q000706:statistics & numerical data; D003627:Data Interpretation, Statistical; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D015233:Models, Statistical; D009363:Neoplasm Proteins / Q000235:genetics* / Q000378:metabolism; D009369:Neoplasms / Q000235:genetics / Q000378:metabolism*; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D012984:Software
chemical_list: D009363:Neoplasm Proteins