Mar 16, 2011

Ranking analysis for identifying differentially expressed genes

Genomics
Yunsong QiLei Pan

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.

Mentioned in this Paper

Prostatic Neoplasms
Prostate Carcinoma
Gene Expression
Computer Programs and Programming
Data Interpretation, Statistical
Genes, vif
Commit Lozenge
Two-Parameter Models
Gene Clusters
Cdna Microarrays

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