Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovaria

Background: A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy. These markers are useful in diagnosis, may identify potential targets for drug development, and can aid in evaluating treatment efficacy and predicting patient outcome. Microarray technology has enabled marker discovery from human cells by permitting measurement of steady-state mRNA levels derived from thousands of genes. However many challenging and unresolved issues regarding the acquisition and analysis of microarray data remain, such as accounting for both experimental and biological noise, transcripts whose expression profiles are not normally distributed, guidelines for statistical assessment of false positive/negative rates and comparing data derived from different research groups. This study addresses these issues using Affymetrix HG-U95A and HG-U133 GeneChip data derived from different research groups. Results: We present here a simple non parametr...

• Type: Journal
• Year: 2004
• Where: BMCBI
• Authors: Virginie M. Aris, Michael J. Cody, Jeff Cheng, James J. Dermody, Patricia Soteropoulos, Michael Recce, Peter P. Tolias
• Keywords: BMCBI 2004, Expression Profiles, Non Parametric Approach, Steady-state MRNA Levels