Gene selection for oligonucleotide array: an approach using PM probe level data.
BACKGROUND
Analysis of oligonucleotide array data, especially to select genes of interest, is a highly challenging task because of the large volume of information and various experimental factors. Moreover, interaction effect (i.e. expression changes depend on probe effects) complicates the analysis because current methods often use an additive model to analyze data. We propose an approach to address these issues with the aim of producing a more reliable selection of differentially expressed genes. The approach uses the rank for normalization, employs the percentile-range to measure expression variation, and applies various filters to monitor expression changes.


RESULTS
We compare our approach with MAS and Dchip models. A data set from an angiogenesis study is used for illustration. Results show that our approach performs better than other methods either in identification of the positive control gene or in PCR confirmatory tests. In addition, the invariant set of genes in our approach provides an efficient way for normalization.
Chen
Dung-Tsa
DT
Biostatistics and Bioinformatics Unit, Comprehensive Cancer Center, University of Alabama at Birmingham, 153 Wallace Tumor Institute, 1824 6th Avenue South, Birmingham, AL 35294, USA. dtchen@uab.edu
Lin
Sue-Hwa
SH
Soong
Seng-Jaw
SJ
D000465:Algorithms; D015342:DNA Probes / Q000737:chemistry* / Q000235:genetics*; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods*; D011388:Prolactin / Q000737:chemistry* / Q000235:genetics*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D016415:Sequence Alignment / Q000379:methods*; D017422:Sequence Analysis, DNA / Q000379:methods*
D015342:DNA Probes; D011388:Prolactin