Adaptive Gene Picking with Microarray Data: Detecting Important Low Abundance Signals

摘要: DNA microarrays to evaluate gene expression present tremendous opportunities for understanding complex biological processes. However, important genes, such as transcription factors and receptors, are expressed at low levels, potentially leading negative values after adjusting background. These low-abundance transcripts have previously been ignored or handled in an ad hoc way. We describe a method that analyzes genes with using normal scores robustly adapts changing variability across average levels. This approach can be the basis clustering other exploratory methods. Our algorithm also assigns p-values sensitive changes expression. Together, these two features expand repertoire of analyzed arrays.