| Summary: | 碩士 === 淡江大學 === 數學學系碩士班 === 98 === In recent years, the microarray experiment has become the most popular
biotechnology to study the gene expression. The expression levels of thousands
of genes are simultaneously measured to investigate the association of
certain treatments, diseases, and genes. In order to remove the impact of nonbiological variations and systematic bias presents in such high-throughput
data, the pre-processing is an essential and important step in microarray data
analysis. Among these, RMA (robust multiarray averaging) and GCRMA are
the most widely used pre-processing methods for Affymetrix GeneChip data.
Both methods use the quantile normalization for the normalization step. In
this study, we proposed a weighted quantile normalization using the principal
component analysis. The standard HGU133 dataset from Affymetrix and
the other 14 datasets from GEO website were employed to compare RMA,
GCRMA and their weighted versions. The evaluation was reported based
on the indices of Affycomp II and the significance analysis of microarrays
(SAM). The finding suggests the differential expressed genes found by the
weighted quantile normalization were slightly different with those obtained
from the classical method if the input data possesses large variations.
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