Comparison of gene expression microarray data with count-based RNA measurements informs microarray interpretation

cDNA Microarray Data Normalization

Comparison of feature extraction methods with microarray gene-expression data

With microarray gene-expression data, we compare supervised feature extraction methods with the unsupervised feature extraction methods. From experimental results, it is shown that the supervised feature extraction methods are more powerful than the unsupervised feature extraction methods in terms of class separability.

Data Analysis: Microarray Gene Expression

Most genomic data within the NextBio platform are generated using the Affymetrix platform (Figure 2). Ideally, all Affymetrix data would be imported as CEL files, and processed using the same normalization method, such as Robust Multi-array Average (RMA)1; however, for pre-existing experiments, this is often impossible. In this case, probeset-level Microarray Suite version 5 (MAS5) intensities2...

Mining Microarray Gene Expression Data

DNA microarray technology provides biologists with the ability to measure the expression levels of thousands of genes in a single experiment. Many initial experiments suggest that genes of similar function yield similar expression patterns in microarray hybridization experiments. Hence it makes possible to distinguish genes with different functions by analyzing gene data generated from microarr...

Data Analysis: Microarray Gene Expression

Most genomic data within the NextBio platform are generated using the Affymetrix platform (Figure 2). Ideally, all Affymetrix data would be imported as CEL files, and processed using the same normalization method, such as Robust Multi-array Average (RMA)1; however, for pre-existing experiments, this is often impossible. In this case, probeset-level Microarray Suite version 5 (MAS5) intensities2...