Microarray segmentation methods significantly influence data precision

Microarray segmentation methods significantly influence data precision.

Little consideration has been given to the effect of different segmentation methods on the variability of data derived from microarray images. Previous work has suggested that the significant source of variability from microarray image analysis is from estimation of local background. In this study, we used Analysis of Variance (ANOVA) models to investigate the effect of methods of segmentation ...

cDNA Microarray Data Normalization

Statistical methods for microarray data

Microarray analysis is particularly interesting from a statistical point of view since already a wide range of methods has been applied in the interpretation of the data and there is doubtless more to come. The purpose of this introduction is to collect analysis methods proposed in several papers, provide the details that are often missing, use a uniform notation, indicate in which direction re...

Data Mining Methods for Microarray Data Analysis

The advent of gene expression microarray technology enables the simultaneous measurement of expression levels for thousands or tens of thousands of genes in a single experiment (Schena, et al., 1995). Analysis of gene expression microarray data presents unprecedented opportunities and challenges for data mining in areas such as gene clustering (Eisen, et al., 1998; Tamayo, et al., 1999), sample...

Statistical methods for analyzing tissue microarray data.

Tissue microarrays (TMAs) are a new high-throughput tool for the study of protein expression patterns in tissues and are increasingly used to evaluate the diagnostic and prognostic importance of biomarkers. TMA data are rather challenging to analyze. Covariates are highly skewed, non-normal, and may be highly correlated. We present statistical methods for relating TMA data to censored time-to-e...