Gene Expression Arrays
Overview
Analysis of variance for expression data
Experimental design for microarrays
Bootstrapping cluster Analysis
Design and analysis of microarrays
Statistical analysis of a gene expression microarray experiment with replication
Analysis of a designed microarray experiment
Bootstrapping Cluster Analysis:
Assessing the Reliability of Conclusions from Microarray Experiments
M. Kathleen Kerr and Gary Churchill
Abstract: We introduce a general technique for making statistical inference from gene expression microarray data. The approach utilizes an analysis of variance model to achieve normalization and estimate differential expression of genes across multiple conditions. Statistical inference is based on two applications of a randomization technique, bootstrapping. Bootstrapping is used to obtain confidence intervals for differential expression estimates from individual genes, and then to assess the stability of results from a cluster analysis. We illustrate the technique with a publicly available data set and draw conclusions about reliability of clustering results in light of variation in the data. The bootstrapping procedure relies on experimental replication. We discuss the implications of replication and good design in microarray experiments. Manuscript