Reproducibility of Microarray and Gene Expression Analysis: Reference

Key Points

Introduction to Gene Expression	Microarray heat maps can be used to compare and identify gene expression patterns.
Introduction to Hierarchical Clustering	Hierarchical clustering groups observations on a continuous scale and allows a dissimilarity threshold to determine the number of clusters.
Recreating Supporting Figure 6 with TreeView	TreeView 3.0 can process the data files distributed with the paper to fully recreate Supporting Figure 6.
Recreating Supporting Figure 6 with Hive Plots	Hive plots are intended to represent large network data. The expression profiles for a large number of genes can be visualized using a hive plot.
Recreating the Analysis with Cluster and TreeView	The reclustering of the original data with Cluster 3.0 does not support the recreation of Supporting Figure 6.
Recreating the Analysis with R	We can use R to write an uncentered correlation function, but the similarity calculations are not identical to those in the original paper. We can use R to create dendrograms that show the clustering of arrays and genes, but the clusters are not identical to those in the original paper. Our replicate heat map is not identical to the original heat map because of differences in clustering and color coding.

Citations

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Bryan A. Hanson (2016) HiveR: 2D and 3D Hive Plots for R. R package version 0.2.55, academic.depauw.edu/~hanson/HiveR/HiveR.html

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