Due to the large volume of data obtained from GeoChip, data analysis can be very challenging. The data has a multivariate structure and the number of variables is much larger than the number of observations (p n). To assist users with data analysis steps, a pipeline is available which performs many of the common analyses 3 . Some common descriptive statistics used include relative abundance of gene categories or subcategories, richness and diversity (α and β) indices, and percentages of gene overlap between samples. To compare the overall community structure, unconstrained ordination [principal component analysis (PCA) and correspondence analysis (CA)] to reduce the dimensionality of variables in order to maximize the visible variability of the data or hierarchical cluster analysis (HCA), which groups communities based on the similarity of their gene profiles, can be used. To compare communities, response ratios, which compare the signal intensity of genes between conditions (Luo et al., 2006; Liang et al., 2009a) , t-tests, ANOVA, and dissimilarity tests can be used. Several methods can be used to examine the relationship between communities and environmental parameters. These include constrained ordination, such as canonical correspondence analysis (CCA; ter Braak, 1986), distance-based redundancy analysis (db-RDA; Legendre and Anderson, 1999), variation partitioning analysis (VPA; Økland and Eilertsen, 1994; Ramette and Tiedje, 2007) , and Mantel test. A relatively new analysis method is the random matrix theorybased (Mehta, 1990) neural network analysis (NNA) used to examine gene relationships within microbial ecological networks .