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japonicum bacteroids in soybean root nodules)2 and 12 samples in ""type"":""entrez-geo"",""attrs"":""text"":""GSE8580"",""term_id"":""8580""GSE8580 (response of B. japonicum wild type and mutant strains to genistein)3. We combined these two GSEs to create a partial compendium of 83 samples as indicated in Table ?Table1.1. We then combined other GSEs to create the rest of the other partial compendia. A similar approach was taken for E. coli, S. oneidensis and S. aureus. For example, E. coli data was obtained from M3D4. M3D has collected data from GEO, as well as data deposited directly to M3D by other labs. To create partial compendia for E. coli we created a single partial compendium for all GEO data that is in M3D. We then followed a procedure similar to the one detailed in the previous paragraph by randomly combining related  sets of samples (e.g., all of the data deposited by a single lab) until at least 50 samples were in the partial compendia. A similar approach was used for both S. oneidensis  and S. aureus. Operons and [http://en.wikipedia.org/wiki/Ribosomal_s6_kinase S6 Kinase] metabolic pathways In part of our analysis we consider how correlation estimates vary across different partial compendia for pairs of genes that are predicted to be co-regulated by external databases. In particular, we used operon predictions as made by Microbes Online (Price et al., 2005) and metabolic pathway definitions from the SEED (DeJongh et al., 2007). Statistical analysis For each pair of genes, based on the samples in each compendium (full and partial), we computed three different measures of pairwise gene association for all possible pairs of genes: Pearson correlation, Spearman correlation and  mutual information. We used R/Bioconductor5 to compute all three measures of association. In particular, we used the cor() function to compute the Pearson and Spearman correlations and the mutualInfo() function in the package bioDist to compute mutual information. To provide an evaluation of the consistency of correlation metrics obtained for the same pair of genes across partial compendia, we used 95% bootstrap confidence intervals, which we computed as described in the remainder of this paragraph. Let rki,j represent the correlation between genes i and j in partial compendia k. Let rki,j,z represent the correlation between genes i and j in the zth bootstrap set of samples, z = 1, ��, 1000, from partial compendia k. We compute the endpoints of a 95% bootstrap confidence interval on the difference in pairwise gene association measures between partial compendia k and l by taking the 2.5 and 97.5 percentile of d = (dk,li,j,1 ��,dk,li,j,1000), where dk,li,j,z = rkr,j,z ? rli,j,z. We computed 95% bootstrap confidence intervals using this approach for all pairs of partial compendia for each organism, and separately for each of the three correlation metrics (Pearson correlation, Spearman correlation and mutual information).