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As the minimum standard deviation of the gene pair increases, the correlation between genes likely to not be operons shows little discernible pattern ... Figure 7 Pairwise Pearson correlation versus minimum standard deviation of gene expression value: pathways. As the minimum standard deviation of the gene pair increases, the correlation between genes in the same pathway shows a generally increasing pattern. The ... Controlling sampling bias at a genome-wide level when mining large repositories of expression data Our analysis has shown that bias in correlation estimates based on large gene expression repositories is both rampant and substantial. Thus in order to control sample bias when mining large repositories of expression data we propose that either the percent on-off or the standard deviation be used to flag potentially biased correlation estimates. In essence, through the flagging approach, we can provide confidence that the correlation estimate for a gene pair is not biased. In particular, we propose the following when a two-state (on-off) clustering model is GSKJ4 reasonable for the observed expression data for a pair of genes: unless both states for both genes are present in at least 10% of the samples, the correlation estimate should be flagged as potentially biased. In all cases, regardless of whether the two-state (on-off) clustering model is reasonable, genes with standard deviations of less than 0.5 for RMA normalized data suggest that downstream correlation estimates may be biased. These ��rules of thumb�� were derived by exploring the sensitivity and specificity of different standard deviation and percent on-off rules. Tables  ?Tables6,6, ?,77 provides the sensitivity and specificity of different standard deviation and percent of sample cutoffs at identifying pairs of genes with correlation estimates that are likely to be biased estimates of the true correlation. In particular, for pairs of genes predicted by MicrobesOnline to be highly likely in the same operon (predicted probability of at least 0.99), we examined how often different rules of thumb ��flagged�� Pearson correlations above or below 0.6. We used 0.6 as a threshold of meaningful correlation for genes truly in an operon, though other values are possible. A good rule of thumb should flag most genes in operons with correlations below 0.6 as biased, while not flagging many operon genes with correlation above 0.6. As shown in Tables ?Tables6,6, ?,7,7, a standard deviation of 0.5 and having both states present in at least 10% of the samples tended to provide maximal values of sensitivity + specificity. We note that, as with any rules of thumb, sensitivity can be improved by increasing the standard deviation threshold used to flag gene pairs (e.g., standard deviation cutoff larger than 0.5), but at the expense of specificity and vice versa. Table 6 Sensitivity and specificity of different standard deviation cutoffs.