Partial correlations between gene expression variability and methylation and between GC3 and gene expression variability are much smaller than the full correlation coefficients.
A complete table containing the raw gene tag counts (Log2 scale) and full Pearson correlation coefficient matrix, for all sequencing runs, can be found in Additional File 2. Second, we looked at the concordance/reproducibility of gene identification across different lanes and different libraries.
The 11 coefficients with largest differences explain almost 90%% of total difference (2.54 versus 2.98 nT.yr −1 rms differences for the full model for B, with a correlation coefficient of 0.8 between the full model and that based on these 11 coefficients only).
The Akaike information criterion of the full model was 85795.051; the correlation coefficient of the Schoenfeld Residual for acupuncture and time of the full model was 0.00875, and the p-value was 0.5787, or no indication of lack of fit for the model.
We compared the PCs and FACs obtained from random subsets to those from the full sample using the Pearson correlation coefficient, r.
> -wrap-foot> We compared full and partial correlation coefficients, calculated as in Kim and Yi (2007), between GC3, gene expression variability, and gene-body methylation (table 1).
Network inference methods based on GGM make use of the relation, (55) ρ ij | V ∖ { ij } = − ω ij ω ii ω jj, connecting the partial correlation coefficient of full-order (LHS) with the elements of Ω, ω ij ∈ Ω.
The Generalized Chi-Square/df for the full model was 0.54, the Spearman correlation coefficient between the observed and predicted responses was 0.75 (p<0.001), and the simple squared deviations statistic was 0.886.
The spearman rank correlation coefficient method and the full factorial designs method were used for a sensitivity analysis and validation.
We used full-order partial correlation coefficients to construct correlation network of metabolites to remove the correlation between metabolites due to direct and indirect dependencies on the up-stream metabolites in the pathway.
