“highly significant”

Pearson correlation analysis of these whole‐sample average expression profiles revealed highly significant correlations between replicates (Peach: r = 0.94, p < 2.2e‐16; Nectarine: r = 0.91, p < 2.2e‐16), with LOESS regression confirming the linear relationship (Figure S15A ).

As a result, we found a highly significant positive relationship between visual bleaching scores on Day 0 and Day 7 post-stress ( Fig 3A and S2 Table , linear mixed effects model, p < 2.2e -16 , marginal R 2 = 0.863, df = 44), such that VBS at Day 0 post-stress was highly predictive of VBS throughout the following seven-day period.

This overlap is highly significant ( P < 2.2e-16), indicating that H3K4me3 levels at a core set of genes provide a fingerprint of overall health in both mothers and children at 1 y of age.

The high proportion of variation among environmental means in Figure 3 is supported by highly significant (p< 2.2e-16) differences among the mean effect of each environment.

Bartlett’s test of sphericity was highly significant (χ2 = 362.49, df = 11, p < 2.2 × 10 −16 ), confirming that the correlation matrix was not an identity matrix and that the dataset had sufficient inter-variable relationships for factor analysis.

The overall percentage of HG disease was highly significant, with a hazard ratio of 4.45 for the interquartile range (95% confidence interval 3.30–6.01, P < 2.2 × 10 −16 ), and was similar to the percentage of HG disease seen in the worst core.

The correlation of the concentrations determined by 1 H-NMR and by quantitative methods were highly significant for both the samples of ccDMEM (Pearson: r = 0.99, p = 2.20 * 10 −16 ) and of vcDMEM (Pearson: r = 0.91, p = 2.84 * 10 −6 ).

Clustering of MBM regarding β-values of each CpG area revealed a highly significant ( p < 2.2e−16) separation of BRAF mut and BRAF wt /NRAS mut tumors (Fig. 8a , right panel).

Based on the comparison of intact to degraded cartilage, the log-fold-changes in RNA expression and the differences in mean promoter region methylation values demonstrated a small but highly significant correlation (Pearson’s r = −0.08, p < 2.2 × 10 −16 , Fig. 2b , Supplementary Table S7 ).

A Wilcoxon signed rank test, which compared the ranks based on the two methods, was highly significant ( P < 2.2 × 10 −16 ).

Moreover, not only do we find differences in predictive performance between populations from different continents, we also find highly significant differences in prediction accuracy among the four European ancestry populations considered ( F -test p -value < 2.2 × 10 −16 ).

This shift was highly significant as determined by the Mann-Whitney-Wilcoxon Test ( P <2.2*10 −16 ).

Further, the concordance of miRNA expression (log normalized) of the common miRNAs was also explored by Spearman correlation and showed a highly significant correlation with rho of 0.81, p<2.2x10 -16 ( Fig 4b ).

A Wilcoxon paired test showed that these results are highly significant (both P < 2.2 × 10 −16 ).

Notably, while the in vitro and the denatured control samples slightly but significantly anticorrelated (average PCC = −0.07, Edgington-combined FDR-adjusted P -value = 3.3e–6), a moderate yet highly significant correlation was instead detected between the in vivo and the denatured control samples (average PCC = 0.18, Edgington-combined FDR-adjusted P -value < 2.2e–16).

Overall, we observed a modest but highly significant correlation of predictions with both these regulatory prediction scores reported by DeepSEA (multi-tissue: r = 0.227, p<2.2e-16; PanIslets: r = 0.223, p<2.2e-16) ( Figure 3—figure supplement 1 - AB ).

Transcripts that lose m6A during differentiation show highly significant increase in TE in glioblastoma samples irrespective of subtype (GSC1: p<2.2e-16, GSC2: p<3.4e-11, GSC3: p<2.2e-16) (Fig B in S1 Text ).

Averaging power in this range across the entire cue presentation period revealed a highly significant difference between the six regions in the 3-s spatial working memory task (F 5,1096 = 19.98, p = 2.2E-16, effect size: η 2 = 0.09).

Wilcoxon rank sum test with continuity correction revealed highly significant changes for old versus young cortex and hippocampus circRNA TPM values ( P < 2.2 E-16) ( Fig. 2d ).

Single marker analysis revealed that NAL1 _4_31214045 had a highly significant association with tiller number ( p < 2.2 × 10 − 16 ) and explained 22.07% of the phenotypic variance (PVE), while OFP19 _5_15069810 showed a weaker but still significant association ( p = 1.99 × 10 − 5 ), with a PVE of 3.65% (Table 5 ; Fig. 6 A, B).

The beta estimates for these SNPs showed a highly significant linear correlation ( P < 2.2e−16, Fig. 1g, j ).

The correlation between HGS and NBIGS in EVTs was highly significant ( Figure 6 B; SDQ 3 rho = 0.54 p < 2.2e-16; Table S6 ).

This finding of 50% sensitivity (considering only the peaks that strictly overlap a site) is highly significant (Fisher’s exact test; P < 2.2 e –16) and is a strong indication of the quality of the results, all the more so as the literature derived studies were performed in a variety of species and cell types.

We observed a highly significant difference in the mean correlation values ( P -value < 2.2e-16, lower-tailed Wilcoxon rank sum test), with a median correlation value of -0.15 for the differentially methylated promoters detected by MethylDriver, more than twice as strong as the average association for all promoters ( Supplementary Figure S4 ).

Using an extended unfiltered set of open regions as background for Fisher’s exact test, both overlaps were found to be highly significant ( p value < 2.2e−16), with a slightly higher odds ratio for the modified protocol (13.15 vs. 10.75).