Excerpts

The correlation between the inbreeding coefficients and F ROH were also highly significant (F I: r = 0.71, P = 2.20 × 10 −16 ; F II: r = 0.72, P = 2.20 × 10 −16 ; F III: r = 0.83, P = 2.20 × 10 −16 ).

This difference of 44,043 bp is highly significant (χ2 = 30823.1, df = 3, p-value < 2.2e-16) and corresponds to a 61 % increase of the S. latifolia chromosome size as compared to S. vulgaris .

S6 , we found a highly significant correlation between amplicon genus abundance PCoA1 and shotgun species abundance PCoA1 (the Spearman’s correlation ρ = 0.881 , p-value < 2.2 × 10 - 16 ).

However, it is interesting to note the highly significant co-directionality (binomial test: p < 2.2e−16) of the expression changes observed for nitrogen-limited cultures and in hospite symbionts, with 92.67% of the 12,295 (11,394) DEGs changing expression in the same direction (Fig. 2 e).

Analysis of variance showed that the genotypic (G) and the year (Y) effects for fruit weight were highly significant for each population ( P values < 2.2e-16).

The Kruskal–Wallis test revealed highly significant differences between conditions at 0–24 h (H = 147.38, df = 2, p = 2.2 × 10 −16 ) and 24–48 h (H = 121.46, df = 2, p = 2.2 × 10 −16 ).

The correlation levels are moderate, yet highly significant ( p -values < 2.2 × 10 −16 ), therefore it is likely that these different measures highlight the same underlying core phenomenon.

This difference was highly significant (Mann-Whitney test: U = 2.2×10 7 , P <2.2×10 −16 ).

This deviation from the expected distribution is highly significant by chi-square analysis ( P value = 2.2 × 10 -16 ).

Based on paired t -test, we showed that the exponential regression model had highly significant mean R 2 accuracy than each of the existing methods (Holsteins: t = 584.8–37281; p < 2.2–16; Jerseys: 1178.5–5861.4; p < 2.2e-16).

The excess of GC to AT substitutions is highly significant, indicating non-equilibrium ( p < 2.2 × 1 0 − 16 ).

We observed a highly significant gain of methylation with age at the locus in the LBC cohort, validating our approach (Fig. 1 a, T-test, p < 2.2 × 10 −16 ).

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

Our results indicated that 48.1% ( n = 11,422, P < 0.05) of the examined drift-CpGs exhibited a nominally significant difference (OR = 5.0, Fisher's test, P < 2.2 × 10 −16 ) between these two time points.

With this larger and more diverse dataset, the excess similarity of orthologs is also highly significant for the Molecular Function ontologies (all orthologs vs. all paralogs, t-test, p <2.2 10 −16 ), as for the Biological Process ( Fig. 3 ).

We calculated an odds ratio for this value using a Fisher's exact test and found this association to be highly significant (odds ratio=3.02; P <2.2×10 −16 ).

This comparison resulted in 196 common antigens out of 481, a highly significant overlap (odds ratio: 6.4088; 95% CI: 5.3011–7.7479; chi 2 p -value < 2.2 × 10 −16 ).

Remarkably, we observed a highly significant negative correlation between B cell levels and HEUScore among all immune cell subtypes ( r = -0.8857, P < 2.2e-16) ( Figure 5G ).

It is evident from the data that genome size influences the frequency of microsatellites, supported by a highly significant positive correlation between genome sizes and frequency of microsatellites ( R 2 = 0.53 and P -value = 2.2e –16 ) ( Fig. 2D ).

When examining 92 age‐associated metabolites, the correlation between the age beta coefficient was highly significant ( r = 0.79, p = 2.2e‐16; Figure S2B ), suggesting the reliability of our method. 91 of them changed with age in the same direction, among which 27 were statistically significant.

This comparison also revealed highly significant, albeit slightly weaker correlations (Pearson correlation 0.73; P <2.2e-16; Figure S11 ) – similar to the results we obtained for chromosome 1 CNVs.

Analysis of variance based on the reparametrized polynomial-interaction model with MPR effects showed a highly significant impact of partial yields on test-day yields ( P <2.2e-16).

These differences are highly significant (X 2 test p-value < 2.2e-16).

The correlation coefficients were highly significant in each case (PrEC: r = 0.80, p < 2.2e−16; LNCaP: r = 0.91, p < 2.2e−16; NPF: r = 0.84, p < 2.2e−16; CAF: r = 0.95, p < 2.2e−16), but not completely concordant with WGBS data (Additional file 5 : Figure S4A).

Indeed, this overestimation was highly significant (1-tailed t- test, t = 9.66, df = 557.16, p < 2.2 –16 ).