“highly significant”

This reduction was highly significant with a p -value < 2.2 × 10 −16 ( Figure 2 ).

The rescue of genes found differentially expressed in ∆ Firre CLPs by a Firre transgene produced a highly significant result ( P = 2.2e-16, Fisher exact test); however, we note that the widespread changes in gene expression observed in the CLPs from animals only expressing transgenic Firre RNA could also formally contribute to this effect.

Interplatform adjustment led to a highly significant increase in ICC coefficients (median (prior to interplatform adj., only intraplatform adj.) = 0.1; median (after interplatform adj.) = 0.46; P < 2.2 e − 16).

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.

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 ).

Indeed, we observed a moderate, though highly significant correlation between CDS length and fold change (FC) in translation (r 2 = 0.26; p < 2.2 × 10 −16 ), which produces a downregulatory effect for genes with long CDSs and, vice versa, an upregulatory effect for genes with short CDSs (Fig. 2 C).

The overall model including all predictors was highly significant for both, the plaque size in the aortic root and BCA (BCA: F(4, 94) = 37.44, p-value < 2.2e-16 r2 = 0.60; heart: F(4, 81) = 65.09, p-value < 2.2e-16, r2 = 0.75).

The Spearman’s rank correlation coefficient of nucleotide diversity by gene between species was positive and highly significant for both diversity measures ( π : ρ = 0.36, P value < 2.2e-16; θ W : ρ = 0.38, P value < 2.2e-16) for a subset of 6,798 genes with these statistics available in both species ( supplementary fig.

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).

1c ), which exhibited highly significant hypomethylation ( P < 2.2e-16; Fig. 1d ).

The variance of differences for the genomic GC proportion in humans, mice, and fruit flies respectively is σ ^ n 2 = ( 0.000673 , 0.00055 , 0.00103 ) compared to σ ^ r 2 = ( 0.00469 , 0.00552 , 0.0021 ) for the permuted genome sequence, with the ratios being highly significant in all cases (F-test with p-value p < 2.2 e - 16 ).

Further constraining the thresholds led to highly significant misfits for both positive and negative factors (positive factors, Δ χ 2 397.1, Δ DF 116, p < 2.2E-16; negative factors, Δ χ 2 444.5, Δ DF 52, p < 2.2E-16).

83.36%, respectively), although the difference was highly significant (two-sided Brunner–Munzel paired-rank test, P < 2.2 × 10 −16 ).

Conversely, a robust positive correlation was observed between PKMYT1 expression and TMB, with a correlation coefficient of R=0.4 and a highly significant P value ( Figure 5C , P<2.2e−16).

Results from Figure 3 showed a highly significant difference in MIC values among the treatments (χ 2 = 270.89, df = 5, p < 2.2 × 10 −16 ), with a very large effect size (ε 2 = 0.85, 95% CI [0.80, 0.89]).

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.

Additionally, the P-value for accuracy being greater than the No Information Rate (NIR) was highly significant (P < 2.2e-16), indicating that the model’s performance was far superior to random chance.

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 difference was highly significant ( P < 2.2 × 10 −16 ).

3C ), with the Kolmogorov-Smirnov test revealing a highly significant difference in the two distributions (p = 2.2×10 −16 ).

Consistent with this, many of these genes (80/173) were also downregulated in the DIG1 residual profile, a highly significant enrichment (Fisher’s exact test, 1-tailed: P < 2.2e−16).

This is exemplified by the colocalization of DPDs with regions of higher CpG density ( Figure 6B ) and a robust and highly significant negative correlation between CpG density and PMDs (r = −0.64, P<2.2×10 −16 ).

The power of per-gene testing was constrained by sample size, but results were highly significant (Fisher Exact p-value < 2.2e-16) when variants were combined across all genes [Table S8].

To test this prediction, we performed DRIP-seq in ovaries dissected from adult females and found strong, highly significant correlations between the female whole fly samples and ovary samples for both nonDE and FE peaksets from the adult data (Spearman’s rho > = 0.72 and P < 2.2e-16 in all comparisons, S2D Fig ).

All correlations were highly significant (for all: P < 2.2 E-16; more details on performance estimates and model statistics, see Text S1 in the Supplementary Material).