“highly significant”

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.

Differences between delta delta G are highly significant (wilcoxon test, p-value<2.2 e −16 ) except for the comparison between bin 6 and 7 and bin 7 and 8.

The coefficients derived from the linear regression of each sample pair were highly significant ( P -value <2.2e-16), and the quality of the fit was assessed using R 2 and regression coefficient values ( Fig. 6B and C ).

There was, however, a highly significant correlation between change in TE score and GC content (Pearson’s correlation value of −0.1337, p-value<2.2e −16 ) ( Figure 1I ).

Furthermore, a linear model comparing the paired connectivity estimates from the 24-particle and 1-particle-per-day scenarios revealed a highly significant (p < 2.2e-16) and remarkably strong linear relationship, closely approaching a 1:1 correspondence (R-squared = 0.999; Fig. 3 ).

Finally, we observed a highly significant increase (one-sided Student’s t -test, p = 2.2 × 10 −16 ) in average epistasis values in the winning CR genotypes compared with the overall population, indicating that positive epistasis plays an important role in determining the fittest genotypes in this selection (Fig. 5b ).

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).

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).

We found highly significant strong positive correlation between the RNA-seq and qRT-PCR data (Pearson’s r=0.954, p <2.2e-16) (Figure 5 ).

Lastly, we compared the average TE ages between SUO and SLO genes in human and revealed a highly significant difference (p<2.2e-16, Wilcoxon Rank Sum test), with the former at 16.1% and the latter at 21.3% sequence divergence from their consensus sequences (For reference, 16–18% of unconstrained nucleotides have been substituted since the split of primates from other mammalian orders [32] ).

Comparison using a paired t-test for R s from the predictive model and their corresponding permutated variant indicated a highly significant difference between both model types ( p -value < 2.2 × 10 −16 across cases), indicating that these model predictions are extremely unlikely to be due to chance.

The ability of DEcode to predict global DE prior ranks was highly significant (P < 2.2e-16) and practically relevant (Spearman's rho = 0.53) ( Extended Data Figure 3a ).

Notably, the correlations between differential patterns of H3K4me1/2/3 and H3K27me3 in promoters of genes that were up- or down-regulated in each spermatogonial subtype, respectively, were highly significant (p < 2.2 −16 ) ( Figures S3 A and S3B).

The 138 c-Jun-regulated genes showed a highly significant 5.8-fold enrichment (p=2.2×10 −16 ) among the 1581 genes regulated during chronic denervation.

Besides mean values, the paired t tests of the IASs were highly significant between IASs from different SDs ( p < 2.2 × 10 − 16 ).

The difference was highly significant ( p < 2.2 × 10 −16 ), indicating robust epigenetic dysregulation of the SYTL4 locus in breast cancer ( Figure 2 A).

The reversal DEGs obtained from the dataset for each cell type were highly significant, based on empirical null distribution (B cells: n=24, p-value <2.2×10 −16 ; CD4+ T cells: n=67, p-value <2.2×10 −16 ; CD8+ T cells: n=108, p-value <2.2×10 −16 ; monocytes: n=183, p-value <2.2×10 −16 ; NK cells: n=66, p-value <2.2×10 −16 ) ( supplementary figure S5a–e ).

The differences in TSSL and SYL values between the OsSYL3 AA and OsSYL3 CC genotypes were highly significant (Welch’s t ‐test, P = 2.20 × 10 –16 ) (Figure 2d ).

Spearman’s correlation of the methylation levels obtained for the fully methylated and unmethylated HBV controls with Nanopolish and Guppy+Medaka indicated a highly significant correlation ( R =0.83, P <2.2 −16 ) ( Fig. 2e ).

The two-way ANOVAs demonstrated highly significant differences between the strains ( Pdyn F 1,216 = 79.25, p = 2.2×10 −16 ; Penk F 1,213 = 60.19, p = 3.54×10 −13 for the strain factor) and a significant interaction of both factors (strain x brain region: Pdyn F 3,216 = 8.79, p = 1.6×10 −5 ; Penk F 3,213 = 5.36, p = 1.4×10 −3 ).

A one-way ANOVA confirmed that the differences among residue classes are highly significant ( F (3, 14,983) = 682.6, p < 2.2 × 10 –16 ), supporting the hypothesis that CDR loops correspond to weakly coupled, energetically uncoupled regions when compared to the structural core of nanobodies.

At the provincial level, the analysis reveals a highly significant difference in case distribution across months ( χ 2 = 169.82, df = 11, p -value < 2.2e-16).

The correlation between predicted and measured scores was highly significant ( Figure 3C , Pearson’s r = 0.35, p < 2.2×10 −16 ) and reduced substantially when a randomized version of the ontology was used ( r = 0.04); the maximum achievable correlation, as previously determined by experimental genetic interaction replicates ( Baryshnikova et al., 2010 ), was r = 0.67.

Importantly, we observed a high correlation (R = 0.86, P -value < 0.001) on the level of directionality and magnitude of affected genes following knockout of PAXIP1 and STAG2 (Figure 4B and Supplementary Figure 4A ), with a highly significant ( P- value < 2.2e–16) overlap in the transcriptional changes in GC-treatment conditions ( Supplementary Fig 4B ).

The difference in the location of the median for the success statistic distributions obtained using the two approaches was highly significant ( p < 2.2e‐16) for all three drugs and showed that the PopPK approach performed significantly better than a standard NCA approach.