Prism 6.0a

All statistical analyses were performed using GraphPad Prism 6.0a (GraphPad, La Jolla, CA) for Windows. Tests performed are described in individual Figure legends, along with p-values and significance (ns = not significant, * = p < 0.05, ** = p < 0.01, *** = p < 0.001, **** = p < 0.0001).

In all studies, analyses were performed blindly by a third party, and randomization was applied when animals were injected with saline, LNA‐control, or LNA‐miR‐29. Data are presented as mean ± the standard error of the mean (SEM) (n is noted in the figure legends). Groups were compared with Student's t‐test for parametric data. When comparing multiple groups, data were analyzed by analysis of variance (ANOVA) with Bonferroni's post‐test. Significance was accepted at the level of P < 0.05. Data analysis was performed using SigmaPlot 11.0 (Systat Software, Inc., San Jose, CA) and GraphPad Prism 6.0a software (GraphPad, San Diego, CA). We used the QSpec software proposed as a measure to detect differential expression of proteins. QSpec utilizes a hierarchical Bayes estimation of generalized linear mixed‐effects model (GLMM) to share information across the protein levels (Choi et al, 2008). This eliminates some of the assumptions needed for standard statistical tests and can increase the power of the analysis when there are a limited number of replicates available. The false discovery rate (FDR) is calculated with mixture model‐based method of local FDR control based upon the Bayes factors. We considered proteins with a Bayes Factor < 10 and an FDR < 5%, including those with fold changes above 30%, to be significant.

The data were analyzed with the software GraphPad Prism 6.0a for Mac OS X (GraphPad Software, La Jolla, CA, USA). Comparison between two groups was performed with student t test. Multiple groups comparison was done by analysis of variance (ANOVA). Tukey’s test was used as the post-hoc test to compare the difference between two groups. The statistical significance was considered when the P value is less than 0.05.
GraphPad Prism 6.0a, which uses analysis of covariance (ANCOVA) to compare the slopes of the linear regression lines, was used to analyze the data from the blood flow test. It has been revealed that the magnitude of the hemodynamics change is related to the initial resting values (Millar et al. 2007 (link); Baross et al. 2012 (link)). Hence, ANCOVA was implemented to determine whether there was a significant difference in the contracting TA blood flow of three groups compared to the resting measures, using the resting values as the covariate. Then post-hoc analysis (Tukey’s test) was used to further determine significant difference between two groups. Linearity of data was confirmed by passing residual normality test (Kolmogorov-Smirnov test) and random distribution of residuals in residual plots (Additional file 3: Figure S3). The statistical significance was considered when the P value is less than 0.05.

Using the microarray power calculation tool available at http://bioinformatics.mdanderson.org/MicroarraySampleSize/, we determined that, assuming a 5% false positive rate, a desired fold difference of 2, a desired power of 0.8, and a standard deviation of 0.7, eight replicates per condition would be needed. The median number of replicates per condition in healthy adult blood stimulation is eight. For identification of DETs in microarray experiments, a one-way Welch ANOVA was conducted using a p-value cutoff of 0.05 and Benjamini–Hochberg false discovery rate multiple testing correction. The differences between pairs were analyzed using Tukey’s post hoc test. GeneSpring v.7.3.1. software was used for statistical analysis of microarrays, principal-component analysis, and heatmap plotting. Intracellular and secreted protein levels were compared using the Mann–Whitney U test in GraphPad Prism 6.0a (GraphPad Software). Data were plotted with GraphPad Prism and the ggplot2 v2.1.0 graphical package R v. 3.2.1.