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R statistical software package version 2

Sourced in Austria

R is an open-source software environment for statistical computing and graphics. Version 2.14.1 provides a wide range of statistical and graphical techniques, including linear and nonlinear modeling, classical statistical tests, time-series analysis, classification, clustering, and others. R is widely used in various fields for data analysis and visualization.

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6 protocols using r statistical software package version 2

1

Maternal Diet Effects on Offspring Weight

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All data were analysed using hierarchical linear models with a random effect for litter of origin. Maternal diet and pregnancy status were included as fixed effects. This structure accounted for the fact that post-partum and non-pregnant littermates are derived from a single pregnancy, and these data are therefore effectively paired and cannot be treated as fully independent. Multiple hypothesis correction testing was performed using the P-values obtained from the regression models, correcting for the false discovery rate (FDR). All body weights were expressed as a ratio of current weight/weight on study day 0. Data are represented as means ± SE. A value of P < 0.05 was considered statistically significant. All data analysis was conducted using the R statistical software package, version 2.14.1 (R Foundation for Statistical Computing, Vienna, Austria). In all cases, n refers to the number of litters.
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2

Maternal Diet Effects on Offspring Growth

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Data were analysed using Prism 6 (GraphPad). Offspring growth trajectory was plotted as an average of individual measurements (n = 8) at each time point. Only one male mouse from each litter was used in each analysis as the experimental unit in programming studies is the dam (i.e. eight pups = eight dams represented). Statistical comparisons were made using an unpaired two-tailed Student’s t-test, or a two-way analysis of variance (ANOVA) to estimate the effect of two variables (i.e. maternal diet and offspring diet), followed by Dunnet’s multiple comparison test. The hierarchical linear model was computed using the R statistical software package version 2.14.1 (R Foundation for Statistical Computing, Vienna, Austria). Data are means ± SEM unless stated otherwise. For all statistical comparisons, the level of significance was set at P <0.05.
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3

Maternal Diet Effects on Offspring

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All data were initially analyzed using a 2-way ANOVA with maternal diet and offspring diet as the independent variables. To correct for multiple hypothesis testing of gene expression levels, P values were transformed to q values to take account of the false discovery rates using the p.adjust function in R stats package (R Foundation for Statistical Computing, Vienna, Austria). This adjustment was designed for this study to take account of the specific number of genes that were tested within the initial screen (24 (link)) and therefore to ensure that the P values were optimally transformed. Maternal diet effects were compared between groups using 2-tailed Student’s t tests. Data are represented as means ± sem. Where P values are reported, an α level <0.05 was considered statistically significant. All data analysis was conducted using the R statistical software package version 2.14.1 (R Foundation for Statistical Computing). In all cases, the number of litters = 8 for all groups. Study power was determined based on effect sizes for primordial follicle counts and mtDNA copy number observed in our previous studies of follicular reserve in other dietary models (2 (link), 9 (link)).
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4

Maternal and Offspring Diet Effects

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All data were initially analyzed using a 2-way ANOVA with maternal diet and offspring diet as the independent variables. In order to correct for multiple hypothesis testing of gene expression levels, p values were transformed to q values to take account of the false discovery rates using the p.adjust function in R stats package (R Foundation for Statistical Computing, Vienna, Austria). Data are represented as means ± SEM. Where p values are reported, an alpha level <0.05 was considered statistically significant. All data analysis was conducted using the R statistical software package version 2.14.1 (R Foundation for Statistical Computing, Vienna, Austria). In all cases, n refers to the number of litters, and n=8 for all groups. Study power was determined based on effect sizes for gene expression differences observed in our previous studies of this model24 (link).
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5

Maternal Diet and Exercise Effects on Cardiomyocytes

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Morphometric and physiologic data were analyzed using Prism7 (Graphpad, USA). Values are expressed as mean ± SEM. For maternal data, two-way ANOVA was carried out with repeated measures to determine the overall effect of maternal group and time, over mating and gestation. Otherwise, one-way ANOVA was used to compare the three experimental groups; a Bonferroni post-hoc test was carried out when the overall P value for ANOVA was less than 0.05.
Cell area data were analyzed using a hierarchical linear model. A hierarchical linear model with random effects for individual animal, and an interactive effect between individual and section of origin was used to analyze cardiomyocyte cell area. The model included maternal diet and maternal exercise as fixed effects. This structure accounted for the fact that multiple cell area measurements were obtained from multiple sections of the heart of each individual animal within the groups, and these data cannot be treated as fully independent [R statistical software package version 2.14.1 (R Foundation for Statistical Computing, Austria)].
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6

Hierarchical Linear Modeling of Offspring Diet

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All data were analyzed using a hierarchical linear model with a random effect for litter and grand-maternal diet and offspring age as fixed effects. This structure accounted for the fact that offspring from a single litter are represented at both 3 months and 6 months of age, and these data cannot be treated as fully independent. Data are represented as means ± SE. A value of P < 0.05 was considered statistically significant. All data analysis was conducted using the R statistical software package version 2.14.1 (R Foundation for Statistical Computing, Vienna, Austria). In all cases, n refers to the number of litters.
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