Computational protocol: The association between Histone 3 Lysine 27 Trimethylation (H3K27me3) and prostate cancer: relationship with clinicopathological parameters

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Protocol publication

[…] All statistical analysis were performed using R 3.0.1 software [] and the statistical packages agricolae [], HH [] and multcomp []. All the data obtained were statistically analyzed with a one-way ANOVA to test the level of statistical significance of different groups on H3K27me3 levels and relationships between clinicalpathological parameters on different genes. Post-hoc procedures were used when the F-test was significant (p < 0.05). Multiple comparisons among means were examined by a Tukey’s Post Hoc test. The level of statistical significance was set at p < 0.05. [...] The major aim of Principal Component Analysis (PCA) is the orderly simplification of a large number of inter-correlated measures to a few uncorrelated representative constructs or factors []. In this study, a small number of linear combinations (called principal components) are derived from a set of variables (genes) measured on several observations (patients) that retain as much as possible of the information about the original variables. This goal typically overrides in empirical research its secondary aim, which is interpretation of the principal components. To a large extent, the interpretation of principal components is generally guided by the degree to which each variable is associated with a particular component []. The main outputs of the principal component analysis are the variances of the principal components and two plots: the projections of the original variables; and the observations onto the plane made by the two first principal components. In the first plot, as all variables are normalized (mean equal to zero and variance equal to 1), the distances between the projections of the variables and the centre are equal to 1, meaning that the projections near the circumference of the circle belong to the plane of the two first principal components, and the projections near the centre do not belong to the plane as they appear to be orthogonal. Those variables, found to be most closely related to a component in question are used as a guide for its interpretation. Principal Component Analysis was performed using the statistical package FactoMineR []. […]

Pipeline specifications

Software tools multcomp, FactoMineR
Applications Miscellaneous, GWAS
Diseases Neoplasms, Prostatic Neoplasms