Spss software 19

MiRNA-Seq data was processed by the limma (Ritchie et al., 2015 (link)) package in R software. The survival curve was described by Kaplan–Meier survival plot and analyzed with log-rank test. The differences between the two groups were analyzed by paired or unpaired Student’s t-test. The Chi-square test was used for exploring the association between miR-383-5p expression and clinical features (such as: age, gender, tumor size, lymph node metastasis, TNM stage, and differentiation grade). P < 0.05 was recognized as statistically significant and all statistical analysis were conducted by IBM SPSS software 19.0.

Metabolite levels were first normalized to creatinine and then unit variance scaled. The transformed data met the assumptions of the tests, and the variance between the groups was similar. Next, SIMCA-P software 11.0 was used to perform orthogonal partial least-squares discriminant analysis (OPLS-DA). A 199-iteration permutation test was applied to rule out separation non-randomness between MDD and HC samples. Metabolites with variable importance plots (VIP) >1.0 were identified as differential metabolites responsible for sample differentiation.¹⁹ Heat maps of these metabolites were obtained using R software 3.1.0 (Stanford University, CA, USA). In order to identify the simplest potential diagnostic biomarker panel from these differential metabolites, SPSS software 19.0 (IBM Analytics, New York, NY, USA) was used to perform binary logistic regression analysis. The Bayesian information criterion was applied to select the best-fit model.^{12 (link)} Receiver-operating characteristic analysis was applied to determine the diagnostic performance of the best-fit model.

The online resource at the Institute for Human Genetics, Munich, Germany (http://ihg.gsf.de) was used for the comparison of rs12979860 and ss469415590 genotypic frequencies between groups to determine p values, odds-ratios (OR) and confidence intervals (C.I.). This resource was also used to carry out the Hardy-Weinberg equilibrium (HWE) test and to calculate the allelic frequencies for each marker. Pair-wise LD estimate (r²) was determined using the Plink software [22] (link).
Group comparisons of categorical variables were performed using Pearson chi-square test or Fisher’s exact test. To compare age among groups we used the Mann-Whitney U tests (data not normally distributed). Multivariate logistic regression models were elaborated including variables associated with SVR in univariate analysis (p<0.05), as well as age and gender, to obtain adjusted p and OR values. All these calculations, as well as those performed to obtain the area under the receiver operating-characteristic curve (AUROC) of models, were carried out using the SPSS software 19.0 (IBM Corporation, Somers, NY, USA). Comparisons between the AUROCs were performed using the MedCalc Statistical Software version 12.7.7 (MedCalc Software bvba, Ostend, Belgium; http://www.medcalc.org; 2013).

Data are presented as mean ± standard deviation (SD). Differences between the mean values for individual groups were assessed with one-way analysis of variance (ANOVA) with Student-Neumann-Keuls post-hoc test, and the data were tested for a two-sided statistical testing. p < 0.01 and p < 0.05 were considered to indicate a statistically significant difference. SPSS software 19.0 (IBM Software; New York, NY, USA) was used for statistical analyses.

Data were analyzed with SPSS software 19.0 (IBM, Armonk, NY, USA) and are presented as mean±s.e.m. Correlations were assessed according to Pearson's correlation analysis. One-way ANOVA followed by Bonferroni correction was used to compare the data of more than two groups, while a unpaired, two-tailed t-test was used for nonparametric data comparison. A value of P<0.05 was considered statistically significant.