Table 3.
Statistical tools will be used for the study
| Variables | Statistical methods | |
|---|---|---|
| Clinical data | ||
| Incidence of UI 12-month post-partum | Categorical | Multivariate analysis, odds ratio and 95% of the Confidence interval |
| ISI Questionnaire | Categorical | Chi-square test with corrections |
| PFM: Vaginal muscle contraction pressure; digital vaginal evaluation and US | Categorical | Chi-square test with corrections |
| RAM: US and Electromyography | Categorical | Chi-square test with corrections |
| Laboratorial data | ||
| Comparison of serum concentration of CCL7; relaxin; calcium; parathyroid hormone; calcitonin; Vitamin D and Insulin | Continuous | Repeated Measures t-test or one-way analysis of variance (ANOVA) and Area under the curve |
| Laboratory assays of RAM sample | ||
| Morphological assay Pathological, morphometric, immunohistochemical and ultrastructural | Continuous | ANOVA followed by Tukey’s multiple comparison tests for the normally distributed variables |
| Categorical | Chi-square test with corrections | |
| Gene expression (PCR arrays®) | Continuous | ANOVA followed by Tukey’s multiple comparison tests for the normally distributed variables |
| Protein expression (Western blot) | Continuous | Student’s t-test and comparison of multiple means used an ANOVA with a Tukey post-test to compare two variables where applicable |
| Transcriptomics, Metabolomics and Proteomics | – | Previously described in the text |
| Ex-vivo assessment of RAM Contractility | Continuous | Student’s t-test and comparison of multiple means used an ANOVA with a Tukey post-test to compare two variables where applicable |
OMICS statistical analysis
For transcriptomics, the bioinformatics analysis will be done using the software fastqc63, FASTQ Quality Filter, FASTA/Q Clipper to identify and remove the low-quality adapters and reads. The RNA reads will be aligned to the reference sequences using the TopHat2 and Bowtie2 software. The relative gene expression will be quantified by Deseq algorithm, evaluated in Bioconductor/R64 packages. Genes/sequences with padj< 0.05 and log2 fold change > 2 or < −2 will be considered for subsequent analysis
The multivariate analyses of the M-NMR data will be carried out by PCA (Principal Component Analysis) or multivariate methods of supervised statistics (PLS or OPLS), aiming to build models for Brazil of the samples and thus Extract metabolic signatures from specific groups [64]. The calculations will be made by written (homemade) programs by our research group using the MATLAB platform (MathWorks, USA). The scoring and loading charts of the PCA analysis will be used for the display of the data. In the score graph, each point represents an NMR spectrum (i.e. a sample) for one of the main components, and the loading graph visualizes the contribution of key metabolites (statistically significant variables) to the Main component. The PCA method will be applied initially to the complete set of data, 11 to which this realization a form of unsupervised analysis. Then the multivariate supervised methods (PLS or OPLS) will be employed with a prior differentiation of the sampling groups