Pandas

PRGS was presented as mean ± SD for patients having four biopsies at least, and in addition, the highest and lowest grading was reported [4 (link)]. Continuous variables were presented as mean with standard deviation (SD) or median with range or interquartile range (IQR) for skewed data. Categorical variables were reported as frequencies (%) and compared with the chi-square test. Depending on the normality of distribution, Student’s t-test and Mann–Whitney U test or Wilcoxon signed ranked test were used for float comparisons. Statistical correlations were tested by use of Pearson’s rank correlation. A level of 0.05 was considered statistically significant. Statistical analyses were performed, and figures were produced with SPSS v20 software (Chicago, IL, USA), GraphPad Prism 7 (GraphPad Software, Inc., La Jolla, CA, USA), Python, NumPy, Pandas, and Seaborne (Anaconda, Berlin, Germany).

The analysis was performed in an Anaconda Python environment with the following packages: Python (version 3.9), BioPython (version 1.79), Matplotlib (version 3.5.1), Plotly (version 4.14.3), Scikit-learn (version 1.0.2), Pandas (version 1.4.1), Pyteomics (version 4.5.3), Matplotlib-venn (version 0.11.6), Seaborn (version 0.11.2), UMAP-learn (version 0.5.2), HDBSCAN (version 0.8.28), and xlrd (version 2.0.1), Logomaker v0.8. An R environment within Anaconda was also used, consisting of R-essentials (version 4.1), R-base (version 4.1.2), and R-devtools (version 2.4.3). These tools and environments enabled data processing and creation of visualizations.