Here’s a breakdown of the software and packages used in the study, extracted from the provided text:
Programming Languages & Core Libraries:
* Python: Used for GSEApy, Venn diagram creation, and general data manipulation.
* R: Used for cibersortx, circlize (chord diagram), and reprocessing of the GSE120649 dataset.
Specific Packages & Versions:
* scikit-fda 0.9.1: used for k-neighbors classifier on proteomics data.
* Netgraph 4.13.1: Used for network plotting and annotation.
* adjustText 1.3.0: Used for adjusting text labels on plots (link provided).
* IOBR 0.99.8: R package for estimating cell population abundance from transcriptomic data.
* GSEApy 1.1.7: Python package for gene set enrichment analysis (link provided).
* venn 0.1.3: Python package for creating Venn diagrams.
* circlize 0.4.16: R package for creating chord diagrams.
* SciPy 1.11.4: Python package for scientific computing, used for distance correlation clustering.
* seaborn 0.13.2: Python package for statistical data visualization,used for heatmap plotting.
* DESeq2: R package for normalizing transcriptomic expression counts.
Data Sources/Datasets:
* GSE120649: public dataset from NCBI GEO, used for comparative analysis of allograft rejection.
This list provides a comprehensive overview of the computational tools employed in the research.