2026-10-14T17:31:13Z https://www.scidb.cn/oai

10.57760/sciencedb.41697 2026-07-02T11:12:12Z

2026-07-02 Source Code and Processed Data for Clustering Analysis of Giant Cell Arteritis Using Varicella-Zoster Virus-Human Interacting Proteins doi:10.57760/sciencedb.41697 en This dataset contains the complete source code and all processed intermediate data for the research entitled "Varicella-zoster virus (VZV)-human interacting proteins enable the clustering of giant cell arteritis (GCA) samples: supports the association of VZV with a subset of GCA cases". All files are packaged in a single ZIP archive for integrated access.Data generation and analytical pipelineThe analysis is built upon 4 publicly available independent datasets, including 1 plasma proteomics dataset (Plasma_GCA_2024) and 3 transcriptomics datasets (GSE201753, Aortitis_2022, GSE174694). For proteomics data, quantile normalization was first performed across samples, followed by gene-wise Z-score standardization. For transcriptomics raw count data, variance stabilizing transformation (VST) was conducted via the DESeq2 package, followed by the same gene-wise Z-score normalization. Spectral clustering algorithm was applied to explore the intrinsic grouping pattern of GCA samples using VZV-human interacting proteins as features, and the silhouette coefficient was calculated to quantify clustering performance. Monte Carlo simulation with 10,000 repeated random samplings was used to evaluate the statistical significance of the observed clustering effect, with all analytical parameters kept consistent between the target protein set and random gene/protein sets.Contents of the zipped packageThe single compressed archive includes:Full R scripts covering the entire workflow: data preprocessing, spectral clustering implementation, silhouette coefficient calculation, and Monte Carlo permutation testPreprocessed expression matrices of all four included datasetsOutput result files of clustering analysis and statistical significance testsA README file illustrating the directory structure, required runtime environment, dependent packages, and step-by-step reproduction instructionsSoftware environmentAll computational analyses were implemented in R (version 4.5.2). Key dependent packages include DESeq2, cluster, kernlab, and ggplot2.Intended useThis dataset supports full reproducibility of all computational analyses and figures in the corresponding manuscript. It also provides a reference analytical pipeline for exploring host-virus interaction associated molecular subtypes in other vasculitis or autoimmune disease studies. Varicella-Zoster Virus-Human Interaction Protein; Varicella-Zoster Virus; Giant Cell Arteritis; Spectral Clustering; Monte Carlo Simulation; Proteome; Transcriptome 叶葳 RESTRICTED dataset Science Data Bank