Data Sheet 2_Integrated bioinformatics and single-cell transcriptomics identify mitochondrial dysfunction-driven biomarkers for preeclampsia diagnosis.csv

Current diagnostic and treatment methods are limited, necessitating the identification of effective biomarkers and therapeutic targets.</p>Methods<p>This study utilized bioinformatics methods, including differential expression analysis, weighted gene co-expression network analysis, protein–protein interaction networks, and machine learning algorithms, to identify critical mitochondrial genes associated with preeclampsia.

Enrichment analysis was performed using gene set enrichment analysis, gene set variation analysis, gene ontology, and the Kyoto Encyclopedia of Genes and Genomes. Receiver operating characteristic analysis and SHapley Additive exPlanations analysis were used to evaluate the diagnostic performance of key genes and diagnostic models. Single-cell analysis provided insights into cellular heterogeneity and interactions.