Tool Comparison for Detecting Tumour Cells in Endometrial Cancer via Single-Cell Copy Number Variations Analysis.

ABSTRACT: Copy number variations (CNVs) are considered a hallmark of cancer and their inference from high-resolution single-cell transcriptome (scRNA-seq) analyses may offer great opportunities for the study of tumor heterogeneity. We compared the results of four major tools (SCEVAN, CopyKAT, InferCNV and sciCNV) that use inferred CNVs to predict endometrial cancer (EC) cells, in order to assess their reliability and offer useful suggestions to researchers to improve the accuracy of their predictions. In this study, we identified EC cells from publicly available scRNA-seq data using well-established EC biomarkers reported in the literature. SCEVAN and CopyKAT tools have moderate sensitivity, but significantly overestimate the true number of true EC tumour cells. However, a comparative analysis of th

SUBMITTER: Dugo E 

PROVIDER: S-EPMC12571190 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature