DeSCENT: Deconvolutional Single-Cell RNA-seq Enhances Transcriptome-based Cancer Survival Analysis
Related Experiment Video
Updated: Nov 7, 2026
Dissociation of Human and Mouse Tumor Tissue Samples for Single-cell RNA Sequencing
Published on: August 16, 2024
Yonghao Zhao1, Zeyu You2, Yu Shen3
1SWJTU-Leeds Joint School, Southwest Jiaotong University, Chengdu, China.
Motivation:
Single-cell RNA-seq captures tumor heterogeneity, but it is rarely available together with patient survival outcomes. Bulk RNA-seq cohorts provide survival data but lack direct measurements of cellular expression.
Results:
DeSCENT generates reference-informed cellular profiles from bulk RNA-seq and combines both representations for survival prediction. We evaluated DeSCENT with three survival models on eight TCGA cancer cohorts. Compared with Bulk, DeSCENT improved average C-index and generally improved time-dependent AUC, while integrated Brier score results were mixed. External validation showed higher average C-index than all three baselines, although performance was lower than in internal evaluation. These findings support the use of cellular representations in the evaluated transcriptome-only setting.
Availability And Implementation:
Code: https://github.com/YonghaoZhao722/DeSCENT. Archival snapshot: doi:10.5281/zenodo.22073099.
Supplementary Information:
Supplementary data are available at Bioinformatics online.
