DeSCENT: Deconvolutional Single-Cell RNA-seq Enhances Transcriptome-based Cancer Survival Analysis
Zhao, Y.; You, Z.; Shen, Y.; Chu, J.; Gong, X.; Li, T.; Wang, Z.; Xu, C.; Luo, Z.; He, Y.
Show abstract
MotivationAccurate cancer survival prediction requires modeling tumor heterogeneity across both population and cell levels. Most cancer survival analyses use tumor transcriptomes only, since cohorts are usually measured with bulk RNA-seq but are rarely recorded with single-cell RNA-seq. This prevents the direct use of cell-level transcriptomes in cancer survival analysis. ResultsTo bridge this gap, we propose using bulk RNA-seq deconvolution algorithms to reconstruct each subjects scRNA-seq profile from their bulk data. Then, by combining both scRNA-seq and bulk RNA-seq together with their survival labels (paired to bulk), we perform multimodal transcriptome-based survival analysis. We built this framework as DeSCENT and evaluated it with common survival models on eight TCGA cancer cohorts. Results showed notable and consistent improvements in C-index over bulk-only models or models using cellular information alone. AvailabilityOur code is available at GitHub: https://github.com/YonghaoZhao722/DeSCENT. Contactzpluo@swjtu.edu.cn; y.he@imperial.ac.uk Supplementary informationSupplementary data are available at Bioinformatics online.
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- mcRigor: a statistical method to enhance the rigor of metacell partitioning in single-cell data analysis 94%
- Integrating T-cell receptor and transcriptome for large-scale single-cell immune profiling analysis 94%
- Deep generative model embedding of single-cell RNA-Seq profiles on hyperspheres and hyperbolic spaces 94%
Similar papers in this journal
- An in-depth comparison of linear and non-linear joint embedding methods for bulk and single-cell multi-omics 96%
- Sincast: a computational framework to predict cell identities in single cell transcriptomes using bulk atlases as references 94%
- Novel multi-omics deconfounding variational autoencoders can obtain meaningful disease subtyping 94%
Similar papers in this journal
Similar papers in this journal
- Highly Accurate Cancer Phenotype Prediction with AKLIMATE, a Stacked Kernel Learner Integrating Multimodal Genomic Data and Pathway Knowledge 94%
- Iterative point set registration for aligning scRNA-seq data 94%
- Optimal tuning of weighted kNN- and diffusion-based methods for denoising single cell genomics data 94%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.