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2022, Nature Cancer. DOI: 10.1038/s43018-022-00356-3 PMID: 35469013 PMCID: PMC9046084
Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology
Tinyi Chu, Zhong Wang, Dana Pe'er, Charles G Danko
Abstract:
Inferring single-cell compositions and their contributions to global gene expression changes from bulk RNA sequencing (RNA-seq) datasets is a major challenge in oncology. Here we develop Bayesian cell proportion reconstruction inferred using statistical marginalization (BayesPrism), a Bayesian method to predict cellular composition and gene expression in individual cell types from bulk RNA-seq, using patient-derived, scRNA-seq as prior information. We conduct integrative analyses in primary glioblastoma, head and neck squamous cell carcinoma and skin cutaneous melanoma to correlate cell type composition with clinical outcomes across tumor types, and explore spatial heterogeneity in malignant and nonmalignant cell states. We refine current cancer subtypes using gene expression annotation after exclusion of confounding nonmalignant cells. Finally, we identify genes whose expression in malignant cells correlates with macrophage infiltration, T cells, fibroblasts and endothelial cells across multiple tumor types. Our work introduces a new lens to accurately infer cellular composition and expression in large cohorts of bulk RNA-seq data.
2022-04-29 16:29:00
#paper https://doi.org/10.1038/s43018-022-00356-3 Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology (2022) 最近为了能从bulkRNAseq数据中分析出肿瘤一致性,所以在找一款比较好用的cellar deconvolution的软件。这方面引用量最好的是CIBERSORT及CIBERSORTx,但是这两款软件存在显示的缺点是只能online分析,不能本地化部署。看到前几天(2022年4月25日)刚在Nature Cancer上发表的BayesPrism,它可以本地化部署且提供的源码,赶紧读一读且拿来了试用。 该软件采用了贝叶斯统计模型,利用已经对cell type/ cell states注释过的single cell data作为 Reference,实现了从bulk RNAseq中推断出不同肿瘤细胞的组成及比例,而且还估计除了不同cell type的gene expression。而且从文章自己展示的性能看,已经超过了CIBERSORT/CIBERSORTx/Bisque/MUSiC 了,并且在肿瘤细胞10%以上的样本中,得到的表达谱和真实表达谱相关性大于0.9。
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