De novo Gene Signature Identification from Single-Cell RNA-Seq with Hierarchical Poisson Factorization. De novo Gene Signature Identification from Single-Cell RNA-Seq with Hierarchical Poisson Factorization

Common approaches to gene signature discovery in single cell RNA-sequencing depend upon predefined structures like clustering or pseudo-temporal orderings, do not account for the sparsity of single cell data, or require prior normalization. We present single cell Hierarchical Poisson Factorization (scHPF), a Bayesian factorization method that adapts Hierarchical Poisson Factorization for de novo discovery of both continuous and discrete expression patterns in complex tissues. scHPF does not require prior normalization and outperforms other methods in benchmark datasets. Applied to single cell RNA-sequencing of the core and margin of a high-grade glioma, scHPF uncovers subtle regional expression biases within glioma subpopulations and an expression signature associated with inferior survival in glioblastoma. Overall design: Performed single cell RNA-seq on radiographically-localized tissue samples from a high-grade glioma and a glioma tumor-sphere.

当前用于单细胞RNA测序（single cell RNA-sequencing）中基因特征发现的常用方法，要么依赖聚类、拟时间排序等预定义结构，要么未考虑单细胞数据的稀疏性，抑或是需要进行预先标准化处理。本研究提出单细胞分层泊松因子分解（single cell Hierarchical Poisson Factorization, scHPF），这是一种贝叶斯因子分解方法，通过对分层泊松因子分解进行适配，可实现复杂组织内连续与离散表达模式的从头发现。scHPF无需预先标准化处理，且在基准数据集上的表现优于其他同类方法。将scHPF应用于高级别胶质瘤核心区与边缘区的单细胞RNA测序数据后，其可揭示胶质瘤亚群内细微的区域表达偏倚，以及胶质母细胞瘤中与不良预后相关的表达特征。实验整体设计：对高级别胶质瘤及胶质瘤肿瘤球的影像学定位组织样本开展单细胞RNA测序。