Department of Biostatistics, Brown University, Providence, RI, USA.
Department of Molecular Biology, Cell Biolgy, and Biochemistry, Brown University, Providence, RI, USA.
BMC Genomics. 2024 Sep 18;23(Suppl 4):866. doi: 10.1186/s12864-024-10763-8.
Aging is a complex, heterogeneous process that has multiple causes. Knowledge on genomic, epigenomic and transcriptomic changes during the aging process shed light on understanding the aging mechanism. A recent breakthrough in biotechnology, single cell RNAseq, is revolutionizing aging study by providing gene expression profile of the entire transcriptome of individual cells. Many interesting information could be inferred from this new type of data with the help of novel computational methods.
In this manuscript a novel statistical method, penalized Latent Dirichlet Allocation (pLDA), is applied to an aging mouse blood scRNA-seq data set. A pipeline is built for cell type and aging prediction. The sequence of models in the pipeline take scRNA-seq expression counts as input, preprocess the data using pLDA and predict the cell type and aging status.
pLDA learns a dimension reduced representation of the expression profile. This representation allows identification of cell types and has predictability of the age of cells.
衰老过程是一个复杂的、异质的过程，有多种原因。对基因组、表观基因组和转录组在衰老过程中的变化的了解，揭示了对衰老机制的理解。生物技术的一个最新突破，单细胞 RNAseq，通过提供单个细胞的整个转录组的基因表达谱，正在彻底改变衰老研究。借助新颖的计算方法，可以从这种新型数据中推断出许多有趣的信息。
在本文中，一种新的统计方法，惩罚潜在狄利克雷分配（pLDA），被应用于衰老小鼠血液 scRNA-seq 数据集。建立了一个用于细胞类型和衰老预测的管道。该管道中的模型序列以 scRNA-seq 表达计数作为输入，使用 pLDA 对数据进行预处理，并预测细胞类型和衰老状态。
pLDA 学习了表达谱的降维表示。这种表示形式允许识别细胞类型，并具有细胞年龄的可预测性。
