Osorio Daniel, Zhong Yan, Li Guanxun, Huang Jianhua Z, Cai James J
Department of Veterinary Integrative Biosciences, Texas A&M University, College Station, TX 77843, USA.
Department of Statistics, Texas A&M University, College Station, TX 77843, USA.
Patterns (N Y). 2020 Nov 5;1(9):100139. doi: 10.1016/j.patter.2020.100139. eCollection 2020 Dec 11.
We present scTenifoldNet-a machine learning workflow built upon principal-component regression, low-rank tensor approximation, and manifold alignment-for constructing and comparing single-cell gene regulatory networks (scGRNs) using data from single-cell RNA sequencing. scTenifoldNet reveals regulatory changes in gene expression between samples by comparing the constructed scGRNs. With real data, scTenifoldNet identifies specific gene expression programs associated with different biological processes, providing critical insights into the underlying mechanism of regulatory networks governing cellular transcriptional activities.
我们展示了scTenifoldNet——一种基于主成分回归、低秩张量逼近和流形对齐构建的机器学习工作流程，用于使用单细胞RNA测序数据构建和比较单细胞基因调控网络（scGRN）。scTenifoldNet通过比较构建的scGRN揭示样本间基因表达的调控变化。利用真实数据，scTenifoldNet识别与不同生物学过程相关的特定基因表达程序，为调控细胞转录活动的网络潜在机制提供关键见解。
