期刊
PATTERNS
卷 1, 期 9, 页码 -出版社
CELL PRESS
DOI: 10.1016/j.patter.2020.100139
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资金
- Texas AM University
- 2020 Award of Texas A&M Institute of Data Science (TAMIDS) Data Resource Development Program
We present scTenifoldNet-a machine learning workflow built upon principal-component regression, lowrank 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.
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