scTenifoldNet
Construct and Compare scGRN from Single-Cell Transcriptomic Data
A workflow based on machine learning methods to construct and compare single-cell gene regulatory networks (scGRN) using single-cell RNA-seq (scRNA-seq) data collected from different conditions. Uses principal component regression, tensor decomposition, and manifold alignment, to accurately identify even subtly shifted gene expression programs. See <doi:10.1016/j.patter.2020.100139> for more details.
Versions across snapshots
| Version | Repository | File | Size |
|---|---|---|---|
1.3 |
rolling linux/jammy R-4.5 | scTenifoldNet_1.3.tar.gz |
80.9 KiB |
1.3 |
rolling linux/noble R-4.5 | scTenifoldNet_1.3.tar.gz |
80.7 KiB |
1.3 |
rolling source/ R- | scTenifoldNet_1.3.tar.gz |
33.6 KiB |
1.3 |
latest linux/jammy R-4.5 | scTenifoldNet_1.3.tar.gz |
80.9 KiB |
1.3 |
latest linux/noble R-4.5 | scTenifoldNet_1.3.tar.gz |
80.7 KiB |
1.3 |
latest source/ R- | scTenifoldNet_1.3.tar.gz |
33.6 KiB |
1.3 |
2026-04-26 source/ R- | scTenifoldNet_1.3.tar.gz |
33.6 KiB |
1.3 |
2026-04-23 source/ R- | scTenifoldNet_1.3.tar.gz |
33.6 KiB |
1.3 |
2026-04-09 windows/windows R-4.5 | scTenifoldNet_1.3.zip |
83.7 KiB |
1.3 |
2025-04-20 source/ R- | scTenifoldNet_1.3.tar.gz |
33.6 KiB |