Package: scTenifoldKnk 2.1.0

scTenifoldKnk: In-Silico Knockout Experiments from Single-Cell Gene Regulatory Networks

A workflow based on 'scTenifoldNet' to perform in-silico knockout experiments using single-cell RNA sequencing (scRNA-seq) data from wild-type (WT) control samples as input. First, the package constructs a single-cell gene regulatory network (scGRN) and knocks out a target gene from the adjacency matrix of the WT scGRN by setting the gene's outdegree edges to zero. Then, it compares the knocked out scGRN with the WT scGRN to identify differentially regulated genes, called virtual-knockout perturbed genes, which are used to assess the impact of the gene knockout and reveal the gene's function in the analyzed cells. This version includes C++ acceleration with Eigen library, cross-platform compatibility (macOS, Linux, Windows), and Seurat v4/v5 support.

Authors:Zaoqu Liu [ctb, cre], Daniel Osorio [aut], Yan Zhong [aut, ctb], Guanxun Li [aut, ctb], Qian Xu [aut, ctb], Andrew Hillhouse [aut, ctb], Jingshu Chen [aut, ctb], Laurie Davidson [aut, ctb], Yanan Tian [aut, ctb], Robert Chapkin [aut, ctb], Jianhua Huang [aut, ctb], James Cai [aut, ctb, ths]

scTenifoldKnk_2.1.0.tar.gz
scTenifoldKnk_2.1.0.zip(r-4.7)scTenifoldKnk_2.1.0.zip(r-4.6)scTenifoldKnk_2.1.0.zip(r-4.5)
scTenifoldKnk_2.1.0.tgz(r-4.6-x86_64)scTenifoldKnk_2.1.0.tgz(r-4.6-arm64)scTenifoldKnk_2.1.0.tgz(r-4.5-x86_64)scTenifoldKnk_2.1.0.tgz(r-4.5-arm64)
scTenifoldKnk_2.1.0.tar.gz(r-4.7-arm64)scTenifoldKnk_2.1.0.tar.gz(r-4.7-x86_64)scTenifoldKnk_2.1.0.tar.gz(r-4.6-arm64)scTenifoldKnk_2.1.0.tar.gz(r-4.6-x86_64)
scTenifoldKnk_2.1.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
scTenifoldKnk/json (API)

# Install 'scTenifoldKnk' in R:
install.packages('scTenifoldKnk', repos = c('https://zaoqu-liu.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/zaoqu-liu/sctenifoldknk/issues

Pkgdown/docs site:https://zaoqu-liu.github.io

Uses libs:
  • c++– GNU Standard C++ Library v3

On CRAN:

Conda:

cpp

4.88 score 3 stars 45 scripts 2.2k downloads 10 exports 6 dependencies

Last updated from:08118337f6. Checks:11 WARNING, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-devel-arm64WARNING204
linux-devel-x86_64WARNING186
source / vignettesOK323
linux-release-arm64WARNING157
linux-release-x86_64WARNING177
macos-release-arm64WARNING124
macos-release-x86_64WARNING313
macos-oldrel-arm64WARNING171
macos-oldrel-x86_64WARNING442
windows-develWARNING170
windows-releaseWARNING179
windows-oldrelWARNING166
wasm-releaseOK147

Exports:cpDecompositiondRegulationmakeNetworksFastmanifoldAlignmentpcNetFastscQCscTenifoldKnkstrictDirectionstrictDirectionCpptensorDecomposition

Dependencies:latticeMASSMatrixRcppRcppEigenRSpectra

Advanced Usage and Parameter Tuning
Introduction | Parameter Reference | Main Function Parameters | Using Individual Functions | 1. Quality Control | 2. Network Construction | 3. Multiple Networks | 4. Tensor Decomposition | 5. Manifold Alignment | 6. Differential Regulation | Parameter Tuning Guidelines | Network Construction Parameters | Quantile Threshold Effect | Tensor Rank Selection | Comparing Multiple Knockouts | Performance Considerations | Memory Usage | Parallel Processing | Best Practices | Recommended Workflow | Troubleshooting | Session Info
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Algorithm Theory and Mathematical Foundation
Overview | Step 1: Quality Control | Mathematical Formulation | Step 2: Network Construction (Principal Component Regression) | Algorithm | Mathematical Derivation | Step 3: Tensor Decomposition (CP-ALS) | CANDECOMP/PARAFAC Decomposition | Alternating Least Squares (ALS) Algorithm | Reconstruction | Step 4: Virtual Knockout | Implementation | Step 5: Manifold Alignment | Non-linear Manifold Alignment (NLMA) | Spectral Embedding | Step 6: Differential Regulation Analysis | Distance-Based Statistics | Fold Change | Statistical Testing | Multiple Testing Correction | Summary | References | Session Info
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Quick Start Guide
Introduction | Key Features | Installation | Quick Example | Load Package and Data | Run Virtual Knockout Analysis | View Results | Visualize Results | Output Structure | Differential Regulation Table | Next Steps | Session Info
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Result Interpretation Guide
Introduction | Run Analysis | Output Structure | Understanding the Differential Regulation Table | Column Descriptions | Key Metrics Explained | Distance | Fold Change (FC) | Statistical Significance | Interpreting the Gene Regulatory Networks | Network Comparison | Knockout Effect on Network | Biological Interpretation Guidelines | Categories of Affected Genes | Potential Interpretations | Quality Assessment | Check Knockout Gene Rank | Network Sparsity Check | Significance Distribution | Exporting Results | Summary | Session Info
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Visualization Guide
Introduction | Run Analysis | 1. Volcano Plot | 2. Distance Distribution Plot | 3. Ranking Plot | 4. Network Heatmap | 5. Manifold Alignment Plot | 6. P-value Distribution | 7. Summary Statistics Table | Session Info
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Last update: 2026-01-23
Started: 2026-01-23