Package: SCENT 2.0.0

SCENT: Single Cell Entropy for Estimating Differentiation Potency

Estimates differentiation potency of single cells from scRNA-Seq data using signaling entropy on protein interaction networks. Implements both the full Signaling Entropy Rate (SR) and the fast CCAT approximation. Based on the method described in Teschendorff AE, Enver T (2017) <doi:10.1038/ncomms15599>.

Authors:Andrew E Teschendorff [aut], Zaoqu Liu [aut, cre]

SCENT_2.0.0.tar.gz
SCENT_2.0.0.zip(r-4.7-x86_64)SCENT_2.0.0.zip(r-4.7-arm64)SCENT_2.0.0.zip(r-4.6-x86_64)SCENT_2.0.0.zip(r-4.6-arm64)SCENT_2.0.0.zip(r-4.5-x86_64)
SCENT_2.0.0.tgz(r-4.6-x86_64)SCENT_2.0.0.tgz(r-4.6-arm64)SCENT_2.0.0.tgz(r-4.5-x86_64)SCENT_2.0.0.tgz(r-4.5-arm64)
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SCENT_2.0.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
SCENT/json (API)

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

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

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

Uses libs:
  • openblas– Optimized BLAS
  • c++– GNU Standard C++ Library v3
Datasets:

On CRAN:

Conda:

openblascpp

4.37 score 117 scripts 3 exports 13 dependencies

Last updated from:8a3a80953b (on main). Checks:15 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64OK162
linux-devel-x86_64OK214
source / vignettesOK342
linux-release-arm64OK166
linux-release-x86_64OK206
macos-release-arm64OK111
macos-release-x86_64OK240
macos-oldrel-arm64OK176
macos-oldrel-x86_64OK225
windows-devel-arm64OK252
windows-devel-x86_64OK202
windows-release-arm64OK364
windows-release-x86_64OK159
windows-oldrel-x86_64OK145
wasm-releaseOK161

Exports:CompCCATCompSRanaDoIntegPPI

Dependencies:clicpp11glueigraphlatticelifecyclemagrittrMatrixpkgconfigRcppRcppArmadillorlangvctrs

Algorithm and Mathematical Background
Biological Motivation | Mathematical Framework | Signaling Entropy Rate (SR) | Step 1: Transition Probabilities | Step 2: Stationary Distribution | Step 3: Local Entropy | Step 4: Global Entropy Rate | Visual Demonstration | Network Structure | Entropy Computation Example | CCAT: Fast Approximation | Why SR and CCAT Correlate | References | Session Info
Rendered fromalgorithm.Rmdusingknitr::rmarkdown

Last update: 2026-01-23
Started: 2026-01-23

Performance Benchmark
Introduction | Performance Comparison | Small Dataset (50 cells) | Medium Dataset (200 cells) | Performance Summary | Scaling Analysis | Extrapolated Performance | Recommendations | Dataset Size Guidelines | Workflow for Large Datasets | Memory Usage | Session Info
Rendered frombenchmark.Rmdusingknitr::rmarkdown

Last update: 2026-01-23
Started: 2026-01-23

Quick Start Guide
Introduction | Installation | Quick Example | Simulate Single-Cell Data | Method 1: CCAT (Fast) | Method 2: SR (Accurate) | Compare Methods | Interpretation | When to Use Each Method | Session Info
Rendered fromquick-start.Rmdusingknitr::rmarkdown

Last update: 2026-01-23
Started: 2026-01-23

Visualization Guide
Introduction | 1. Distribution Plots | Box Plot with Individual Points | Violin Plot | 2. Scatter Plots | SR vs CCAT Correlation | 3. Density Plots | Overlapping Densities | Ridge Plot Style | 4. Statistical Comparison | Significance Annotation | 5. Local Entropy Heatmap | 6. Summary Statistics Table | Publication Tips | Session Info
Rendered fromvisualization.Rmdusingknitr::rmarkdown

Last update: 2026-01-23
Started: 2026-01-23