Package: SecAct 1.0.1

SecAct: Secreted Signaling Activity Inference

Inferring secreted protein activities at bulk, single-cell, and spatial levels. SecAct uses ridge regression with permutation-based significance testing to infer the activity of over 1000 secreted proteins from gene expression profiles.

Authors:Beibei Ru [aut], Zaoqu Liu [cre, ctb]

SecAct_1.0.1.tar.gz
SecAct_1.0.1.zip(r-4.7)SecAct_1.0.1.zip(r-4.6)SecAct_1.0.1.zip(r-4.5)
SecAct_1.0.1.tgz(r-4.6-x86_64)SecAct_1.0.1.tgz(r-4.6-arm64)SecAct_1.0.1.tgz(r-4.5-x86_64)SecAct_1.0.1.tgz(r-4.5-arm64)
SecAct_1.0.1.tar.gz(r-4.7-arm64)SecAct_1.0.1.tar.gz(r-4.7-x86_64)SecAct_1.0.1.tar.gz(r-4.6-arm64)SecAct_1.0.1.tar.gz(r-4.6-x86_64)
SecAct_1.0.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
SecAct/json (API)

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

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

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

Uses libs:
  • gsl– GNU Scientific Library (GSL)

On CRAN:

Conda:

complexheatmapgsl

4.20 score 35 scripts 22 exports 153 dependencies

Last updated from:d65240f867 (on main). Checks:11 NOTE, 2 OK. Indexed: yes.

TargetResultTimeFilesSyslog
linux-devel-arm64NOTE168
linux-devel-x86_64NOTE179
source / vignettesOK453
linux-release-arm64NOTE182
linux-release-x86_64NOTE217
macos-release-arm64NOTE122
macos-release-x86_64NOTE189
macos-oldrel-arm64NOTE98
macos-oldrel-x86_64NOTE200
windows-develNOTE111
windows-releaseNOTE149
windows-oldrelNOTE113
wasm-releaseOK215

Exports:SecAct.activity.inferenceSecAct.activity.inference.scRNAseqSecAct.activity.inference.STSecAct.bar.plotSecAct.CCC.circleSecAct.CCC.dotSecAct.CCC.heatmapSecAct.CCC.sankeySecAct.CCC.scRNAseqSecAct.CCC.scSTSecAct.check.dataSecAct.coxph.regressionSecAct.download.dataSecAct.heatmap.plotSecAct.inference.gslSecAct.inference.rSecAct.lollipop.plotSecAct.signaling.patternSecAct.signaling.pattern.geneSecAct.signaling.velocity.scSTSecAct.signaling.velocity.spotSTSecAct.survival.plot

Dependencies:abindakimabackportsBiobaseBiocGenericsBiocManagerbootbroomcarcarDatacirclizeclassclassIntcliclueclustercodetoolscolorspacecommonmarkComplexHeatmapcorrplotcowplotcpp11crayoncurlDBIDEoptimRDerivdigestdoBydoParalleldplyre1071exactRankTestsfarverforeachforecastFormulafracdiffgenericsGetoptLongggalluvialgganimateggplot2ggpubrggrepelggsciggsignifggtextGlobalOptionsgluegridBasegridExtragridtextgtablehmsIRangesisobanditeratorsjpegKernSmoothlabelinglatticelazyevallifecyclelitedownlme4lmtestlpSolvemagrittrmarkdownMASSmathjaxrMatrixMatrixModelsmatrixStatsmaxstatmetapmgcvmicrobenchmarkminqamnormtmodelrmultcompmulttestmutossmvtnormnlmenloptrNMFnnetnumDerivpbkrtestpillarpkgconfigplotrixplyrpngpolynomprettyunitsprogressproxypurrrqqconfquantregR6RANNrbibutilsRColorBrewerRcppRcppArmadilloRcppEigenRdpackreformulasregistryreshape2rjsonrlangrngtoolsrobustbaserstatixs2S4VectorsS7sandwichscalessfshapesnspSparseMstringistringrsurvivalsurvminerTFisherTH.datatibbletidyrtidyselecttimeDatetransformrtweenrunitsurcautf8vctrsviridisLitewithrwkxfunxml2zoo

SecAct Algorithm: Ridge Regression with Permutation Testing
Overview | Mathematical Framework | Problem Formulation | Ridge Regression Model | Implementation via Cholesky Decomposition | Statistical Significance | Permutation Testing | Z-score Calculation | P-value Computation | Signature Matrix Construction | SecAct Signature Database | Signature Grouping | Algorithm Comparison: R vs GSL | Practical Considerations | Lambda Selection | Number of Permutations | Summary | References | Session Info
Rendered fromalgorithm.Rmdusingknitr::rmarkdown

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

Cell-cell communication for single-cell resolution spatial transcriptomics data
output: github_document | Read ST data to a SpaCET object | Infer secreted protein activity | Infer cell-cell communication | Visualize cell-cell communication | Visualize secreted signaling velocity
Rendered fromstCCC.Rmdusingknitr::rmarkdown

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

Cell-cell communication mediated by secreted proteins from scRNA-Seq data
output: github_document | Prepare expression matrix | Infer cell-cell communication | Visualize cell-cell communication | Visualize secreted signaling flow
Rendered fromscCCC.Rmdusingknitr::rmarkdown

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

Clinical relevance of secreted proteins in a large patient cohort
output: github_document | Prepare expression data | Infer secreted protein activity | Calculate clinical relevance | Visualize risk score | Draw survival plot
Rendered frombulkCohort.Rmdusingknitr::rmarkdown

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

Quick Start Guide
Introduction | Basic Usage | 1. Load Example Data | 2. Infer Secreted Protein Activity | 3. Explore Results | Visualization | Activity Heatmap | Bar Plot | Lollipop Plot | Compare Treatment vs Control | R vs GSL Implementation | Workflow Summary | Next Steps | Getting Help | Session Info
Rendered fromquick-start.Rmdusingknitr::rmarkdown

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

Secreted protein signaling activity change between two phenotypes
output: github_document | Prepare expression data | Infer activity change | Visualize activity change | Run a differential profile
Rendered frombulkChange.Rmdusingknitr::rmarkdown

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

Secreted protein signaling activity for distinct cell states from scRNA-Seq data
output: github_document | Load scRNA-Seq data | Infer secreted protein activity | Visualize activity
Rendered fromscState.Rmdusingknitr::rmarkdown

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

Signaling patterns and velocities for multi-cellular spatial transcriptomics data
output: github_document | Read ST data to a SpaCET object | Infer secreted protein activity | Estimate signaling pattern | Calculate signaling velocity | Deconvolve ST data | Calculate hallmark score
Rendered fromstPattern.Rmdusingknitr::rmarkdown

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

Visualization Gallery
Overview | Activity Heatmaps | Basic Heatmap | Customized Colors | Divergent Scale | Bar Plots | Basic Bar Plot | With Title | Custom Colors | Lollipop Plots | Basic Lollipop | Combining Plots | Side-by-Side Comparison | Publication-Ready Figures | Customized Theme | Statistical Visualization | P-value Distribution | Volcano Plot | Sample Correlation | Tips for Better Visualizations | 1. Choose Appropriate Color Palettes | 2. Consider Your Audience | 3. Always Include Context | Session Info
Rendered fromvisualization.Rmdusingknitr::rmarkdown

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