SVP
Bioc currentPredicting cell states and their variability in single-cell or spatial omics data
Release Lineage
Entered 3.21 · Apr 16, 2025
Current · Requires R 4.6
Description
SVP uses the distance between cells and cells, features and features, cells and features in the space of MCA to build nearest neighbor graph, then uses random walk with restart algorithm to calculate the activity score of gene sets (such as cell marker genes, kegg pathway, go ontology, gene modules, transcription factor or miRNA target sets, reactome pathway, ...), which is then further weighted using the hypergeometric test results from the original expression matrix. To detect the spatially or single cell variable gene sets or (other features) and the spatial colocalization between the features accurately, SVP provides some global and local spatial autocorrelation method to identify the spatial variable features. SVP is developed based on SingleCellExperiment class, which can be interoperable with the existing computing ecosystem.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
280 5 exported
Complexity
3.1 avg / 20 max
Call network
280 nodes / 214 edges
Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.
Call graph
Open call graph →Lowest coverage
Per-function coverage is not measured for this package yet.
Code
Structure
Lines of code
13,989
Files
106
Compiled share
20.3%
Has compiled src
Yes
Language breakdown
API
Exported functions
47
Internal functions
143
Recent export changes
Testing
Has tests
Yes
Test-to-code ratio
0.05
testthat edition
3
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
0
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.0
System requirements
1 externalGNU make
C++ standard
–
License
GPL-3
License flags
SPDX valid, OSI approved
History
Versions
3
First release
2025-06-15
Latest release
2026-08-18
Avg cadence
215 days
Cold removal rate
–
Dep drift
0
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 34 (2 internal)includes a package overview page
- Examples that run
- 100%32 run in a plain check
- Exported functions with examples
- 96%
- Documented parameters
- 100%
- Return-value docs
- 100%
- Help pages citing references
- 24%
- SVP Vignette HTML
Repository practices
6 development-tooling and community-health practices detected across 4 families in the upstream repository
Checks run against github.com/yulab-smu/svp on 2026-10-21.
The R CMD check workflow runs on Linux, macOS and Windows, and on R-devel
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| CancerSEAEnsemble | list | 14 elements | No other package |
| CancerSEASymbol | list | 14 elements | No other package |
| CellCycle.Hs | list | 3 elements | No other package |
| SenMayoSymbol | list | 1 element | No other package |
| hpda_spe_cell_dec | – | – | – |
| mob_marker_genes | list | 5 elements | No other package |
| mob_sce | – | – | – |
| sceSubPbmc | – | – | – |
Topics
People
- Shuangbin Xu author maintainer
- Guangchuang Yu author contributor
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("SVP")This package has no citation file, so citation() returns this entry built from DESCRIPTION.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.