spacexr
Bioc currentSpatialeXpressionR: Cell Type Identification in Spatial Transcriptomics
Release Lineage
Entered 3.21 · Apr 16, 2025
Current · Requires R 4.6
Description
Spatial-eXpression-R (spacexr) is a package for analyzing cell types in spatial transcriptomics data. This implementation is a fork of the spacexr GitHub repo (https://github.com/dmcable/spacexr), adapted to work with Bioconductor objects. The original package implements two statistical methods: RCTD for learning cell types and CSIDE for inferring cell type-specific differential expression. Currently, this fork only implements RCTD, which learns cell type profiles from annotated RNA sequencing (RNA-seq) reference data and uses these profiles to identify cell types in spatial transcriptomic pixels while accounting for platform-specific effects. Future releases will include an implementation of CSIDE.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
84 10 exported
Complexity
3.2 avg / 17 max
Call network
84 nodes / 101 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
5,840
Files
80
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
10
Internal functions
74
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.10
testthat edition
3
CI present
No
CI type
[]
PR gated
No
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
0%
Unsafe pattern score
0
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.5.0
System requirements
–
C++ standard
–
License
GPL (>= 3)
License flags
SPDX valid, OSI approved
History
Versions
3
First release
2025-04-15
Latest release
2026-04-28
Avg cadence
189 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
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
Topics
Depended on by (1)
Bioconductor (1)
People
- Gabriel Grajeda maintainer
- Fannie and John Hertz Foundation fnd
- Dylan Cable author
- Rafael Irizarry author