scAnnotatR
Bioc currentPretrained learning models for cell type prediction on single cell RNA-sequencing data
Release Lineage
Entered 3.14 · Oct 27, 2021
Current · Requires R 4.6
Description
The package comprises a set of pretrained machine learning models to predict basic immune cell types. This enables all users to quickly get a first annotation of the cell types present in their dataset without requiring prior knowledge. scAnnotatR also allows users to train their own models to predict new cell types based on specific research needs.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
44 13 exported
Complexity
4.2 avg / 19 max
Call network
44 nodes / 90 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
4,744
Files
38
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
16
Internal functions
24
Testing & CI
Has tests
Yes
Test-to-code ratio
0.05
testthat edition
–
CI present
No
CI type
[]
PR gated
No
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
4.1
System requirements
–
C++ standard
–
License
MIT + file LICENSE
License flags
SPDX valid, OSI approved
History
Versions
10
First release
2021-10-26
Latest release
2026-04-28
Avg cadence
182 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
- Johannes Griss maintainer
- Vy Nguyen author