cytomapper
Bioc currentVisualization of highly multiplexed imaging data in R
Release Lineage
Entered 3.11 · Apr 28, 2020
Current · Requires R 4.6
Description
Highly multiplexed imaging acquires the single-cell expression of selected proteins in a spatially-resolved fashion. These measurements can be visualised across multiple length-scales. First, pixel-level intensities represent the spatial distributions of feature expression with highest resolution. Second, after segmentation, expression values or cell-level metadata (e.g. cell-type information) can be visualised on segmented cell areas. This package contains functions for the visualisation of multiplexed read-outs and cell-level information obtained by multiplexed imaging technologies. The main functions of this package allow 1. the visualisation of pixel-level information across multiple channels, 2. the display of cell-level information (expression and/or metadata) on segmentation masks and 3. gating and visualisation of single cells.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
70 8 exported
Complexity
9.5 avg / 49 max
Call network
70 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
59,620
Files
553
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
15
Internal functions
62
Testing & CI
Has tests
Yes
Test-to-code ratio
7.90
testthat edition
–
CI present
Yes
CI type
["github-actions"]
PR gated
Yes
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
100%
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
–
C++ standard
–
License
GPL (>= 2)
License flags
SPDX valid, OSI approved
History
Versions
13
First release
2020-04-27
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
13
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
- 90%
- References docs
- 29%
Topics
Depended on by (7)
Bioconductor (7)
People
- Lasse Meyer maintainer contributor
- Nicolas Damond author
- Nils Eling author
- Tobias Hoch contributor
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("cytomapper")This is what citation() produces when a package has no citation file of its own. If it prints something else, use that.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-08-22, which the citation names so these numbers can be found later. More on citing and the projects behind them.