SpatialCPie

Bioc current

Cluster analysis of Spatial Transcriptomics data

v1.28.0 · software · MIT + file LICENSE

View on Bioconductor ↗ Source ↗

Release Lineage

Entered 3.9 · May 3, 2019

Current · Requires R 4.6

1.0 In 15 of 49 releases 3.23

Description

SpatialCPie is an R package designed to facilitate cluster evaluation for spatial transcriptomics data by providing intuitive visualizations that display the relationships between clusters in order to guide the user during cluster identification and other downstream applications. The package is built around a shiny "gadget" to allow the exploration of the data with multiple plots in parallel and an interactive UI. The user can easily toggle between different cluster resolutions in order to choose the most appropriate visual cues.

Test coverage

Line coverage

–

Expression

–

Tests / Examples

–

Functions

16 2 exported

Complexity

2.3 avg / 6 max

Call network

16 nodes / 14 edges

Test coverage is not measured for Bioconductor packages; nodes fall back to a neutral fill.

Call graph

Open call graph →

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Lowest coverage

Per-function coverage is not measured for this package yet.

Code

Structure

Lines of code

1,987

Files

Compiled share

Has compiled src

Language breakdown

R 1,074 (54.1%)Tests 88 (4.4%)Docs 462 (23.3%)Vignettes 363 (18.3%)

API

Exported functions

Internal functions

Recent export changes

v3.9+2 parseSpotFile, runCPie

Testing & CI

Has tests

Yes

Test-to-code ratio

0.08

testthat edition

–

CI present

CI type

[]

PR gated

Docs

Roxygen coverage

100%

Health & Security signals

Informational signals; not verdicts.

on.exit coverage

Unsafe pattern score

Dep constraint coverage

100%

Secret pattern count

Bundled 3rd-party code

2 items

Portability & License

Min R version

3.6

System requirements

–

C++ standard

–

License

MIT + file LICENSE

License flags

SPDX valid, OSI approved

History

Versions

First release

2019-05-02

Latest release

2026-04-28

Avg cadence

182 days

Cold removal rate

–

Dep drift

LOC over versions

Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.

Documentation

READMEYes · 70 wordsVignettesYes · dynamicpkgdown siteNoNEWSYes · 100% structuredCode of conductNoContributing guideNo

Examples that run: 100%
Documented parameters: 85%
Return-value docs: 100%
References docs: 0%

Topics

Software › ResearchField › Transcriptomics

Software › StatisticalMethod › Clustering

Software › Technology › Sequencing › RNASeq

Software

People

Joseph Bergenstraahle author maintainer

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