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scDDboost

Bioc current

A compositional model to assess expression changes from single-cell rna-seq data

v1.14.0 · software · GPL (>= 2)

Release Lineage

Entered 3.16 · Nov 2, 2022

Current · Requires R 4.6

1.0 In 8 of 49 releases 3.23

Description

scDDboost is an R package to analyze changes in the distribution of single-cell expression data between two experimental conditions. Compared to other methods that assess differential expression, scDDboost benefits uniquely from information conveyed by the clustering of cells into cellular subtypes. Through a novel empirical Bayesian formulation it calculates gene-specific posterior probabilities that the marginal expression distribution is the same (or different) between the two conditions. The implementation in scDDboost treats gene-level expression data within each condition as a mixture of negative binomial distributions.

Test coverage

Line coverage

Expression

Tests / Examples

Functions

50 7 exported

Complexity

2.2 avg / 13 max

Call network

50 nodes / 31 edges

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

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Lowest coverage

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

Code

Structure

Lines of code

2,576

Files

62

Compiled share

41.7%

Has compiled src

Yes

Language breakdown

R 689 (26.7%)C/C++/src 1,074 (41.7%)Tests 25 (1%)Docs 589 (22.9%)Vignettes 199 (7.7%)

API

Exported functions

7

Internal functions

15

Testing & CI

Has tests

Yes

Test-to-code ratio

0.04

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

16.7%

Secret pattern count

0

Bundled 3rd-party code

2 items

Portability & License

Min R version

4.2

System requirements

1

C++ standard

C++14

License

GPL (>= 2)

License flags

SPDX valid, OSI approved

History

Versions

8

First release

2022-11-01

Latest release

2026-04-28

Avg cadence

182 days

Cold removal rate

Dep drift

0

LOC over versions

v3.16: 2,644 LOCv3.17: 2,644 LOCv3.18: 2,644 LOCv3.19: 2,644 LOCv3.20: 2,644 LOCv3.21: 2,576 LOCv3.22: 2,576 LOCv3.23: 2,576 LOC

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

Documentation

Documentation
READMEYes · 147 wordsVignettesYes · dynamicpkgdown siteNoNEWSYes · 67% structuredCode of conductNoContributing guideNo
Examples that run
100%
Documented parameters
100%
Return-value docs
100%
References docs
4%

Topics

People

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("scDDboost")
Ma, X., & Newton, M. A. (2026). scDDboost: A compositional model to assess expression changes from single-cell rna-seq data (Version 1.14.0) [Computer software]. https://bioconductor.org/packages/scDDboost

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.

APA

Balamuta, J. J. (2026). R Observatory: Metrics for scDDboost version 1.14.0 [Data set]. HJJB, LLC. Data release v2026-08-22. https://doi.org/10.5281/zenodo.21843040

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.

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