PCAtools
Bioc currentPCAtools: Everything Principal Components Analysis
Release Lineage
Entered 3.9 · May 3, 2019
Current · Requires R 4.6
Description
Principal Component Analysis (PCA) is a very powerful technique that has wide applicability in data science, bioinformatics, and further afield. It was initially developed to analyse large volumes of data in order to tease out the differences/relationships between the logical entities being analysed. It extracts the fundamental structure of the data without the need to build any model to represent it. This 'summary' of the data is arrived at through a process of reduction that can transform the large number of variables into a lesser number that are uncorrelated (i.e. the 'principal components'), while at the same time being capable of easy interpretation on the original data. PCAtools provides functions for data exploration via PCA, and allows the user to generate publication-ready figures. PCA is performed via BiocSingular - users can also identify optimal number of principal components via different metrics, such as elbow method and Horn's parallel analysis, which has relevance for data reduction in single-cell RNA-seq (scRNA-seq) and high dimensional mass cytometry data.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
19 13 exported
Complexity
10.6 avg / 65 max
Call network
19 nodes / 10 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
5,911
Files
106
Compiled share
1.9%
Has compiled src
Yes
Language breakdown
API
Exported functions
13
Internal functions
3
Recent export changes
Testing & CI
Has tests
Yes
Test-to-code ratio
0.11
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
6.3%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
–
System requirements
1
C++ standard
–
License
GPL-3
License flags
SPDX valid, OSI approved
History
Versions
15
First release
2019-05-02
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
11
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
- 96%
- Return-value docs
- 100%
- References docs
- 0%
Topics
Depended on by (5)
Bioconductor (5)
People
- Jared Andrews author maintainer
- Kevin Blighe author
- Anna-Leigh Brown contributor
- Vincent Carey contributor
- Guido Hooiveld contributor
- Aaron Lun author contributor
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("PCAtools")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 v2026-08-23, which the citation names so these numbers can be found later. More on citing and the projects behind them.