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omicsViewer

Bioc current

Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer

v1.16.0 · Package record · software · GPL-2

Release Lineage

Entered 3.15 · Apr 27, 2022

Current · Requires R 4.6

1.0 In 9 of 49 releases 3.23

Description

omicsViewer visualizes ExpressionSet (or SummarizedExperiment) in an interactive way. The omicsViewer has a separate back- and front-end. In the back-end, users need to prepare an ExpressionSet that contains all the necessary information for the downstream data interpretation. Some extra requirements on the headers of phenotype data or feature data are imposed so that the provided information can be clearly recognized by the front-end, at the same time, keep a minimum modification on the existing ExpressionSet object. The pure dependency on R/Bioconductor guarantees maximum flexibility in the statistical analysis in the back-end. Once the ExpressionSet is prepared, it can be visualized using the front-end, implemented by shiny and plotly. Both features and samples could be selected from (data) tables or graphs (scatter plot/heatmap). Different types of analyses, such as enrichment analysis (using Bioconductor package fgsea or fisher's exact test) and STRING network analysis, will be performed on the fly and the results are visualized simultaneously. When a subset of samples and a phenotype variable is selected, a significance test on means (t-test or ranked based test; when phenotype variable is quantitative) or test of independence (chi-square or fisher’s exact test; when phenotype data is categorical) will be performed to test the association between the phenotype of interest with the selected samples. Additionally, other analyses can be easily added as extra shiny modules. Therefore, omicsViewer will greatly facilitate data exploration, many different hypotheses can be explored in a short time without the need for knowledge of R. In addition, the resulting data could be easily shared using a shiny server. Otherwise, a standalone version of omicsViewer together with designated omics data could be easily created by integrating it with portable R, which can be shared with collaborators or submitted as supplementary data together with a manuscript.

Advisories

None published for Bioconductor

Test coverage

Line coverage

–

Expression

–

Tests / Examples

–

Functions

147 36 exported

Complexity

7 avg / 76 max

Call network

147 nodes / 178 edges

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

Loading call graph…

Lowest coverage

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

Code

Structure

Lines of code

17,353

Files

160

Compiled share

0%

Has compiled src

No

Language breakdown

R 12,181 (70.2%)Tests 625 (3.6%)Docs 3,249 (18.7%)Vignettes 1,298 (7.5%)

API

Exported functions

38

Internal functions

111

Recent export changes

v3.23+6 validate_character_vector, validate_dataframe, validate_matching_dimensions +3 more
v3.22+2 L1_data_space_module, L1_result_space_module

Testing

Has tests

Yes

Test-to-code ratio

0.05

testthat edition

–

Docs

Roxygen coverage

100%

Health & Security signals

Informational signals; not verdicts.

on.exit coverage

42.9%

Unsafe pattern score

3

Dep constraint coverage

0%

Secret pattern count

0

Bundled 3rd-party code

2 items

Portability & License

Min R version

4.2

System requirements

–

C++ standard

–

License

GPL-2

License flags

SPDX valid, OSI approved

History

Versions

9

First release

2022-05-09

Latest release

2026-04-28

Avg cadence

179 days

Cold removal rate

–

Dep drift

4

LOC over versions

v3.15: 11,876 LOCv3.16: 11,878 LOCv3.17: 12,173 LOCv3.18: 12,175 LOCv3.19: 13,063 LOCv3.20: 13,141 LOCv3.21: 13,159 LOCv3.22: 13,815 LOCv3.23: 17,353 LOC

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

Documentation

In the Bioconductor 3.23 release
READMEYes · 635 wordsVignettesYes · 1 · dynamicNEWSYes · 100% structuredCitation fileNo
Help pages
84 (20 internal)
Examples that run
91%32 run in a plain check (10 of them under a condition) · 1 never run · 2 hold no code
Exported functions with examples
79%
Documented parameters
93%
Return-value docs
92%
Help pages citing references
2%
Vignettes (1)

Repository practices

Upstream repositoryBeta

2 development-tooling and community-health practices detected across 2 families in the upstream repository

Checks run against github.com/mengchen18/omicsviewer on 2026-10-11.

Continuous integration (1)
GitHub Actions
Docs source (1)

GitHub Pages site last deployed 2026-10-09

How this is detected·Detection ruleset v3 (2026-09-26)

Datasets

Bundled datasets · 6
NameClassSizeAlso in
demo–––
expressionMatrixdata.frame2,702 × 47No other package
featureGeneraldata.frame2,702 × 3No other package
sampleDrugdata.frame47 × 245No other package
sampleGeneraldata.frame47 × 6No other package
sampleSurvdata.frame47 × 2No other package

All of omicsViewer's data objects

Topics

Maintainer
Chen Meng

Available on

CRANr2uBioconductorautoCRANCOPR

People

Cite

Cite this package

Run in R for the authors' preferred citation:

citation("omicsViewer")
Meng, C. (2026). omicsViewer: Interactive and explorative visualization of SummarizedExperssionSet or ExpressionSet using omicsViewer (Version 1.16.0) [Computer software]. https://bioconductor.org/packages/omicsViewer

This package has no citation file, so citation() returns this entry built from DESCRIPTION.

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 omicsViewer version 1.16.0 [Data set]. HJJB, LLC. Data release v2026-10-19. https://doi.org/10.5281/zenodo.21843040

From data release v2026-10-19, which the citation names so these numbers can be found later. More on citing and the projects behind them.

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