TCC
Bioc currentTCC: Differential expression analysis for tag count data with robust normalization strategies
Release Lineage
Entered 2.13 · Oct 15, 2013
Current · Requires R 4.6
Description
This package provides a series of functions for performing differential expression analysis from RNA-seq count data using robust normalization strategy (called DEGES). The basic idea of DEGES is that potential differentially expressed genes or transcripts (DEGs) among compared samples should be removed before data normalization to obtain a well-ranked gene list where true DEGs are top-ranked and non-DEGs are bottom ranked. This can be done by performing a multi-step normalization strategy (called DEGES for DEG elimination strategy). A major characteristic of TCC is to provide the robust normalization methods for several kinds of count data (two-group with or without replicates, multi-group/multi-factor, and so on) by virtue of the use of combinations of functions in depended packages.
None published for Bioconductor
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
19 13 exported
Complexity
5.8 avg / 24 max
Call network
19 nodes / 3 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
5,953
Files
58
Compiled share
0%
Has compiled src
No
Language breakdown
API
Exported functions
18
Internal functions
31
Testing
Has tests
Yes
Test-to-code ratio
0.00
testthat edition
–
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
0%
Unsafe pattern score
6
Dep constraint coverage
0%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
3.0
System requirements
–
C++ standard
–
License
GPL-2
License flags
SPDX valid, OSI approved
History
Versions
26
First release
2013-10-14
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
100%
Dep drift
5
LOC over versions
Per-file churn detail lives in the source pipeline: https://github.com/r-observatory/bioc-code-metrics.
Documentation
- Help pages
- 20includes a package overview page
- Examples that run
- 100%20 run in a plain check
- Exported functions with examples
- 100%
- Documented parameters
- 97%
- Return-value docs
- 80%
- Help pages citing references
- 25%
- TCC PDF
Repository practices
Checks read the Bioconductor git repository (git.bioconductor.org), branch RELEASE_3_23.
No development-tooling practices detected in the Bioconductor git repository.
Datasets
| Name | Class | Size | Also in |
|---|---|---|---|
| arab | matrix | 26,222 × 6 | 3 packages |
| hypoData | matrix | 1,000 × 6 | 2 packages |
| hypoData_mg | matrix | 1,000 × 9 | 2 packages |
| hypoData_ts | matrix | 8 × 10 | No other package |
| nakai | matrix | 31,099 × 8 | No other package |
Topics
Suggested by (1)
People (from the free-text Author field)
Cite
Cite this package
Run in R for the authors' entry:
citation("TCC")The package provides a citation file, so citation() returns the authors' own entry.
Cite the R Observatory
For a number measured here: a download total, a coverage figure, an archival date.
From data release v2026-10-25, which the citation names so these numbers can be found later. More on citing and the projects behind them.