ppcseq
Bioc currentProbabilistic Outlier Identification for RNA Sequencing Generalized Linear Models
Release Lineage
Entered 3.13 · May 20, 2021
Current · Requires R 4.6
Description
Relative transcript abundance has proven to be a valuable tool for understanding the function of genes in biological systems. For the differential analysis of transcript abundance using RNA sequencing data, the negative binomial model is by far the most frequently adopted. However, common methods that are based on a negative binomial model are not robust to extreme outliers, which we found to be abundant in public datasets. So far, no rigorous and probabilistic methods for detection of outliers have been developed for RNA sequencing data, leaving the identification mostly to visual inspection. Recent advances in Bayesian computation allow large-scale comparison of observed data against its theoretical distribution given in a statistical model. Here we propose ppcseq, a key quality-control tool for identifying transcripts that include outlier data points in differential expression analysis, which do not follow a negative binomial distribution. Applying ppcseq to analyse several publicly available datasets using popular tools, we show that from 3 to 10 percent of differentially abundant transcripts across algorithms and datasets had statistics inflated by the presence of outliers.
Test coverage
Line coverage
–
Expression
–
Tests / Examples
–
Functions
40 2 exported
Complexity
1.7 avg / 12 max
Call network
40 nodes / 58 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
2,750
Files
43
Compiled share
0.9%
Has compiled src
Yes
Language breakdown
API
Exported functions
2
Internal functions
37
Testing & CI
Has tests
Yes
Test-to-code ratio
0.03
testthat edition
3
CI present
Yes
CI type
["github-actions","travis"]
PR gated
Yes
Docs
Roxygen coverage
100%
Health & Security signals
Informational signals; not verdicts.
on.exit coverage
–
Unsafe pattern score
2
Dep constraint coverage
23.8%
Secret pattern count
0
Bundled 3rd-party code
2 items
Portability & License
Min R version
4.1.0
System requirements
1
C++ standard
C++14
License
GPL-3
License flags
SPDX valid, OSI approved
History
Versions
11
First release
2021-05-19
Latest release
2026-04-28
Avg cadence
182 days
Cold removal rate
–
Dep drift
3
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
- 92%
- Return-value docs
- 100%
- References docs
- 25%
Topics
People
- Stefano Mangiola author maintainer
Cite
Cite this package
Run in R for the authors' preferred citation:
citation("ppcseq")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.
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