ppcseq

DOI: 10.18129/B9.bioc.ppcseq    

Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models

Bioconductor version: Release (3.13)

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.

Author: Stefano Mangiola [aut, cre]

Maintainer: Stefano Mangiola <mangiolastefano at gmail.com>

Installation

To install this package, start R (version "4.1") and enter:

if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("ppcseq")

For older versions of R, please refer to the appropriate Bioconductor release.

Documentation

To view documentation for the version of this package installed in your system, start R and enter:

browseVignettes("ppcseq")

 

HTML	R Script	Overview of the ppcseq package
PDF		Reference Manual

Details

biocViews	Clustering, DifferentialExpression, GeneExpression, Normalization, QualityControl, RNASeq, Sequencing, Software, Transcription, Transcriptomics
Version	1.0.0
In Bioconductor since	BioC 3.13 (R-4.1) (< 6 months)
License	GPL-3
Depends	R (>= 4.1.0)
Imports	methods, Rcpp (>= 0.12.0), rstan (>= 2.18.1), rstantools (>= 2.0.0), tibble, dplyr, magrittr, purrr, future, furrr, tidyr (>= 0.8.3.9000), lifecycle, ggplot2, foreach, tidybayes, edgeR, benchmarkme, parallel, rlang, stats, utils, graphics
LinkingTo	BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), rstan (>= 2.18.1), StanHeaders (>= 2.18.0)
Suggests	knitr, testthat, BiocStyle, rmarkdown
SystemRequirements	GNU make
Enhances
URL
BugReports	https://github.com/stemangiola/ppcseq/issues
Depends On Me
Imports Me
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Build Report

Package Archives

Follow Installation instructions to use this package in your R session.

Source Package	ppcseq_1.0.0.tar.gz
Windows Binary	ppcseq_1.0.0.zip
macOS 10.13 (High Sierra)	ppcseq_1.0.0.tgz
Source Repository	git clone https://git.bioconductor.org/packages/ppcseq
Source Repository (Developer Access)	git clone git@git.bioconductor.org:packages/ppcseq
Package Short Url	https://bioconductor.org/packages/ppcseq/
Package Downloads Report	Download Stats