ppcseq

DOI: 10.18129/B9.bioc.ppcseq    

This is the development version of ppcseq; for the stable release version, see ppcseq.

Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models

Bioconductor version: Development (3.15)

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>

Citation (from within R, enter citation("ppcseq")):

Installation

To install this package, start R (version "4.2") and enter:

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

# The following initializes usage of Bioc devel
BiocManager::install(version='devel')

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 1.3.0
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
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Package Archives

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

Source Package ppcseq_1.3.0.tar.gz
Windows Binary
macOS 10.13 (High Sierra)
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/
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