ppcseq
This is the released version of ppcseq; for the devel version, see ppcseq.
Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models
Bioconductor version: Release (3.23)
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("ppcseq")):Stefano Mangiola. ppcseq: Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models. doi:10.18129/B9.bioc.ppcseq, R package version 1.20.0, https://bioconductor.org/packages/ppcseq.
Generated from the package metadata; it may differ from the package's own citation.
Installation
To install this package, start R (version "4.6") and enter:
if (!require("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")| Overview of the ppcseq package | HTML | R Script |
| Reference Manual |
Details
| biocViews | Clustering, DifferentialExpression, GeneExpression, Normalization, QualityControl, RNASeq, Sequencing, Software, Transcription, Transcriptomics |
| Version | 1.20.0 |
| In Bioconductor since | BioC 3.13 (R-4.1) (5.5 years) |
| License | GPL-3 |
| Depends | R (>= 4.1.0), rstan (>= 2.18.1) |
| Imports | benchmarkme, dplyr, edgeR, foreach, ggplot2, graphics, lifecycle, magrittr, methods, parallel, purrr, Rcpp (>= 0.12.0), RcppParallel (>= 5.0.1), rlang, rstantools (>= 2.1.1), stats, tibble, tidybayes, tidyr (>= 0.8.3.9000), utils |
| System Requirements | GNU make |
| URL | https://github.com/stemangiola/ppcseq |
| Bug Reports | https://github.com/stemangiola/ppcseq/issues |
See More
| Suggests | knitr, testthat, BiocStyle, rmarkdown |
| Linking To | BH (>= 1.66.0), Rcpp (>= 0.12.0), RcppEigen (>= 0.3.3.3.0), RcppParallel (>= 5.0.1), rstan (>= 2.18.1), StanHeaders (>= 2.18.0) |
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| Build Report | Build Report, r-universe |
Package Archives
Follow Installation instructions to use this package in your R session.
| Source Package | ppcseq_1.20.0.tar.gz |
| Windows Binary (x86_64) | ppcseq_1.20.0.zip |
| macOS Binary (big-sur-x86_64) | ppcseq_1.20.0.tgz |
| macOS Binary (sonoma-arm64) | ppcseq_1.20.0.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 |