Package: ppcseq
Title: Probabilistic Outlier Identification for RNA Sequencing
        Generalized Linear Models
Version: 1.6.0
Authors@R: 
    person(given = "Stefano",
           family = "Mangiola",
           role = c("aut", "cre"),
           email = "mangiolastefano@gmail.com",
           comment = c(ORCID = "0000-0001-7474-836X"))
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.
License: GPL-3
Encoding: UTF-8
LazyData: true
Biarch: true
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
VignetteBuilder: knitr
RdMacros: lifecycle
biocViews: RNASeq, DifferentialExpression, GeneExpression,
        Normalization, Clustering, QualityControl, Sequencing,
        Transcription, Transcriptomics
SystemRequirements: GNU make
RoxygenNote: 7.1.1
Roxygen: list(markdown = TRUE)
BugReports: https://github.com/stemangiola/ppcseq/issues
Config/testthat/edition: 3
git_url: https://git.bioconductor.org/packages/ppcseq
git_branch: RELEASE_3_16
git_last_commit: 366ea46
git_last_commit_date: 2022-11-01
Date/Publication: 2022-11-07
NeedsCompilation: yes
Packaged: 2022-11-09 08:13:04 UTC; biocbuild
Author: Stefano Mangiola [aut, cre] (<https://orcid.org/0000-0001-7474-836X>)
Maintainer: Stefano Mangiola <mangiolastefano@gmail.com>
Built: R 4.2.1; aarch64-apple-darwin20; 2022-11-10 05:18:26 UTC; unix
