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DESCRIPTION
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Package: ppcseq
Title: Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models
Version: 1.9.1
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),
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
LinkingTo:
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)
Suggests:
knitr,
testthat,
BiocStyle,
rmarkdown
VignetteBuilder:
knitr
RdMacros:
lifecycle
biocViews: RNASeq, DifferentialExpression, GeneExpression, Normalization, Clustering, QualityControl, Sequencing, Transcription, Transcriptomics
SystemRequirements: GNU make
RoxygenNote: 7.2.3
Roxygen: list(markdown = TRUE)
URL: https://github.com/stemangiola/ppcseq
BugReports: https://github.com/stemangiola/ppcseq/issues
Config/testthat/edition: 3