recipe bioconductor-ppcseq

Probabilistic Outlier Identification for RNA Sequencing Generalized Linear Models

Homepage:

https://bioconductor.org/packages/3.18/bioc/html/ppcseq.html

License:

GPL-3

Recipe:

/bioconductor-ppcseq/meta.yaml

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.

package bioconductor-ppcseq

versions:

1.10.0-0,  1.8.0-0,  1.6.0-1,  1.6.0-0,  1.2.0-2,  1.2.0-1,  1.2.0-0,  1.0.0-0

depends bioconductor-edger:

>=4.0.0,<4.1.0

depends bioconductor-edger:

>=4.0.2,<4.1.0a0

depends libblas:

>=3.9.0,<4.0a0

depends libgcc-ng:

>=12

depends liblapack:

>=3.9.0,<4.0a0

depends libstdcxx-ng:

>=12

depends r-base:

>=4.3,<4.4.0a0

depends r-benchmarkme:

depends r-bh:

>=1.66.0

depends r-dplyr:

depends r-foreach:

depends r-ggplot2:

depends r-lifecycle:

depends r-magrittr:

depends r-purrr:

depends r-rcpp:

>=0.12.0

depends r-rcppeigen:

>=0.3.3.3.0

depends r-rcppparallel:

>=5.0.1

depends r-rlang:

depends r-rstan:

>=2.18.1

depends r-rstantools:

>=2.1.1

depends r-stanheaders:

>=2.18.0

depends r-tibble:

depends r-tidybayes:

depends r-tidyr:

>=0.8.3.9000

requirements:

Installation

You need a conda-compatible package manager (currently either micromamba, mamba, or conda) and the Bioconda channel already activated (see set-up-channels).

While any of above package managers is fine, it is currently recommended to use either micromamba or mamba (see here for installation instructions). We will show all commands using mamba below, but the arguments are the same for the two others.

Given that you already have a conda environment in which you want to have this package, install with:

   mamba install bioconductor-ppcseq

and update with::

   mamba update bioconductor-ppcseq

To create a new environment, run:

mamba create --name myenvname bioconductor-ppcseq

with myenvname being a reasonable name for the environment (see e.g. the mamba docs for details and further options).

Alternatively, use the docker container:

   docker pull quay.io/biocontainers/bioconductor-ppcseq:<tag>

(see `bioconductor-ppcseq/tags`_ for valid values for ``<tag>``)

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