recipe bioconductor-rnaseqcovarimpute

Impute Covariate Data in RNA Sequencing Studies

Homepage:

https://bioconductor.org/packages/3.20/bioc/html/RNAseqCovarImpute.html

License:

GPL-3

Recipe:

/bioconductor-rnaseqcovarimpute/meta.yaml

The RNAseqCovarImpute package makes linear model analysis for RNA sequencing read counts compatible with multiple imputation (MI) of missing covariates. A major problem with implementing MI in RNA sequencing studies is that the outcome data must be included in the imputation prediction models to avoid bias. This is difficult in omics studies with high-dimensional data. The first method we developed in the RNAseqCovarImpute package surmounts the problem of high-dimensional outcome data by binning genes into smaller groups to analyze pseudo-independently. This method implements covariate MI in gene expression studies by 1) randomly binning genes into smaller groups, 2) creating M imputed datasets separately within each bin, where the imputation predictor matrix includes all covariates and the log counts per million (CPM) for the genes within each bin, 3) estimating gene expression changes using `limma::voom` followed by `limma::lmFit` functions, separately on each M imputed dataset within each gene bin, 4) un-binning the gene sets and stacking the M sets of model results before applying the `limma::squeezeVar` function to apply a variance shrinking Bayesian procedure to each M set of model results, 5) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 6) adjusting P-values for multiplicity to account for false discovery rate (FDR). A faster method uses principal component analysis (PCA) to avoid binning genes while still retaining outcome information in the MI models. Binning genes into smaller groups requires that the MI and limma-voom analysis is run many times (typically hundreds). The more computationally efficient MI PCA method implements covariate MI in gene expression studies by 1) performing PCA on the log CPM values for all genes using the Bioconductor `PCAtools` package, 2) creating M imputed datasets where the imputation predictor matrix includes all covariates and the optimum number of PCs to retain (e.g., based on Horn’s parallel analysis or the number of PCs that account for >80% explained variation), 3) conducting the standard limma-voom pipeline with the `voom` followed by `lmFit` followed by `eBayes` functions on each M imputed dataset, 4) pooling the results with Rubins’ rules to produce combined coefficients, standard errors, and P-values, and 5) adjusting P-values for multiplicity to account for false discovery rate (FDR).

package bioconductor-rnaseqcovarimpute

versions:

1.4.0-0,  1.0.2-0

depends bioconductor-biobase:

>=2.66.0,<2.67.0

depends bioconductor-biocgenerics:

>=0.52.0,<0.53.0

depends bioconductor-biocparallel:

>=1.40.0,<1.41.0

depends bioconductor-edger:

>=4.4.0,<4.5.0

depends bioconductor-limma:

>=3.62.0,<3.63.0

depends r-base:

>=4.4,<4.5.0a0

depends r-dplyr:

depends r-foreach:

depends r-magrittr:

depends r-mice:

depends r-rlang:

requirements:

additional platforms:

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-rnaseqcovarimpute

and update with::

   mamba update bioconductor-rnaseqcovarimpute

To create a new environment, run:

mamba create --name myenvname bioconductor-rnaseqcovarimpute

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-rnaseqcovarimpute:<tag>

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

Download stats

Link to this page

Render an badge with the following MarkDown:

[![install with bioconda](https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat)](http://bioconda.github.io/recipes/bioconductor-rnaseqcovarimpute/README.html)