Package: RNAseqCovarImpute
Title: Impute Covariate Data in RNA Sequencing Studies
Version: 1.9.0
Authors@R: c(
    person("Brennan", "Baker", email = "brennanhilton@gmail.com", role = c("aut", "cre"),
           comment = c(ORCID = "0000-0001-5459-9141")),
    person("Sheela", "Sathyanarayana", role = "aut"),
    person("Adam", "Szpiro", role = "aut"),
    person("James", "MacDonald", role = "aut"),
    person("Alison", "Paquette", role = "aut"))    
URL: https://github.com/brennanhilton/RNAseqCovarImpute
BugReports: https://github.com/brennanhilton/RNAseqCovarImpute/issues
Description: 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).
License: GPL-3
Encoding: UTF-8
Roxygen: list(markdown = TRUE)
RoxygenNote: 7.2.3
VignetteBuilder: knitr
biocViews: RNASeq, GeneExpression, DifferentialExpression, Sequencing
Suggests: BiocStyle, knitr, PCAtools, rmarkdown, tidyr, stringr,
        testthat (>= 3.0.0)
Depends: R (>= 4.3.0)
Imports: Biobase, BiocGenerics, BiocParallel, stats, limma, dplyr,
        magrittr, rlang, edgeR, foreach, mice
Config/testthat/edition: 3
Collate: 'RNAseqCovarImpute-package.R' 'combine_rubins.R'
        'combine_rubins_pca.R' 'example_DGE.R' 'example_data.R'
        'get_gene_bin_intervals.R' 'impute_by_gene_bin_helper.R'
        'impute_by_gene_bin.R' 'voom_sx_sy.R' 'lowess_all_gene_bins.R'
        'voom_master_lowess.R' 'limmavoom_imputed_data_list_helper.R'
        'limmavoom_imputed_data_list.R'
        'limmavoom_imputed_data_pca_helper.R'
        'limmavoom_imputed_data_pca.R'
Config/pak/sysreqs: cmake make libicu-dev libx11-dev zlib1g-dev
Repository: https://bioc.r-universe.dev
Date/Publication: 2025-10-29 15:25:57 UTC
RemoteUrl: https://github.com/bioc/RNAseqCovarImpute
RemoteRef: HEAD
RemoteSha: c21a2433dbe5730aa743f8e82ecc1d252642d751
NeedsCompilation: no
Packaged: 2025-11-02 04:09:41 UTC; root
Author: Brennan Baker [aut, cre] (ORCID:
    <https://orcid.org/0000-0001-5459-9141>),
  Sheela Sathyanarayana [aut],
  Adam Szpiro [aut],
  James MacDonald [aut],
  Alison Paquette [aut]
Maintainer: Brennan Baker <brennanhilton@gmail.com>
Built: R 4.6.0; ; 2025-11-02 04:12:05 UTC; windows
