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This is the development version of RNAseqCovarImpute; for the stable release version, see RNAseqCovarImpute.

Impute Covariate Data in RNA Sequencing Studies

Bioconductor version: Development (3.19)

The RNAseqCovarImpute package implements multiple imputation of missing covariates and differential gene expression analysis 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 voom followed by 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 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).

Author: Brennan Baker [aut, cre] , Sheela Sathyanarayana [aut], Adam Szpiro [aut], James MacDonald [aut], Alison Paquette [aut]

Maintainer: Brennan Baker <brennanhilton at>

Citation (from within R, enter citation("RNAseqCovarImpute")):


To install this package, start R (version "4.4") and enter:

if (!require("BiocManager", quietly = TRUE))

# The following initializes usage of Bioc devel


For older versions of R, please refer to the appropriate Bioconductor release.


To view documentation for the version of this package installed in your system, start R and enter:

Example Data for RNAseqCovarImpute HTML R Script
Impute Covariate Data in RNA-sequencing Studies HTML R Script
Reference Manual PDF


biocViews DifferentialExpression, GeneExpression, RNASeq, Sequencing, Software
Version 1.1.2
In Bioconductor since BioC 3.18 (R-4.3) (< 6 months)
License GPL-3
Depends R (>= 4.3.0)
Imports Biobase, BiocParallel, stats, limma, dplyr, magrittr, rlang, edgeR, foreach, mice
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