ArticleGenome biology2024
RNAseqCovarImpute: a multiple imputation procedure that outperforms complete case and single imputation differential expression analysis.
Article in Genome biology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
3 citing papers in PubMed.
- A review of multi-omics integration techniques across five machine learning method families.Bioinformatics advances · 2026Review
- Associations of maternal blood biomarkers of prenatal APAP exposure with placental gene expression and child attention deficit hyperactivity disorder.Nature. Mental health · 2025Article
- Placental transcriptomic signatures of prenatal and preconceptional maternal stress.Molecular psychiatry · 2024Article
Corrections and comments
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Authors and funding
5 authors.
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Abstract
Missing covariate data is a common problem that has not been addressed in observational studies of gene expression. Here, we present a multiple imputation method that accommodates high dimensional gene expression data by incorporating principal component analysis of the transcriptome into the multiple imputation prediction models to avoid bias. Simulation studies using three datasets show that this method outperforms complete case and single imputation analyses at uncovering true positive differentially expressed genes, limiting false discovery rates, and minimizing bias. This method is easily implemented via an R Bioconductor package, RNAseqCovarImpute that integrates with the limma-voom pipeline for differential expression analysis.
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Registered trials
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