ArticleGenome biology2023
Modeling group heteroscedasticity in single-cell RNA-seq pseudo-bulk data.
Article in Genome biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 13 papers.
What it found
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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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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
13 citing papers in PubMed.
- Relationships between gene expression variability, expression levels, and Protein-Protein interactions in mouse and yeast.PloS one · 2026Article
- An integrated SOX4-driven transcriptional and metabolic program governs tumor-infiltrating regulatory T cells in ovarian cancer.Theranostics · 2026Article
- Article
- UNICORN: Towards universal cellular expression prediction with a multi-task learning framework.Nature communications · 2025Article
- Article
- Mitochondria-related gene-based molecular subtypes of lung adenocarcinoma and their prognostic implications.Scientific reports · 2025Article
- cytoKernel: robust kernel embeddings for assessing differential expression of single-cell data.Bioinformatics (Oxford, England) · 2025Article
- APNet, an explainable sparse deep learning model to discover differentially active drivers of severe COVID-19.Bioinformatics (Oxford, England) · 2025Article
- Estimating the effect of tissue- and blood-derived cell reference matrices on deconvolving bulk transcriptomic datasets.Computational and structural biotechnology journal · 2025Article
- Powerful and accurate detection of temporal gene expression patterns from multi-sample multi-stage single-cell transcriptomics data with TDEseq.Genome biology · 2024Article
- Challenges and opportunities to computationally deconvolve heterogeneous tissue with varying cell sizes using single-cell RNA-sequencing datasets.Genome biology · 2023Review
- Publisher Correction: Modeling group heteroscedasticity in single-cell RNA-seq pseudo-bulk data.Genome biology · 2023Article
- Article
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Group heteroscedasticity is commonly observed in pseudo-bulk single-cell RNA-seq datasets and its presence can hamper the detection of differentially expressed genes. Since most bulk RNA-seq methods assume equal group variances, we introduce two new approaches that account for heteroscedastic groups, namely voomByGroup and voomWithQualityWeights using a blocked design (voomQWB). Compared to current gold-standard methods that do not account for group heteroscedasticity, we show results from simulations and various experiments that demonstrate the superior performance of voomByGroup and voomQWB in terms of error control and power when group variances in pseudo-bulk single-cell RNA-seq data are unequal.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.