ArticleNucleic acids research2022
RUV-III-NB: normalization of single cell RNA-seq data.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- Canon enables causal inference of downstream genes in single-cell CRISPR studies via instrumental variable analysis.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Recurrent intra-tumour heterogeneity is a hallmark of metastatic prostate cancer.Nature communications · 2026Article
- Perivascular adipose single-cell atlas identifies CD55Nature communications · 2026Article
- CMDdemux: an efficient single cell demultiplexing method.Nucleic acids research · 2026Article
- Uncovering causal relationships in single-cell omic studies with causarray.Briefings in bioinformatics · 2026Article
- Removal of unwanted variation in pseudobulk analysis of single-cell RNA sequencing data and the leveraging of pseudoreplicates.NAR genomics and bioinformatics · 2025Article
- Conceptualizing Experimental Controls Using the Potential Outcomes Framework.The American statistician · 2025Article
- Simultaneous inference for generalized linear models with unmeasured confounders.Journal of the American Statistical Association · 2025Article
- SpaNorm: spatially-aware normalization for spatial transcriptomics data.Genome biology · 2025Article
- Assessing and mitigating batch effects in large-scale omics studies.Genome biology · 2024Review
- Library size confounds biology in spatial transcriptomics data.Genome biology · 2024Article
- Atlas-scale single-cell multi-sample multi-condition data integration using scMerge2.Nature communications · 2023Article
- Assessing and removing the effect of unwanted technical variations in microbiome data.Scientific reports · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Normalization of single cell RNA-seq data remains a challenging task. The performance of different methods can vary greatly between datasets when unwanted factors and biology are associated. Most normalization methods also only remove the effects of unwanted variation for the cell embedding but not from gene-level data typically used for differential expression (DE) analysis to identify marker genes. We propose RUV-III-NB, a method that can be used to remove unwanted variation from both the cell embedding and gene-level counts. Using pseudo-replicates, RUV-III-NB explicitly takes into account potential association with biology when removing unwanted variation. The method can be used for both UMI or read counts and returns adjusted counts that can be used for downstream analyses such as clustering, DE and pseudotime analyses. Using published datasets with different technological platforms, kinds of biology and levels of association between biology and unwanted variation, we show that RUV-III-NB manages to remove library size and batch effects, strengthen biological signals, improve DE analyses, and lead to results exhibiting greater concordance with independent datasets of the same kind. The performance of RUV-III-NB is consistent and is not sensitive to the number of factors assumed to contribute to the unwanted variation.
Indexed as
Identifiers
What OpenQuestion holds
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.