ArticleNature communications2024
Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq.
Article in Nature communications, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 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
9 citing papers in PubMed.
- Integrating feature selection with unsupervised deep embedding for clustering single-cell RNA-seq data.Briefings in bioinformatics · 2026Article
- Statistically principled feature selection for single cell transcriptomics.BMC bioinformatics · 2025Article
- Article
- Feature selection methods affect the performance of scRNA-seq data integration and querying.Nature methods · 2025Article
- Considerations for building and using integrated single-cell atlases.Nature methods · 2025Review
- Statistically principled feature selection for single cell transcriptomics.bioRxiv : the preprint server for biology · 2024Article
- Leveraging gene correlations in single cell transcriptomic data.BMC bioinformatics · 2024Article
- Anti-correlated feature selection prevents false discovery of subpopulations in scRNAseq.Nature communications · 2024Article
- Leveraging gene correlations in single cell transcriptomic data.bioRxiv : the preprint server for biology · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
While sub-clustering cell-populations has become popular in single cell-omics, negative controls for this process are lacking. Popular feature-selection/clustering algorithms fail the null-dataset problem, allowing erroneous subdivisions of homogenous clusters until nearly each cell is called its own cluster. Using real and synthetic datasets, we find that anti-correlated gene selection reduces or eliminates erroneous subdivisions, increases marker-gene selection efficacy, and efficiently scales to millions of cells.
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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.