ArticleNAR genomics and bioinformatics2025
Explicit Scale Simulation for analysis of RNA-sequencing count data with ALDEx2.
Article in NAR genomics and bioinformatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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.
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
6 citing papers in PubMed.
- Not every gene is special: Modelling scale controls the false discovery rate when analysing high-throughput sequencing data.PLoS computational biology · 2026Article
- Uterine microbiome signatures associated with endometriosis.BMC biology · 2026Article
- Gut-Microbial Responses to Acute Polyester Microplastic Exposure in Zebrafish: Dysbiosis, Opportunistic Bacteria, and Functional Impact.International journal of molecular sciences · 2026Article
- Comprehensive evaluation of statistical approaches for differential metaproteomics.bioRxiv : the preprint server for biology · 2026Article
- Incorporating Scale Uncertainty into Differential Expression Analyses Using ALDEx2.Current protocols · 2026Article
- Plant reproductive suppression triggers fatty acid-mediated enrichment of Mortierella for enhanced stress resilience.The ISME journal · 2025Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In high-throughput sequencing (HTS) studies, sample-to-sample variation in sequencing depth is driven by technical factors, and not by variation in the scale (size) of the biological system. Typically a statistical normalization removes unwanted technical variation in the data or the parameters of the model to enable differential abundance analyses. We recently showed that all normalizations make implicit assumptions about the unmeasured system scale and that errors in these assumptions can dramatically increase false positive and false negative rates. We demonstrated that these errors can be mitigated by accounting for uncertainty using a
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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.