ArticlePLoS computational biology2022
Transcriptome diversity is a systematic source of variation in RNA-sequencing data.
Article in PLoS computational biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- The entropic view of aging: from thermodynamics to biology.Life medicine · 2026Article
- α-Diversity analysis of hepatic transcriptome reveals distinct pathways in alcohol-associated hepatitis.JCI insight · 2026Article
- Integrated transcriptomics-metabolomics analysis reveals biomarkers and metabolic dysregulation characteristics of parenteral nutrition-associated liver disease.Frontiers in nutrition · 2026Article
- SPRY1 regulates macrophage M1 polarization in skin aging and melanoma prognosis.Translational oncology · 2025Article
- Interaction between cecal microbiota and liver genes of laying ducks with different residual feed intake.Animal microbiome · 2025Article
- Latent spaces for tumour transcriptomes.Nature biomedical engineering · 2025Article
- First neurotranscriptome of adults Tambaquis (Colossoma macropomum) with characterization and differential expression between males and females.Scientific reports · 2024Article
- Protist predation promotes antimicrobial resistance spread through antagonistic microbiome interactions.The ISME journal · 2024Article
- Climate change induces shifts in coastal Baltic Sea surface water microorganism stress and photosynthesis gene expression.Frontiers in microbiology · 2024Article
- Profiling of repetitive RNA sequences in the blood plasma of patients with cancer.Nature biomedical engineering · 2023Article
- Single-cell and long-read sequencing to enhance modelling of splicing and cell-fate determination.Computational and structural biotechnology journal · 2023Review
- PCA outperforms popular hidden variable inference methods for molecular QTL mapping.Genome biology · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
RNA sequencing has been widely used as an essential tool to probe gene expression. While standard practices have been established to analyze RNA-seq data, it is still challenging to interpret and remove artifactual signals. Several biological and technical factors such as sex, age, batches, and sequencing technology have been found to bias these estimates. Probabilistic estimation of expression residuals (PEER), which infers broad variance components in gene expression measurements, has been used to account for some systematic effects, but it has remained challenging to interpret these PEER factors. Here we show that transcriptome diversity-a simple metric based on Shannon entropy-explains a large portion of variability in gene expression and is the strongest known factor encoded in PEER factors. We then show that transcriptome diversity has significant associations with multiple technical and biological variables across diverse organisms and datasets. In sum, transcriptome diversity provides a simple explanation for a major source of variation in both gene expression estimates and PEER covariates.
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Registered trials
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