ArticleStatistics in medicine2022
Differential expression of single-cell RNA-seq data using Tweedie models.
Article in Statistics in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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.
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Who cites it
19 citing papers in PubMed.
- Impact of Fecal Microbiota Transplantation on Gut Bacterial Bile Acid Metabolism in Humans.Nutrients · 2022Trial
- Meta-analytic microbiome target discovery for immune checkpoint inhibitor response in advanced melanoma.Communications medicine · 2026Article
- PalmaClust: A graph-fusion framework leveraging the Palma ratio for robust ultra-rare cell type detection in scRNA-seq data.bioRxiv : the preprint server for biology · 2026Article
- scCOSMIX: A Mixed-Effects Framework for Differential Coexpression and Transcriptional Interactions Modeling in Single-Cell RNA-Seq.Statistics in medicine · 2025Article
- Fibroblast-Mediated Macrophage Recruitment Supports Acute Wound Healing.The Journal of investigative dermatology · 2025Article
- Analysis of microbiome high-dimensional experimental design data using generalized linear models and ANOVA simultaneous component analysis.Frontiers in microbiomes · 2025Article
- Deep skin fibroblast-mediated macrophage recruitment supports acute wound healing.bioRxiv : the preprint server for biology · 2024Article
- eSVD-DE: cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.BMC bioinformatics · 2024Article
- eSVD-DE: Cohort-wide differential expression in single-cell RNA-seq data using exponential-family embeddings.bioRxiv : the preprint server for biology · 2024Article
- Hospital antimicrobial stewardship: profiling the oral microbiome after exposure to COVID-19 and antibiotics.Frontiers in microbiology · 2024Article
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- Article
- Construction and validation of a prognostic signature based on necroptosis-related genes in hepatocellular carcinoma.PloS one · 2023Article
- Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges.Entropy (Basel, Switzerland) · 2022Review
- Metabolite, protein, and tissue dysfunction associated with COVID-19 disease severity.Scientific reports · 2022Article
- Editorial: Methods for Single-Cell and Microbiome Sequencing Data.Frontiers in genetics · 2022Article
- Multivariable association discovery in population-scale meta-omics studies.PLoS computational biology · 2021Article
- tascCODA: Bayesian Tree-Aggregated Analysis of Compositional Amplicon and Single-Cell Data.Frontiers in genetics · 2021Article
- Dietary resistant starch supplementation increases gut luminal deoxycholic acid abundance in mice.Gut microbesArticle
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
Abstract
The performance of computational methods and software to identify differentially expressed features in single-cell RNA-sequencing (scRNA-seq) has been shown to be influenced by several factors, including the choice of the normalization method used and the choice of the experimental platform (or library preparation protocol) to profile gene expression in individual cells. Currently, it is up to the practitioner to choose the most appropriate differential expression (DE) method out of over 100 DE tools available to date, each relying on their own assumptions to model scRNA-seq expression features. To model the technological variability in cross-platform scRNA-seq data, here we propose to use Tweedie generalized linear models that can flexibly capture a large dynamic range of observed scRNA-seq expression profiles across experimental platforms induced by platform- and gene-specific statistical properties such as heavy tails, sparsity, and gene expression distributions. We also propose a zero-inflated Tweedie model that allows zero probability mass to exceed a traditional Tweedie distribution to model zero-inflated scRNA-seq data with excessive zero counts. Using both synthetic and published plate- and droplet-based scRNA-seq datasets, we perform a systematic benchmark evaluation of more than 10 representative DE methods and demonstrate that our method (Tweedieverse) outperforms the state-of-the-art DE approaches across experimental platforms in terms of statistical power and false discovery rate control. Our open-source software (R/Bioconductor package) is available at https://github.com/himelmallick/Tweedieverse.
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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.