ArticleNucleic acids research2017
Differential expression analysis for RNAseq using Poisson mixed models.
Article in Nucleic acids research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 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
50 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
- Admixture influences the genetic architecture of DNA methylation in a wild primate hybrid zone.Genome biology · 2026Article
- A Silk-Derived Dual Peptide System Suppresses Skin Photoaging by Inhibiting PDGFRβ-Mediated Cellular Senescence and TRPV4-Mediated Melanogenesis.Research (Washington, D.C.) · 2026Article
- SMOPCA: spatially aware dimension reduction integrating multi-omics improves the efficiency of spatial domain detection.Genome biology · 2025Article
- Assessing etiological heterogeneity for multinomial outcome with two-phase outcome-dependent sampling design.American journal of epidemiology · 2025Article
- Polygenic prediction for underrepresented populations through transfer learning by utilizing genetic similarity shared with European populations.Briefings in bioinformatics · 2024Article
- Temperature-Dependent tRNA Modifications in Bacillales.International journal of molecular sciences · 2024Article
- A review of model evaluation metrics for machine learning in genetics and genomics.Frontiers in bioinformatics · 2024Review
- Differential gene expression analysis based on linear mixed model corrects false positive inflation for studying quantitative traits.Scientific reports · 2023Article
- SRTsim: spatial pattern preserving simulations for spatially resolved transcriptomics.Genome biology · 2023Article
- A multi-view latent variable model reveals cellular heterogeneity in complex tissues for paired multimodal single-cell data.Bioinformatics (Oxford, England) · 2023Article
- Spatially aware dimension reduction for spatial transcriptomics.Nature communications · 2022Article
- RNAseq Analysis of Brain Aging in Wild Specimens of Short-Lived Turquoise Killifish: Commonalities and Differences With Aging Under Laboratory Conditions.Molecular biology and evolution · 2022Article
- Spatially informed cell-type deconvolution for spatial transcriptomics.Nature biotechnology · 2022Article
- A successful hybrid deep learning model aiming at promoter identification.BMC bioinformatics · 2022Article
- Likelihood-based Mendelian randomization analysis with automated instrument selection and horizontal pleiotropic modeling.Science advances · 2022Article
- Effective and scalable single-cell data alignment with non-linear canonical correlation analysis.Nucleic acids research · 2022Article
- SpatialMap: Spatial Mapping of Unmeasured Gene Expression Profiles in Spatial Transcriptomic Data Using Generalized Linear Spatial Models.Frontiers in genetics · 2022Article
- Genetic prediction of complex traits with polygenic scores: a statistical review.Trends in genetics : TIG · 2021Review
- Small RNA expression and miRNA modification dynamics in human oocytes and early embryos.Genome research · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
Abstract
Identifying differentially expressed (DE) genes from RNA sequencing (RNAseq) studies is among the most common analyses in genomics. However, RNAseq DE analysis presents several statistical and computational challenges, including over-dispersed read counts and, in some settings, sample non-independence. Previous count-based methods rely on simple hierarchical Poisson models (e.g. negative binomial) to model independent over-dispersion, but do not account for sample non-independence due to relatedness, population structure and/or hidden confounders. Here, we present a Poisson mixed model with two random effects terms that account for both independent over-dispersion and sample non-independence. We also develop a scalable sampling-based inference algorithm using a latent variable representation of the Poisson distribution. With simulations, we show that our method properly controls for type I error and is generally more powerful than other widely used approaches, except in small samples (n <15) with other unfavorable properties (e.g. small effect sizes). We also apply our method to three real datasets that contain related individuals, population stratification or hidden confounders. Our results show that our method increases power in all three data compared to other approaches, though the power gain is smallest in the smallest sample (n = 6). Our method is implemented in MACAU, freely available at www.xzlab.org/software.html.
Indexed as
Identifiers
What Socratic 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 Socratic graph.