ReviewComputational and structural biotechnology journal2020
Naught all zeros in sequence count data are the same.
Review in Computational and structural biotechnology journal, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 93 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
93 citing papers in PubMed, 186 citations in OpenAlex.
- Integrating zero-inflation correction and transcriptional kinetics for single-cell transcriptomic analysis.PLoS computational biology · 2026Article
- PLNFGL: joint estimation of multi-condition gene networks from single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2026Article
- Evaluating microbial network inference methods: moving beyond synthetic data with reproducibility-driven benchmarks.BMC bioinformatics · 2026Article
- Differential expression analysis in single-cell and spatial RNA-seq without model assumptions.Cell reports methods · 2026Article
- A Tree-Based Model for Addressing Sparsity and Taxa Covariance in Microbiome Compositional Count Data.Statistics in medicine · 2026Article
- Reliable enteric methane prediction from the cattle (Bos taurus) rumen microbiome.Communications biology · 2026Article
- Scale reliant mixed effects models enhance microbiome data analysis.Microbiome · 2026Article
- A negative binomial latent factor model for paired microbiome sequencing data.BMC bioinformatics · 2026Article
- NBSR: a Negative Binomial Softmax Regression model for microRNA-seq data analysis.Biostatistics (Oxford, England) · 2026Article
- Salivary microbiota signatures of periodontitis are associated with CGM-derived short-term glycaemic control in adults with type 1 diabetes: a pilot study.Frontiers in endocrinology · 2026Article
- Differential expression analysis in single cell and spatial RNASeq without model assumptions.bioRxiv : the preprint server for biology · 2025Article
- National Football League (NFL) "Dynamic Kickoff" Rule Change: A Comparative Analysis of Concussion and Musculoskeletal Injury Incidence.Orthopaedic journal of sports medicine · 2025Article
- D3Impute: Dropout-aware discrimination, distribution-aware modeling, and density-guide imputation for scRNA-seq data.PLoS computational biology · 2025Article
- scFPC-DE: Robust Differential Expression Analysis Along Single Cell Trajectories via Functional Principal Component Analysis.bioRxiv : the preprint server for biology · 2025Article
- GIMIC: smoothed graph-image representation of microbiome samples induce an optimal distance.Genome biology · 2025Article
- A New Dirichlet-Multinomial Mixture Regression Model for the Analysis of Microbiome Data.Statistics in medicine · 2025Article
- Replacing normalizations with interval assumptions enhances differential expression and differential abundance analyses.BMC bioinformatics · 2025Article
- parafac4microbiome: exploratory analysis of longitudinal microbiome data using parallel factor analysis.mSystems · 2025Article
- TphPMF: A microbiome data imputation method using hierarchical Bayesian Probabilistic Matrix Factorization.PLoS computational biology · 2025Article
- ISCAZIM: Integrated statistical correlation analysis for zero-inflated microbiome data.Heliyon · 2025Article
33 more citing papers are in PubMed but not listed here.
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 at 2 institutions in 1 country.
Funding
Abstract
Genomic studies feature multivariate count data from high-throughput DNA sequencing experiments, which often contain many zero values. These zeros can cause artifacts for statistical analyses and multiple modeling approaches have been developed in response. Here, we apply different zero-handling models to gene-expression and microbiome datasets and show models can disagree substantially in terms of identifying the most differentially expressed sequences. Next, to rationally examine how different zero handling models behave, we developed a conceptual framework outlining four types of processes that may give rise to zero values in sequence count data. Last, we performed simulations to test how zero handling models behave in the presence of these different zero generating processes. Our simulations showed that simple count models are sufficient across multiple processes, even when the true underlying process is unknown. On the other hand, a common zero handling technique known as "zero-inflation" was only suitable under a zero generating process associated with an unlikely set of biological and experimental conditions. In concert, our work here suggests several specific guidelines for developing and choosing state-of-the-art models for analyzing sparse sequence count data.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.