ArticleBioinformatics (Oxford, England)2018
Two-phase differential expression analysis for single cell RNA-seq.
Article in Bioinformatics (Oxford, England), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed.
- Evaluation of statistical differential analysis methods for identification of senescent cells using single-cell transcriptomics.Cell reports methods · 2026Article
- iDESC: identifying differential expression in single-cell RNA sequencing data with multiple subjects.BMC bioinformatics · 2023Article
- Differential expression of single-cell RNA-seq data using Tweedie models.Statistics in medicine · 2022Article
- Differential Expression Analysis of Single-Cell RNA-Seq Data: Current Statistical Approaches and Outstanding Challenges.Entropy (Basel, Switzerland) · 2022Review
- A high-efficiency differential expression method for cancer heterogeneity using large-scale single-cell RNA-sequencing data.Frontiers in genetics · 2022Article
- Article
- Mixed Distribution Models Based on Single-Cell RNA Sequencing Data.Interdisciplinary sciences, computational life sciences · 2021Article
- Network Modeling in Biology: Statistical Methods for Gene and Brain Networks.Statistical science : a review journal of the Institute of Mathematical Statistics · 2021Article
- Non-linear Normalization for Non-UMI Single Cell RNA-Seq.Frontiers in genetics · 2021Article
- Simulation, power evaluation and sample size recommendation for single-cell RNA-seq.Bioinformatics (Oxford, England) · 2020Article
- A hierarchical Bayesian mixture model for inferring the expression state of genes in transcriptomes.Proceedings of the National Academy of Sciences of the United States of America · 2020Article
- M3S: a comprehensive model selection for multi-modal single-cell RNA sequencing data.BMC bioinformatics · 2019Article
- DECENT: differential expression with capture efficiency adjustmeNT for single-cell RNA-seq data.Bioinformatics (Oxford, England) · 2019Article
- LTMG: a novel statistical modeling of transcriptional expression states in single-cell RNA-Seq data.Nucleic acids research · 2019Article
- Cell type-specific transcriptional programs in mouse prefrontal cortex during adolescence and addiction.Nature communications · 2019Article
- lncDIFF: a novel quasi-likelihood method for differential expression analysis of non-coding RNA.BMC genomics · 2019Article
- Article
- Circulating Tumor Cells as a Tool for Assessing Tumor Heterogeneity.Theranostics · 2019Review
- Single-Cell RNA Sequencing-Based Computational Analysis to Describe Disease Heterogeneity.Frontiers in genetics · 2019Article
- SAREV: A review on statistical analytics of single-cell RNA sequencing data.Wiley interdisciplinary reviews. Computational statisticsArticle
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.
Funding
Abstract
Motivation: Single-cell RNA-sequencing (scRNA-seq) has brought the study of the transcriptome to higher resolution and makes it possible for scientists to provide answers with more clarity to the question of 'differential expression'. However, most computational methods still stick with the old mentality of viewing differential expression as a simple 'up or down' phenomenon. We advocate that we should fully embrace the features of single cell data, which allows us to observe binary (from Off to On) as well as continuous (the amount of expression) regulations. Results: We develop a method, termed SC2P, that first identifies the phase of expression a gene is in, by taking into account of both cell- and gene-specific contexts, in a model-based and data-driven fashion. We then identify two forms of transcription regulation: phase transition, and magnitude tuning. We demonstrate that compared with existing methods, SC2P provides substantial improvement in sensitivity without sacrificing the control of false discovery, as well as better robustness. Furthermore, the analysis provides better interpretation of the nature of regulation types in different genes. Availability and implementation: SC2P is implemented as an open source R package publicly available at https://github.com/haowulab/SC2P. Supplementary information: Supplementary data are available at Bioinformatics online.
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.