ArticleGenome research2020
Characterizing and inferring quantitative cell cycle phase in single-cell RNA-seq data analysis.
Article in Genome research, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 63 papers, 1 of them a synthesis that pooled it.
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
63 citing papers in PubMed, 1 synthesis or guideline pooled it, 101 citations in OpenAlex.
- What are the applications of single-cell RNA sequencing in cancer research: a systematic review.Journal of experimental & clinical cancer research : CR · 2021Pooled it
- COBRA: cell-type-specific orthogonal batch effect removal algorithm in single cell RNA-sequencing data.Bioinformatics (Oxford, England) · 2026Article
- Temperature regulation of cell cycle and growth dynamics in Arabidopsis.Biochemical Society transactions · 2026Review
- CALIPERS: Cell cycle-aware live imaging for phenotyping experiments and regeneration studies.Nature communications · 2026Article
- scFANCL: Dual contrastive learning with false-negative correction at cell level for single-cell RNA-seq clustering.BMC genomics · 2026Article
- The STAT3-CCND2 Axis Drives a Proliferative Metaplastic Precursor Population in Gastric Intestinal Metaplasia.Journal of cellular and molecular medicine · 2026Article
- ScRNA-seq Data Reveal Gene Upregulation and Downregulation in Oxygen-Induced Retinopathy.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- CycleVI: isolating cell cycle variation with an interpretable deep generative model.Bioinformatics (Oxford, England) · 2026Article
- Single-cell trajectory inference for detecting transient events in biological processes.Nucleic acids research · 2026Article
- MEIOC prevents continued mitotic cycling and promotes meiotic entry during mouse oogenesis.Development (Cambridge, England) · 2026Article
- Maternal dietary fibre intake results in sex-specific single-cell molecular changes in the heart of the offspring.Clinical science (London, England : 1979) · 2025Article
- MEIOC prevents continued mitotic cycling and promotes meiotic entry during mouse oogenesis.bioRxiv : the preprint server for biology · 2025Article
- Mitigating Cell Cycle Effects in Multi-Omics Data: Solutions and Analytical Frameworks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Cell-surface proteomic profiling identifies CD72 as a regulator of microglial tiling.bioRxiv : the preprint server for biology · 2025Article
- Isogenic hiPSC models of Turner syndrome development reveal shared roles of inactive X and Y in the human cranial neural crest network.American journal of human genetics · 2025Article
- Classifying cell cycle states and a quiescent-like G0 state using single-cell transcriptomics.bioRxiv : the preprint server for biology · 2025Article
- Using pseudotime derivative on single-cell RNA sequencing data to identify genes undergoing cell cycle regulation.Bioinformatics advances · 2025Article
- Transcription factor networks in cellular quiescence.Nature cell biology · 2025Review
- A concise guide to fluorescent cell cycle reporters for live-cell imaging.Frontiers in cell and developmental biology · 2025Review
- Article
3 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
8 authors at 2 institutions in 1 country.
Funding
Abstract
Cellular heterogeneity in gene expression is driven by cellular processes, such as cell cycle and cell-type identity, and cellular environment such as spatial location. The cell cycle, in particular, is thought to be a key driver of cell-to-cell heterogeneity in gene expression, even in otherwise homogeneous cell populations. Recent advances in single-cell RNA-sequencing (scRNA-seq) facilitate detailed characterization of gene expression heterogeneity and can thus shed new light on the processes driving heterogeneity. Here, we combined fluorescence imaging with scRNA-seq to measure cell cycle phase and gene expression levels in human induced pluripotent stem cells (iPSCs). By using these data, we developed a novel approach to characterize cell cycle progression. Although standard methods assign cells to discrete cell cycle stages, our method goes beyond this and quantifies cell cycle progression on a continuum. We found that, on average, scRNA-seq data from only five genes predicted a cell's position on the cell cycle continuum to within 14% of the entire cycle and that using more genes did not improve this accuracy. Our data and predictor of cell cycle phase can directly help future studies to account for cell cycle-related heterogeneity in iPSCs. Our results and methods also provide a foundation for future work to characterize the effects of the cell cycle on expression heterogeneity in other cell types.
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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.