ArticleNature communications2020
Latent periodic process inference from single-cell RNA-seq data.
Article in Nature communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 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
29 citing papers in PubMed.
- OscillomeR infers ultradian oscillations and targets of the Hes family.Patterns (New York, N.Y.) · 2026Article
- Uncovering smooth structures in single-cell data with neighbor embeddings guided by predictability-computability-stability.Patterns (New York, N.Y.) · 2026Article
- Mathematical and Computational Models of Biochemical Reactions and Cell Signaling-From Ordinary Differential Equations to Machine Learning.International journal of molecular sciences · 2026Review
- Deep learning models for cell cycle phase prediction from single-cell RNA sequencing data.Briefings in bioinformatics · 2026Article
- CycleVI: isolating cell cycle variation with an interpretable deep generative model.Bioinformatics (Oxford, England) · 2026Article
- Transcriptional noise sets fundamental limits to decoding circadian clock phase from single-cell RNA snapshots.iScience · 2026Article
- Deciphering Cell Cycle Dynamics and Cell States in Single-cell RNA-seq data with SPAE.bioRxiv : the preprint server for biology · 2026Article
- CellUntangler: Separating distinct biological signals in single-cell data with deep generative models.Cell genomics · 2026Article
- BAYESIAN RHYTHMIC MODEL FOR JOINTLY DETECTING CIRCADIAN BIOMARKERS AND PREDICTING MOLECULAR CIRCADIAN TIME IN HUMAN POST-MORTEM BRAIN TRANSCRIPTOME.bioRxiv : the preprint server for biology · 2026Article
- DCPR: a deep learning framework for circadian phase reconstruction.BMC bioinformatics · 2025Article
- Article
- Mitigating Cell Cycle Effects in Multi-Omics Data: Solutions and Analytical Frameworks.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Rhythm profiling using COFE reveals multi-omic circadian rhythms in human cancers in vivo.PLoS biology · 2025Article
- A Mesenchymal stem cell Aging Framework, from Mechanisms to Strategies.Stem cell reviews and reports · 2024Review
- A Cell Cycle-Aware Network for Data Integration and Label Transferring of Single-Cell RNA-Seq and ATAC-Seq.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2024Article
- SiFT: uncovering hidden biological processes by probabilistic filtering of single-cell data.Nature communications · 2024Article
- From G1 to M: a comparative study of methods for identifying cell cycle phases.Briefings in bioinformatics · 2024Review
- scFseCluster: a feature selection-enhanced clustering for single-cell RNA-seq data.Life science alliance · 2023Article
- scPrisma infers, filters and enhances topological signals in single-cell data using spectral template matching.Nature biotechnology · 2023Article
- Article
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
The development of a phenotype in a multicellular organism often involves multiple, simultaneously occurring biological processes. Advances in single-cell RNA-sequencing make it possible to infer latent developmental processes from the transcriptomic profiles of cells at various developmental stages. Accurate characterization is challenging however, particularly for periodic processes such as cell cycle. To address this, we develop Cyclum, an autoencoder approach identifying circular trajectories in the gene expression space. Cyclum substantially improves the accuracy and robustness of cell-cycle characterization beyond existing approaches. Applying Cyclum to removing cell-cycle effects substantially improves delineations of cell subpopulations, which is useful for establishing various cell atlases and studying tumor heterogeneity.
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.