ArticleNucleic acids research2022
MarkovHC: Markov hierarchical clustering for the topological structure of high-dimensional single-cell omics data with transition pathway and critical point detection.
Article in Nucleic acids research, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 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
10 citing papers in PubMed.
- Integrative Analyses Identify a cGAS-STING Pathway-Driven Signature With Context-Dependent Roles in Systemic Lupus Erythematosus.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Genomic adaptability and virulence of Bacillus anthracis: a machine learning-based pan-genome and comparative analysis.BMC genomics · 2026Article
- Comparative benchmarking of single-cell clustering algorithms for transcriptomic and proteomic data.Genome biology · 2025Article
- soFusion: facilitating tissue structure identification via spatial multi-omics data fusion.Briefings in bioinformatics · 2025Article
- HemaScope: A Tool for Analyzing Single-cell and Spatial Transcriptomics Data of Hematopoietic Cells.Genomics, proteomics & bioinformatics · 2025Article
- Single-cell multi-omics deciphers hepatocyte dedifferentiation and illuminates maintenance strategies.Cell proliferation · 2025Article
- Topological data analysis in single cell biology.Frontiers in immunology · 2025Review
- mFusion: a multiscale fusion method bridging neuroimages to genes through neurotransmissions in mental health disorders.Communications biology · 2024Article
- An increment of diversity method for cell state trajectory inference of time-series scRNA-seq data.Fundamental research · 2024Article
- Discovering Immune-Mediated Mechanisms of Gastric Carcinogenesis Through Single-Cell RNA Sequencing.Frontiers in immunology · 2022Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Clustering cells and depicting the lineage relationship among cell subpopulations are fundamental tasks in single-cell omics studies. However, existing analytical methods face challenges in stratifying cells, tracking cellular trajectories, and identifying critical points of cell transitions. To overcome these, we proposed a novel Markov hierarchical clustering algorithm (MarkovHC), a topological clustering method that leverages the metastability of exponentially perturbed Markov chains for systematically reconstructing the cellular landscape. Briefly, MarkovHC starts with local connectivity and density derived from the input and outputs a hierarchical structure for the data. We firstly benchmarked MarkovHC on five simulated datasets and ten public single-cell datasets with known labels. Then, we used MarkovHC to investigate the multi-level architectures and transition processes during human embryo preimplantation development and gastric cancer procession. MarkovHC found heterogeneous cell states and sub-cell types in lineage-specific progenitor cells and revealed the most possible transition paths and critical points in the cellular processes. These results demonstrated MarkovHC's effectiveness in facilitating the stratification of cells, identification of cell populations, and characterization of cellular trajectories and critical points.
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.