Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Dec 11, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.9K

Visualizing Single-Cell RNA-seq Data with Semisupervised Principal Component Analysis.

Zhenqiu Liu1

  • 1Department of Public Health Sciences, Penn State College of Medicine, Hershey, PA 17033, USA.

International Journal of Molecular Sciences
|August 19, 2020
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Recent evolution of risk analyses in atomic bomb survivor studies: new methods and applications.

Carcinogenesis·2025
Same author

Distinct noncoding RNAs and RNA binding proteins associated with high-risk pediatric and adult acute myeloid leukemias detected by regulatory network analysis.

Cancer reports (Hoboken, N.J.)·2021
Same author

Clustering Single-Cell RNA-Seq Data with Regularized Gaussian Graphical Model.

Genes·2021
Same author

Expression Patterns of Immune Genes Reveal Heterogeneous Subtypes of High-Risk Neuroblastoma.

Cancers·2020
Same journal

RETRACTED: Kim et al. The Angiogenesis Inhibitor ALS-L1023 from Lemon-Balm Leaves Attenuates High-Fat Diet-Induced Nonalcoholic Fatty Liver Disease Through Regulating the Visceral Adipose-Tissue Function. <i>Int. J. Mol. Sci.</i> 2017, <i>18</i>, 846.

International journal of molecular sciences·2026
Same journal

Correction: Mahmud et al. Thymoquinone Attenuates NF-κβ Signalling Activation in Retinal Pigment Epithelium Cells Under AMD-Mimicking Conditions. <i>Int. J. Mol. Sci.</i> 2025, <i>26</i>, 11473.

International journal of molecular sciences·2026
Same journal

Correction: Borovikov et al. The Twisting and Untwisting of Actin and Tropomyosin Filaments Are Involved in the Molecular Mechanisms of Muscle Contraction, and Their Disruption Can Result in Muscle Disorders. <i>Int. J. Mol. Sci</i>. 2025, <i>26</i>, 6705.

International journal of molecular sciences·2026
Same journal

Correction: Molagoda et al. Flavonoid Glycosides from <i>Ziziphus jujuba</i> var. <i>inermis</i> (Bunge) Rehder Seeds Inhibit α-Melanocyte-Stimulating Hormone-Mediated Melanogenesis. <i>Int. J. Mol. Sci.</i> 2021, <i>22</i>, 7701.

International journal of molecular sciences·2026
Same journal

Correction: Guo et al. Integrated Transcriptomic and Metabolomic Analysis Reveals the Molecular Regulatory Mechanism of Flavonoid Biosynthesis in Maize Roots Under Lead Stress. <i>Int. J. Mol. Sci.</i> 2024, <i>25</i>, 6050.

International journal of molecular sciences·2026
Same journal

Correction: Chang et al. Improvement of Carbon Tetrachloride-Induced Acute Hepatic Failure by Transplantation of Induced Pluripotent Stem Cells Without Reprogramming Factor c-Myc. <i>Int. J. Mol. Sci.</i> 2012, <i>13</i>, 3598-3617.

International journal of molecular sciences·2026
See all related articles

We introduce semisupervised principal component analysis (ssPCA), a novel method for visualizing single-cell RNA sequencing (scRNA-seq) data. ssPCA effectively preserves both local and global data structures, revealing biological insights and cell transitions.

Area of Science:

  • Genomics
  • Bioinformatics
  • Computational Biology

Background:

  • Single-cell RNA sequencing (scRNA-seq) enables analysis of diverse cell populations.
  • Current visualization methods like PCA, t-SNE, and UMAP have limitations in preserving global data structure.
  • Effective visualization is crucial for extracting biological information and identifying cell subtypes from scRNA-seq data.

Purpose of the Study:

  • To develop a novel visualization method for scRNA-seq data that preserves both local and global structures.
  • To address the limitations of existing methods in capturing the complete data landscape.
  • To provide a robust and efficient tool for scRNA-seq data exploration and interpretation.

Main Methods:

  • Proposed a semisupervised principal component analysis (ssPCA) approach for scRNA-seq data visualization.
Keywords:
cluster visualizationdimension reductionnonlinear visualizationscRNA-seq visualizationsemisupervised principal component analysis

More Related Videos

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets
11:34

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

16.9K
Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy
04:21

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Published on: January 19, 2024

3.3K

Related Experiment Videos

Last Updated: Dec 11, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues
10:12

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

18.9K
Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets
11:34

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

16.9K
Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy
04:21

Author Spotlight: Vascular Tissue Dissociation and Exploring Single-Cell Subclusters for Targeted Therapy

Published on: January 19, 2024

3.3K
  • ssPCA incorporates cluster labels into the dimension reduction process.
  • The method discovers principal components that maximize both data variance and cluster dependence.

    • Main Results:

    • ssPCA successfully preserves both local and global structures in scRNA-seq data.
    • The method can uncover data transitions and progressions, revealing biological trajectories.
    • Experiments with simulated and real data validate the effectiveness of ssPCA.

    Conclusions:

    • ssPCA offers a superior approach for scRNA-seq data visualization compared to existing methods.
    • The method is computationally efficient, robust, and provides a global optimal solution.
    • ssPCA is a valuable tool for visualizing clusters and understanding complex cell populations in scRNA-seq studies.