Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Search research articles

Related Experiment Video

Updated: Jul 5, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

scQA: A dual-perspective cell type identification model for single cell transcriptome data.

Di Li¹, Qinglin Mei², Guojun Li¹

¹Research Center for Mathematics and Interdisciplinary Sciences, Shandong University, Qingdao 266237, China.

Computational and Structural Biotechnology Journal

|January 18, 2024

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

LRRC4 functions as a neuron-protective role in experimental autoimmune encephalomyelitis.

Molecular medicine (Cambridge, Mass.)·2021

Same author

Discovery of a novel RORγ antagonist with skin-restricted exposure for topical treatment of mild to moderate psoriasis.

Scientific reports·2021

Same author

Turning-on persistent luminescence out of chromium-doped zinc aluminate nanoparticles by instilling antisite defects under mild conditions.

Nanoscale·2021

Same author

The α2-Plasmin Inhibitor-Plasmin Complex is a Potential Biomarker of Venous Thromboembolism in Orthopedic Trauma Patients.

Clinical laboratory·2021

Same author

Incidence, clinical risk and prognostic factors for liver metastasis in patients with cervical cancer: a population-based retrospective study.

BMC cancer·2021

Same author

Application of logistic regression and convolutional neural network in prediction and diagnosis of high-risk populations of lung cancer.

European journal of cancer prevention : the official journal of the European Cancer Prevention Organisation (ECP)·2021

Same journal

From Pixels to Patterns: A Multidimensional Framework to Decode Cytoskeletal Organization.

Computational and structural biotechnology journal·2026

Same journal

A Large Concept Model for Mechanistic Simulation of Disease Trajectories: A Hypothesis-Generating Exemplar for Pediatric Acute Lymphoblastic Leukemia.

Computational and structural biotechnology journal·2026

Same journal

Adversarial Sequence Mutations in AlphaFold and ESMFold Reveal Nonphysical Structural Invariance, Confidence Failures, and Concerns for Protein Design.

Computational and structural biotechnology journal·2026

Same journal

High-Throughput Prediction of Protein-Protein Interactions Uncovers Hidden Molecular Networks in Biosynthetic Gene Clusters.

Computational and structural biotechnology journal·2026

Same journal

A Region-Aware Structured Framework Improves Prediction of Gene Expression from DNA Methylation.

Computational and structural biotechnology journal·2026

Same journal

Ensemble Machine Learning Approaches Predict Survival in Lower-Grade Glioma Based on Glycosphingolipid Gene Expression and Metabolic Modeling.

Computational and structural biotechnology journal·2026

See all related articles

scQA identifies cell types and key genes from single-cell RNA sequencing data by integrating qualitative and quantitative views. This novel method effectively handles dropout events and outperforms existing tools for cell heterogeneity analysis.

Area of Science:

Computational Biology
Genomics
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for understanding cell heterogeneity.
Existing clustering algorithms often overlook dropout events and focus only on quantitative data.
There is a need for methods that leverage both qualitative and quantitative aspects of scRNA-seq data.

Purpose of the Study:

To introduce scQA, a novel method for cell type and key gene identification from scRNA-seq data.
To develop a bidirectional clustering approach that considers both qualitative and quantitative data features.
To improve the analysis of cell heterogeneity by effectively handling dropout events.

Main Methods:

scQA iteratively identifies key genes, minimizing landmarks while maximizing quasi-trend-preserved genes with dropouts.

Keywords:

Bidirectional clustering Dropout Feature extraction Label propagation Single-cell RNA-seq

More Related Videos

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

Related Experiment Videos

Last Updated: Jul 5, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

Published on: January 10, 2019

Transcriptome Analysis of Single Cells

Transcriptome Analysis of Single Cells

Published on: April 25, 2011

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Single-cell RNA Sequencing and Analysis of Human Pancreatic Islets

Published on: July 18, 2019

It employs a label propagation strategy for clustering, removing the need for a predefined number of cell types.

The method integrates qualitative and quantitative perspectives for comprehensive analysis.

Main Results:

scQA demonstrates superior performance across 20 diverse scRNA-seq datasets compared to existing tools.
Identified key genes are validated both internally and externally, showing significant biological relevance.
The method successfully performs bidirectional clustering, identifying cell types and associated key genes.

Conclusions:

scQA is a valuable tool for scRNA-seq data analysis, offering a unique fusion of qualitative and quantitative approaches.
Its bidirectional clustering and ability to handle dropout events enhance the investigation of cell heterogeneity.
The method is robust, validated, and can be integrated into broader scRNA-seq analysis pipelines.