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Updated: Jul 16, 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

ZINBMM: a general mixture model for simultaneous clustering and gene selection using single-cell transcriptomic data.

Yang Li^1,2,3, Mingcong Wu^1,3, Shuangge Ma⁴

¹Center for Applied Statistics and School of Statistics, Renmin University of China, Beijing, China.

|September 11, 2023

Summary

This summary is machine-generated.

This study introduces a novel zero-inflated negative binomial mixture model (ZINBMM) for single-cell RNA sequencing (scRNA-seq) data analysis. ZINBMM effectively clusters cells and identifies cluster-specific genes, enhancing the understanding of cell heterogeneity.

Keywords:

Clustering analysis Gene selection ScRNA-seq data

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Last Updated: Jul 16, 2025

Droplet Barcoding-Based Single Cell Transcriptomics of Adult Mammalian Tissues

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Published on: January 10, 2019

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A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

A Combinatorial Single-cell Approach to Characterize the Molecular and Immunophenotypic Heterogeneity of Human Stem and Progenitor Populations

Published on: October 25, 2018

Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Single-cell RNA sequencing (scRNA-seq) is crucial for identifying cell types and lineages.
Existing methods often lack the ability to pinpoint cluster-specific genes driving cell heterogeneity.
Understanding cell heterogeneity is key to advancing biological insights.

Purpose of the Study:

To develop a novel computational model for simultaneous clustering and gene selection in scRNA-seq data.
To address limitations in current methods for investigating cluster-specific genes.
To improve the biological understanding of cell heterogeneity.

Main Methods:

A zero-inflated negative binomial mixture model (ZINBMM) was developed.
The model analyzes raw gene expression counts, accounting for batch effects and dropout events.
Systemic analysis was performed on simulated and real scRNA-seq datasets.

Main Results:

ZINBMM demonstrated effective clustering of scRNA-seq data.
The model successfully performed gene selection for cluster-specific markers.
Practical applicability was validated across five diverse scRNA-seq datasets.

Conclusions:

ZINBMM offers a robust approach for scRNA-seq data analysis.
The method enhances the identification of genes contributing to cell heterogeneity.
ZINBMM facilitates deeper biological insights from single-cell data.