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 Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

16.5K
Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
16.5K
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

732
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
732
The Extracellular Matrix01:42

The Extracellular Matrix

88.8K
Overview
88.8K
Transcription Factors02:16

Transcription Factors

82.7K
Tissue-specific transcription factors contribute to diverse cellular functions in mammals. For example, the gene for beta globin, a major component of hemoglobin, is present in all cells of the body. However, it is only expressed in red blood cells because the transcription factors that can bind to the promoter sequences of the beta globin gene are only expressed in these cells. Tissue-specific transcription factors also ensure that mutations in these factors may impair only the function of...
82.7K
Overview of Cell-Matrix Interactions01:24

Overview of Cell-Matrix Interactions

9.0K
The extracellular matrix or ECM holds cells together to form a tissue and allows the cells within the tissue to communicate. ECM comprises proteins such as fibronectin, collagen, laminin, etc. The most abundant protein in this space is collagen. Collagen fibers are interwoven with carbohydrate-containing protein molecules called proteoglycans. ECM allows cell migration and provides a structural scaffold at cell adhesion that anchors the cell when the extracellular matrix proteins interact with...
9.0K
Overview of Microsoft Excel as a Data Analysis Tool01:13

Overview of Microsoft Excel as a Data Analysis Tool

1.5K
Microsoft Excel is a cornerstone tool for data analysis and statistical operations, offering a wide array of functionalities to manage, analyze, and visualize data efficiently. Recognized for its versatility, Excel facilitates the performance of basic to complex statistical operations, serving as an indispensable asset for analysts, researchers, and students alike. Excel's significance in data analysis emanates from its spreadsheet environment, where data can be organized in rows and...
1.5K

You might also read

Related Articles

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

Sort by
Same author

ssHiCstuff: a package for the design and analysis of ssDNA-specific Hi-C experiments.

Bioinformatics (Oxford, England)·2026
Same author

<i>Trans</i>-acting mutations reveal non-nuclear modulators of both intrinsic and extrinsic gene expression noise in a eukaryote.

bioRxiv : the preprint server for biology·2025
Same author

Multi-cellular phenotypic dynamics during the progression of an immunocompetent breast cancer model.

iScience·2025
Same author

Mast cells act as pro-angiogenic and pro-tumorigenic players in pituitary gonadotroph tumors.

Neuro-oncology·2025
Same author

Computational pathology annotation enhances the resolution and interpretation of breast cancer spatial transcriptomics data.

NPJ precision oncology·2025
Same author

Inspiratory and expiratory sinus arrhythmia in healthy human.

Physiological reports·2025
Same journal

Biomedical Concept Recognition with Error-aware Negative-enhanced Ranking Framework.

Bioinformatics (Oxford, England)·2026
Same journal

TEDLH: Domain HMMs for sensitive detection of remote homologues.

Bioinformatics (Oxford, England)·2026
Same journal

PLNFGL: Joint Estimation of Multi-Condition Gene Networks from Single-cell RNA-seq Data.

Bioinformatics (Oxford, England)·2026
Same journal

MCFST: Spatial domain identification method based on multi-view graph convolutional network and graph fusion network.

Bioinformatics (Oxford, England)·2026
Same journal

SpaBiT: Enhancing Spatial Transcriptomics Resolution via Bidirectional Attention Transformers.

Bioinformatics (Oxford, England)·2026
Same journal

EDEL: Enhancing Dense Retrievers for Curation of Biomedical Knowledge Bases.

Bioinformatics (Oxford, England)·2026
See all related articles

Related Experiment Video

Updated: Jan 27, 2026

Counting Proteins in Single Cells with Addressable Droplet Microarrays
12:25

Counting Proteins in Single Cells with Addressable Droplet Microarrays

Published on: July 6, 2018

9.0K

Probabilistic count matrix factorization for single cell expression data analysis.

Ghislain Durif1,2,3, Laurent Modolo1,4,5, Jeff E Mold5

  • 1Univ Lyon, Université Lyon 1, CNRS, LBBE UMR 5558, F Villeurbanne, France.

Bioinformatics (Oxford, England)
|March 14, 2019
PubMed
Summary
This summary is machine-generated.

We developed a probabilistic Count Matrix Factorization (pCMF) method for analyzing single-cell RNA sequencing data. This approach effectively represents complex gene expression patterns, aiding in cell clustering and visualization.

More Related Videos

Flow Cytometry Analysis of Tissue Factor Expression in Human Platelets
10:08

Flow Cytometry Analysis of Tissue Factor Expression in Human Platelets

Published on: November 22, 2024

1.6K
Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
11:01

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Published on: November 17, 2016

48.9K

Related Experiment Videos

Last Updated: Jan 27, 2026

Counting Proteins in Single Cells with Addressable Droplet Microarrays
12:25

Counting Proteins in Single Cells with Addressable Droplet Microarrays

Published on: July 6, 2018

9.0K
Flow Cytometry Analysis of Tissue Factor Expression in Human Platelets
10:08

Flow Cytometry Analysis of Tissue Factor Expression in Human Platelets

Published on: November 22, 2024

1.6K
Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins
11:01

Automated Quantification and Analysis of Cell Counting Procedures Using ImageJ Plugins

Published on: November 17, 2016

48.9K

Area of Science:

  • Genomics
  • Computational Biology
  • Bioinformatics

Background:

  • High-throughput single-cell sequencing reveals cellular transcriptome diversity and variability.
  • Statistical challenges arise in summarizing and visualizing complex single-cell expression data.
  • Traditional methods like Principal Component Analysis (PCA) are limited by Euclidean distance for over-dispersed count data with dropouts.

Purpose of the Study:

  • To introduce a novel probabilistic Count Matrix Factorization (pCMF) approach for single-cell expression data analysis.
  • To develop a method that can jointly represent cells and genes in a low-dimensional space.
  • To provide a statistically robust framework for visualizing and clustering single-cell data.

Main Methods:

  • Developed a sparse Gamma-Poisson factor model for probabilistic Count Matrix Factorization (pCMF).
  • Inferred the hierarchical model using a variational Expectation-Maximization (EM) algorithm.
  • Evaluated pCMF against standard representation methods like t-SNE for single-cell data.

Main Results:

  • pCMF jointly builds low-dimensional representations of cells and genes.
  • The probabilistic framework provides a suitable geometry for single-cell data visualization.
  • pCMF demonstrates powerful data compression for clustering purposes, outperforming existing methods.

Conclusions:

  • pCMF offers an effective probabilistic approach for analyzing single-cell expression data.
  • The method enhances data visualization and clustering capabilities.
  • The pCMF R-package is available for broader research use.