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

Types of Errors: Detection and Minimization01:12

Types of Errors: Detection and Minimization

11.3K
Error is the deviation of the obtained result from the true, expected value or the estimated central value. Errors are expressed in absolute or relative terms.
Absolute error in a measurement is the numerical difference from the true or central value. Relative error is the ratio between absolute error and the true or central value, expressed as a percentage.
Errors can be classified by source, magnitude, and sign. There are three types of errors: systematic, random, and gross.
Systematic or...
11.3K
T Cell Types and Functions01:24

T Cell Types and Functions

2.4K
When T cells with CD4 markers are activated, they give rise to two types of effector cells: helper T cells and regulatory T cells. Meanwhile, T cells with CD8 markers differentiate into effector cytotoxic T cells. The differentiation of CD4 T cells into helper T cell subsets, such as Th1, Th2, and Th17 cells, is dependent on the antigen type, antigen-presenting cell, and regulatory cytokines.
Th1 cells stimulate dendritic cells to express necessary co-stimulatory molecules on their surfaces for...
2.4K
Connective Tissue Cell Types01:22

Connective Tissue Cell Types

4.2K
Connective tissue develops from the mesoderm of a developing embryo and consists of cells, fibers, and ground substance: a gel-like material containing large complexes of carbohydrates and proteins. Connective tissue was first identified as a separate tissue family in the 18th century, and Johannes Peter Muller coined the term connective tissue.
Fat cells (adipocytes), smooth muscle cells (myoblasts), and bone cells (osteoblasts) are some connective tissue cell types. Some immune system cells...
4.2K
Types of Receptors: Cell Surface Receptors01:28

Types of Receptors: Cell Surface Receptors

27.3K
Cell-surface receptors, also known as transmembrane receptors, are cell surface, membrane-anchored (integral) proteins that bind to external ligand molecules. This type of receptor spans the plasma membrane and performs signal transduction, converting an extracellular signal into an intracellular signal. Ligands that interact with cell-surface receptors do not have to enter the cell that they affect. Cell-surface receptors are also called cell-specific proteins or markers because they are...
27.3K
Cell Adhesion Molecules - Types and Functions01:20

Cell Adhesion Molecules - Types and Functions

9.3K
Cell adhesion molecules (CAMs) are pivotal to multicellularity and the coordinated functioning of tissues and organ systems. They enable physical interactions between cells and provide mechanical strength to tissues. They also function as receptors for signal transmission across the plasma membrane. The CAMs are broadly classified into four families - integrins, cadherins, selectins, and immunoglobulin-like CAMs (IgCAMs).
CAM Families
The Integrin family of proteins is primarily  involved...
9.3K
Types of Hormones02:13

Types of Hormones

83.5K
Hormones can be classified into three main types based on their chemical structures: steroids, peptides, and amines. Their actions are mediated by the specific receptors they bind to on target cells.
83.5K

You might also read

Related Articles

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

Sort by
Same author

A Drug-Drug Multicomponent Crystal of Metformin and Dobesilate: Crystal Structure Analysis and Hygroscopicity Property.

Molecules (Basel, Switzerland)·2022
Same author

CircCERS6 Suppresses the Development of Epithelial Ovarian Cancer Through Mediating miR-630/RASSF8.

Biochemical genetics·2022
Same author

Thrombotic pulmonary embolism of inferior vena cava during caesarean section: A case report and review of the literature.

World journal of clinical cases·2022
Same author

High-performance K-ion half/full batteries with superb rate capability and cycle stability.

Proceedings of the National Academy of Sciences of the United States of America·2022
Same author

One-Pot Synthesis and Characterization of Polyrotaxane-Silica Hybrid Aerogel.

ACS macro letters·2022
Same author

Selective deposition of gold particles onto silicon at the nanoscale controlled by a femtosecond laser through galvanic displacement.

RSC advances·2022
Same journal

Isolation of Mesenchymal Stem Cell-Derived Extracellular Vesicles.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Modeling Melanoma Immune Surveillance by CAR-T Cells in Human Skin Organoids.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Stepwise Optimization of a Matrigel-Based In Vitro Angiogenesis Assay for Reproducible and Quantifiable 2D-Tube Formation Using HUVECs.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Quantifying Mechanical Properties of Fresh Ovarian Tissue with Optical Brillouin Microscopy.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

3D Chromatin Architecture During Early Development: New Methods and New Findings.

Methods in molecular biology (Clifton, N.J.)·2026
Same journal

Metabolic Plasticity in Embryogenesis Throughout the Lens of NAD<sup></sup>.

Methods in molecular biology (Clifton, N.J.)·2026
See all related articles

Related Experiment Video

Updated: Jan 29, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.7K

Rare Cell Type Detection.

Lan Jiang1,2,3

  • 1Howard Hughes Medical Institute, Boston Children's Hospital, Boston, MA, USA. Lan_Jiang@hms.harvard.edu.

Methods in Molecular Biology (Clifton, N.J.)
|February 14, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces GiniClust, a new computational method for identifying rare cell types within large single-cell datasets. It enhances the discovery of distinct cell populations using high-throughput single-cell technologies.

Keywords:
ClusteringGini indexRNA-seqRare cell typeSingle-cell analysisqPCR

More Related Videos

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

17.3K
Author Spotlight: Advancing Metabolomics Analysis of Rare Hematopoietic Stem Cells
08:28

Author Spotlight: Advancing Metabolomics Analysis of Rare Hematopoietic Stem Cells

Published on: February 23, 2024

2.5K

Related Experiment Videos

Last Updated: Jan 29, 2026

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
14:06

Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER

Published on: June 23, 2012

15.7K
Detection of Rare Mutations in CtDNA Using Next Generation Sequencing
11:11

Detection of Rare Mutations in CtDNA Using Next Generation Sequencing

Published on: August 24, 2017

17.3K
Author Spotlight: Advancing Metabolomics Analysis of Rare Hematopoietic Stem Cells
08:28

Author Spotlight: Advancing Metabolomics Analysis of Rare Hematopoietic Stem Cells

Published on: February 23, 2024

2.5K

Area of Science:

  • Computational biology
  • Genomics
  • Single-cell analysis

Background:

  • High-throughput single-cell technologies enable the discovery of novel cell types.
  • Detecting rare cell types amidst large cell populations presents a significant computational challenge.

Purpose of the Study:

  • To present a novel computational method for identifying rare cell types.
  • To overcome the limitations of existing methods in detecting distinct cell populations.

Main Methods:

  • Development and application of the GiniClust algorithm.
  • Utilizing single-cell data to identify cell type heterogeneity.

Main Results:

  • GiniClust effectively detects rare cell types.
  • The method distinguishes rare cell populations from larger, more abundant ones.

Conclusions:

  • GiniClust is a valuable tool for rare cell type discovery in single-cell genomics.
  • This method advances the potential of high-throughput single-cell technologies.