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

Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

5.7K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
5.7K
Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

4.5K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
4.5K
Dimensional Analysis01:23

Dimensional Analysis

1.2K
Dimensional analysis is a powerful tool that is used in physics and engineering to understand and predict the behavior of physical systems. The basic idea behind dimensional analysis is to express physical quantities in terms of fundamental dimensions such as the mass, length, and time. Derived dimensions like the velocity, acceleration, and force are derived from the combinations of these fundamental dimensions.
Dimensional analysis allows us to analyze and compare physical quantities on a...
1.2K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

195
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
195
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

287
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
287
¹H NMR Signal Integration: Overview00:58

¹H NMR Signal Integration: Overview

2.0K
The intensity of a signal, which can be represented by the area under the peak, depends on the number of protons contributing to that signal. The area under each peak is shown as a vertical line called an integral, with the integral value listed under it, as seen in the proton NMR spectrum of benzyl acetate. Each integral value is divided by the smallest integral value to obtain the ratio of the number of protons producing each signal. The ratio reveals the relative number of protons and not...
2.0K

You might also read

Related Articles

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

Sort by
Same author

Osr1-expressing mesoderm contributes to lymphatic vessel assembly and complexity in the mammalian kidney.

Cell reports·2026
Same author

Heterogeneity and dynamics of DENV-specific CD8 + T cells in dengue infection.

Nature communications·2026
Same author

A Hormone Cell Atlas maps the human endocrine system at cellular resolution.

Science (New York, N.Y.)·2026
Same author

Editorial: Silent But Detectable-High-Throughput Proteomics to Identify Clinically Significant Liver Disease.

Alimentary pharmacology & therapeutics·2026
Same author

Comparison of Meat Quality, Including Fatty Acid Content and Amino Acid Profile, and Transcriptome Profile among Hanwoo, Korea Black Cattle, and Jeju Black Cattle.

Food science of animal resources·2026
Same author

Single cell transcriptional evolution of myeloid leukemia of Down syndrome.

Nature communications·2026

Related Experiment Video

Updated: Oct 9, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K

MultiMAP: dimensionality reduction and integration of multimodal data.

Mika Sarkin Jain1,2, Krzysztof Polanski3, Cecilia Dominguez Conde3

  • 1Theory of Condensed Matter, Dept Physics, Cavendish Laboratory, University of Cambridge, JJ Thomson Ave, Cambridge, CB3 0HE, UK. mikasarkinjain@gmail.com.

Genome Biology
|December 21, 2021
PubMed
Summary
This summary is machine-generated.

MultiMAP is a new algorithm that integrates diverse biological data, like single-cell transcriptomics, to reveal insights into cell differentiation. It offers a scalable and flexible approach for analyzing complex multimodal datasets.

More Related Videos

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.5K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.2K

Related Experiment Videos

Last Updated: Oct 9, 2025

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments
08:12

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

2.6K
Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects
08:13

Using the Race Model Inequality to Quantify Behavioral Multisensory Integration Effects

Published on: May 10, 2019

6.5K
Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology
09:44

Author Spotlight: Advancing Large-Scale Neural Dynamics Through HD-MEA Technology

Published on: March 8, 2024

5.2K

Area of Science:

  • Single-cell biology
  • Bioinformatics
  • Computational biology

Background:

  • Multimodal data is rapidly expanding in scientific research, particularly in single-cell biology.
  • Integrating diverse datasets (e.g., transcriptomics, epigenomics, spatial) is crucial for a comprehensive understanding of biological systems.
  • Existing methods often have limitations in scalability, flexibility, and handling features unique to specific datasets.

Purpose of the Study:

  • To introduce MultiMAP, a novel algorithm for dimensionality reduction and data integration.
  • To develop a scalable and flexible tool capable of integrating any number of multimodal datasets.
  • To enable the analysis of complex biological processes, such as cell differentiation, using integrated data.

Main Methods:

  • MultiMAP employs a novel, non-linear mapping approach for dimensionality reduction and integration.
  • The algorithm can incorporate features present in only a subset of datasets.
  • Users can specify the influence of individual datasets, and the method is highly scalable for large datasets.

Main Results:

  • MultiMAP was applied to single-cell transcriptomics, chromatin accessibility, methylation, and spatial data, outperforming current methods.
  • Integration of a thymus dataset allowed for analysis along a temporal differentiation trajectory.
  • Quantitative comparisons of transcription factor expression and chromatin accessibility revealed dynamic patterns during T cell differentiation.

Conclusions:

  • MultiMAP provides a powerful and versatile tool for integrating multimodal single-cell data.
  • The algorithm facilitates deeper insights into dynamic biological processes like cell differentiation.
  • MultiMAP's scalability and flexibility make it suitable for large-scale, complex biological data analysis.