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Related Concept Videos

Block Diagram Reduction01:22

Block Diagram Reduction

The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

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,...
Extraction: Partition and Distribution Coefficients01:14

Extraction: Partition and Distribution Coefficients

The distribution law or Nernst's distribution law is the law that governs the distribution of a solute between two immiscible solvents. This law, also known as the partition law, states that if a solute is added to the mixture of two immiscible solvents at a constant temperature, the solute is distributed between the two solvents in such a way that the ratio of solute concentrations in the solvents remains constant at equilibrium.
For extracting a solute from an aqueous phase into an organic...
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This relationship...
Manipulation and Analysis01:21

Manipulation and Analysis

GIS manipulation and analysis functions are vital for decision-making and planning. These activities range from data retrieval tasks, such as selecting information based on specific criteria, to advanced analytical techniques that address complex spatial problems.One critical GIS analysis method is overlaying, which combines multiple data layers to examine impacts. For example, overlaying a river-dammed lake boundary with road networks can identify affected infrastructure. Another common...

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Related Experiment Video

Updated: May 9, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Global, local and unique decompositions in OnPLS for multiblock data analysis.

Tommy Löfstedt1, Daniel Hoffman, Johan Trygg

  • 1Computational Life Science Cluster (CLiC), Department of Chemistry, Umeå University, Umeå, Sweden.

Analytica Chimica Acta
|July 30, 2013
PubMed
Summary

OnPLS extends O2PLS to decompose matrices into global, local, and unique variation. This method enhances the interpretability of multiblock and path models in complex biological data analysis.

Keywords:
GlobalLocal and uniquevariationMultiblock analysisOnPLSOrthogonal partial least squares

Related Experiment Videos

Last Updated: May 9, 2026

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease
08:51

Application of Unsupervised Multi-Omic Factor Analysis to Uncover Patterns of Variation and Molecular Processes Linked to Cardiovascular Disease

Published on: September 20, 2024

Area of Science:

  • Multivariate data analysis
  • Bioinformatics
  • Systems biology

Background:

  • Analyzing complex biological data requires methods to disentangle shared and unique variation across multiple data types.
  • Orthogonal Partial Least Squares (O2PLS) is a method for analyzing relationships between two matrices.
  • Extending O2PLS to multiple matrices is crucial for systems biology approaches.

Purpose of the Study:

  • To introduce an extension of O2PLS, termed OnPLS, for multiblock and path model analysis.
  • To further extend OnPLS to decompose non-globally joint variation into locally joint and unique components.
  • To demonstrate the utility of the extended OnPLS method using simulated and real biological data.

Main Methods:

  • OnPLS decomposes matrices into globally joint, locally joint, and unique variation.
  • Recursive application of OnPLS to remaining variation identifies locally joint models.
  • The method was applied to simulated data and a real dataset from metabolomic, proteomic, and transcriptomic profiling.

Main Results:

  • OnPLS successfully decomposes matrices into global, local, and unique components.
  • The method resulted in fewer globally joint components and higher intercorrelations of scores.
  • Application to hybrid aspen data demonstrated successful decomposition and increased model interpretability.

Conclusions:

  • OnPLS provides a robust framework for dissecting variation in multiblock data.
  • The method enhances the interpretability of complex biological systems by separating global, local, and unique patterns.
  • OnPLS is a valuable tool for systems biology research integrating multi-omics data.