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DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
¹H NMR Signal Integration: Overview00:58

¹H NMR Signal Integration: Overview

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...
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...

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Related Experiment Video

Updated: Jul 10, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

A tensor higher-order singular value decomposition for integrative analysis of DNA microarray data from different

Larsson Omberg1, Gene H Golub, Orly Alter

  • 1Department of Biomedical Engineering, and Institutes for Cellular and Molecular Biology, University of Texas, Austin, TX 78712, USA.

Proceedings of the National Academy of Sciences of the United States of America
|November 16, 2007
PubMed
Summary

Higher-order singular value decomposition (HOSVD) integrates gene expression data. This method reveals significant biological programs and previously unrecognized roles for specific genes in yeast cell cycle responses to oxidative stress.

Related Experiment Videos

Last Updated: Jul 10, 2026

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress

Published on: July 29, 2022

Area of Science:

  • Systems Biology
  • Bioinformatics
  • Genomics

Background:

  • Integrating large-scale DNA microarray data from diverse studies presents computational challenges.
  • Understanding complex gene expression patterns requires advanced analytical methods.
  • Yeast cell cycle regulation under oxidative stress involves intricate molecular pathways.

Purpose of the Study:

  • To develop and apply a higher-order singular value decomposition (HOSVD) method for analyzing multi-study gene expression data.
  • To identify significant biological programs and regulatory mechanisms underlying yeast responses to hydrogen peroxide and menadione.
  • To uncover novel correlations between biological processes at a genome-wide scale.

Main Methods:

  • Transformation of gene expression data into a multi-dimensional tensor.
  • Application of higher-order singular value decomposition (HOSVD) to decompose the data tensor into core components (eigenarrays, eigengenes).
  • Analysis of rank-1 subtensors to define and quantify the information captured by each biological program.

Main Results:

  • Significant subtensors identified represent distinct biological programs and experimental phenomena.
  • Specific conserved genes (YKU70, MRE11, AIF1, ZWF1) and pathways (retrotransposition, apoptosis, oxidative pentose phosphate pathway) implicated in differential oxidative stress responses.
  • A genome-scale correlation between DNA replication initiation and RNA transcription was independently discovered.

Conclusions:

  • HOSVD is an effective method for integrating and analyzing complex, multi-study gene expression datasets.
  • The study highlights previously unrecognized roles for specific genes and pathways in yeast's response to oxidative stress.
  • The findings suggest potential novel regulatory mechanisms connecting DNA replication and RNA transcription.