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Related Concept Videos

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...
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Frequency-dependent Selection

When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Linear Approximation in Frequency Domain

Linear systems are characterized by two main properties: superposition and homogeneity. Superposition allows the response to multiple inputs to be the sum of the responses to each individual input. Homogeneity ensures that scaling an input by a scalar results in the response being scaled by the same scalar.
In contrast, nonlinear systems do not inherently possess these properties. However, for small deviations around an operating point, a nonlinear system can often be approximated as linear.
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Determination of Expected Frequency

Suppose one wants to test independence between the two variables of a contingency table. The values in the table constitute the observed frequencies of the dataset. But how does one determine the expected frequency of the dataset? One of the important assumptions is that the two variables are independent, which means the variables do not influence each other. For independent variables, the statistical probability of any event involving both variables is calculated by multiplying the individual...

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The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics
13:02

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published on: October 5, 2016

Listen to genes: dealing with microarray data in the frequency domain.

Jianfeng Feng1, Dongyun Yi, Ritesh Krishna

  • 1Centre for Computational System Biology, Shanghai, Fudan University, Shanghai, People's Republic of China. Jianfeng.Feng@warwick.ac.uk

Plos One
|June 30, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel data-driven method for analyzing temporal gene expression data. It uses advanced clustering and causality analysis to uncover complex gene interaction networks, aiding biological hypothesis generation.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Genomics

Background:

  • Temporal microarray data analysis is crucial for understanding dynamic biological processes.
  • Existing methods often lack the systematic approach needed for complex gene interactions.
  • Analyzing gene expression over time reveals regulatory networks and cellular responses.

Purpose of the Study:

  • To develop and validate a novel, systematic approach for analyzing temporal microarray data.
  • To identify gene interaction networks and biological hypotheses directly from data.
  • To apply the method to Arabidopsis leaf senescence data.

Main Methods:

  • Genes are normalized using an error model to estimate variations and minimize correlations.
  • Clustering is performed based on power spectrum density for gene grouping.
  • Complex and partial Granger causality are applied in time and frequency domains to infer gene interactions.

Main Results:

  • The approach successfully identified gene circuits in Arabidopsis leaf data.
  • Three distinct gene circuits (circadian, ethylene, and a novel senescence-related circuit) were analyzed.
  • The method revealed a hierarchical structure for senescence initiation.

Conclusions:

  • The data-driven approach facilitates biological hypothesis generation from temporal gene expression.
  • Power spectrum clustering and Granger causality are effective for uncovering hidden biological interactions.
  • This methodology provides valuable tools for researchers working with time-series gene expression data.