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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Deconvolution01:20

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...
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...
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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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
05:22

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Semi-supervised Nonnegative Matrix Factorization for gene expression deconvolution: a case study.

Renaud Gaujoux1, Cathal Seoighe

  • 1Computational Biology Group, Institute of Infectious Disease and Molecular Medicine, University of Cape Town, South Africa. renaud@cbio.uct.ac.za

Infection, Genetics and Evolution : Journal of Molecular Epidemiology and Evolutionary Genetics in Infectious Diseases
|September 21, 2011
PubMed
Summary
This summary is machine-generated.

Incorporating marker genes into Nonnegative Matrix Factorization (NMF) significantly enhances the accuracy of gene expression deconvolution in heterogeneous samples. This semi-supervised approach improves cell type proportion and signature estimation for better biological interpretation.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Sample heterogeneity is a critical challenge in gene expression studies, particularly in immunology and infectious disease research.
  • Accurate interpretation of biological processes requires accounting for variations in cell type proportions within samples.
  • Nonnegative Matrix Factorization (NMF) is a powerful unsupervised learning technique for analyzing high-dimensional data like gene expression.

Purpose of the Study:

  • To investigate the use of prior knowledge, specifically marker genes, to improve gene expression deconvolution using NMF algorithms.
  • To enhance the accuracy and consistency of estimating cell type proportions and gene expression signatures from heterogeneous samples.

Main Methods:

  • Developed and tested semi-supervised NMF algorithms guided by known marker genes.
  • Applied the marker-guided NMF methods to a microarray dataset of known cell type mixtures.
  • Evaluated the performance by comparing estimated cell type proportions and marker gene assignments against true values.

Main Results:

  • Marker-guided NMF substantially improved the Pearson correlation between true and estimated cell type proportions (from ~0.5 to ~0.8).
  • The guided NMF versions demonstrated improved consistency in estimating both cell type proportions and gene expression signatures.
  • Known marker genes were more frequently and correctly assigned to their respective cell types using the semi-supervised approach.

Conclusions:

  • Utilizing marker genes as prior knowledge significantly boosts the accuracy of NMF-based gene expression deconvolution.
  • Marker-guided NMF offers a more reliable method for analyzing complex biological samples with varying cellular compositions.
  • Further refinements in utilizing marker gene information could lead to even greater improvements in deconvolution accuracy.