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Related Concept Videos

Deconvolution01:20

Deconvolution

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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.
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Sampling Methods: Sample Types01:18

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Sampling materials are classified into three main types: solid, liquid, and gas.
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The alternative coordinate method, also known as the Shoelace Formula, is a technique for determining the area of a traverse using Cartesian coordinates. This method relies on the sequential arrangement of x and y coordinates for each point of the shape, ensuring accuracy and ease of application.In this approach, each corner's x and y coordinates are listed as fractions, with the x-coordinate as the numerator and the y-coordinate as the denominator. These coordinates are arranged sequentially...
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Convenience Sampling Method00:55

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
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Related Experiment Video

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Deblender: a semi-/unsupervised multi-operational computational method for complete deconvolution of expression data

Konstantina Dimitrakopoulou1,2, Elisabeth Wik3,4, Lars A Akslen3,4

  • 1Centre for Cancer Biomarkers CCBIO, Department of Informatics, University of Bergen, Bergen, Norway.

BMC Bioinformatics
|November 9, 2018
PubMed
Summary

Deblender is a new computational tool that analyzes gene expression data to understand cellular heterogeneity in tumors. It helps identify cancer biomarkers by deconvoluting mixed samples, improving cancer research and clinical applications.

Keywords:
Cellular heterogeneityClusteringDeconvolutionGene expressionMatrix factorizationModel selectionParticle swarmQuadratic programming

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Area of Science:

  • Computational biology
  • Genomics
  • Biomarker discovery

Background:

  • Understanding cellular heterogeneity and tumor microenvironment interactions is crucial for cancer biomarker discovery.
  • Previous computational deconvolution methods had limitations, requiring prior information on cell proportions or expression signatures.
  • Second-generation 'complete' deconvolution methods estimate cell proportions and expression profiles from mixed gene expression data using marker genes.

Purpose of the Study:

  • To introduce Deblender, a flexible computational tool for complete deconvolution of gene expression data.
  • To enable semi-/unsupervised analysis based on user-provided marker gene lists and cell composition information.
  • To offer a solution for estimating cell types and proportions even with limited prior knowledge.

Main Methods:

  • Deblender operates in semi-/unsupervised modes, utilizing known marker genes or global gene expression variability for clustering.
  • A model selection criterion is integrated to predict the number of constituent cell/tissue types.
  • A specialized algorithm estimates mixture proportions for complex scenarios where cell types exceed sample numbers.

Main Results:

  • Deblender's performance was evaluated against state-of-the-art tools using benchmark and patient cancer expression datasets, including TCGA.
  • The tool demonstrated flexibility in handling varying levels of prior knowledge.
  • Results confirmed Deblender's capability in analyzing complex gene expression mixtures.

Conclusions:

  • Deblender is a valuable tool for enhancing the understanding of gene expression datasets in cancer research.
  • Its application has implications for improved prediction and clinical utilization of genomic data.
  • The Deblender software is implemented in MATLAB and publicly available.