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Mixtures of Acids03:27

Mixtures of Acids

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The pH of a solution containing an acid can be determined using its acid dissociation constant and its initial concentration. If a solution contains two different acids, then its pH can be determined using one of several methods depending upon the relative strength of the acids and their dissociation constants.
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The pH of a solution containing an acid can be determined using its acid dissociation constant and initial concentration. If a solution contains two different acids, then its pH can be determined using one of several methods depending on the relative strength of the acids and their dissociation constants.
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Poisson's ratio is a material property that indicates their stress response. It explains the connection between the elongation or compression a material undergoes in the direction of an applied force and the contraction or expansion it experiences perpendicular to that force. When a slender bar is loaded axially, it stretches in the direction of the force and contracts laterally. Poisson's ratio is the negative ratio of this lateral contraction to the axial elongation. The negative sign...
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A Poisson probability distribution is a discrete probability distribution. It gives the probability of a number of events occurring in a fixed interval of time or space if these events happen at a known average rate and independently of the time since the last event. For example, a book editor might be interested in the number of words spelled incorrectly in a particular book. It might be that, on average, there are five words spelled incorrectly in 100 pages. The interval is 100 pages.
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Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project
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A multivariate Poisson-log normal mixture model for clustering transcriptome sequencing data.

Anjali Silva1,2, Steven J Rothstein2, Paul D McNicholas3

  • 1Department of Mathematics and Statistics, University of Guelph, Guelph, N1G 2W1, Canada.

BMC Bioinformatics
|July 18, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a new clustering method for high-throughput sequencing data. The mixture of multivariate Poisson-log normal (MPLN) model effectively identifies co-expressed genes, aiding in biological pathway discovery.

Keywords:
ClusteringCo-expression networksDiscrete dataMarkov chain Monte CarloMultivariate Poisson-log normal distributionRNA sequencing

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-throughput sequencing generates discrete, skewed data, posing challenges for gene network analysis.
  • Traditional visualization methods are limited in high dimensions and lack clarity for subgroup separation.
  • Cluster analysis offers an intuitive approach to uncover patterns in complex biological data.

Purpose of the Study:

  • To develop a novel clustering method for high-dimensional, discrete, and skewed transcriptome sequencing data.
  • To identify groups of co-expressed genes to elucidate biological functions and pathways.
  • To address limitations of current methods in analyzing complex gene expression patterns.

Main Methods:

  • Development of a mixture of multivariate Poisson-log normal (MPLN) model.
  • Parameter estimation using a Markov chain Monte Carlo expectation-maximization (MCMC-EM) algorithm.
  • Model selection guided by information criteria.

Main Results:

  • The MPLN model effectively clusters high-throughput transcriptome sequencing data.
  • The method accommodates various correlation and overdispersion scenarios.
  • Successful application in modeling multivariate count data from RNA sequencing.

Conclusions:

  • The mixture of MPLN model is suitable for analyzing RNA sequencing data.
  • This approach aids in discovering biologically relevant gene groupings.
  • Open-source scripts are available for method implementation.