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Related Concept Videos

Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
RNA-seq03:21

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while microarray-based...
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...
Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Gene Duplication and Divergence02:37

Gene Duplication and Divergence

The seminal work of Ohno in 1970 popularized the idea of gene duplication and divergence. DNA sequence comparison studies reveal that a large portion of the genes in bacteria, archaebacteria, and eukaryotes was  generated by gene duplication and divergence, indicating its critical role in evolution.
The duplicated copies of the gene are called Paralogs. Paralogs with similar sequences and functions form a gene family. Across several species, a large number of gene families are characterized.
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 13, 2026

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing
10:44

Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing

Published on: March 23, 2022

Simcluster: clustering enumeration gene expression data on the simplex space.

Ricardo Z N Vêncio1, Leonardo Varuzza, Carlos A de B Pereira

  • 1Institute for Systems Biology, 1441 North 34th street, Seattle, WA 98103-8904, USA. rvencio@gmail.com

BMC Bioinformatics
|July 13, 2007
PubMed
Summary

Simcluster is a new software tool for analyzing gene expression data. It correctly handles compositional data from transcript enumeration methods, unlike standard analysis tools.

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Low-input Nucleus Isolation and Multiplexing with Barcoded Antibodies of Mouse Sympathetic Ganglia for Single-nucleus RNA Sequencing
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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Transcript enumeration techniques like SAGE, MPSS, and EST digital northern are vital for digital gene expression measurement.
  • These methods generate compositional data, which has unique properties due to constrained sums, unlike Euclidean spaces used for microarray data.
  • Standard pattern recognition methods for microarrays may be ineffective for enumeration data due to ignoring simplex space properties.

Purpose of the Study:

  • To introduce Simcluster, a novel software tool for clustering analysis of gene expression data.
  • To address the limitations of existing methods when analyzing compositional data from transcript enumeration techniques.

Main Methods:

  • Development of Simcluster, a software tool specifically designed for compositional data analysis.
  • Implementation of Simcluster as both a command-line C package and an online tool.

Main Results:

  • Simcluster provides a principled approach to clustering analysis for data on the simplex space.
  • The software is available as a downloadable package and an accessible online tool.

Conclusions:

  • Simcluster is built upon a robust mathematical framework for compositional data analysis.
  • The tool is applicable to various contexts, particularly for analyzing enumeration-based gene expression data.
  • Simcluster offers a more appropriate method for analyzing complex gene expression datasets.