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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...

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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data

Published on: January 16, 2019

clusterMaker: a multi-algorithm clustering plugin for Cytoscape.

John H Morris1, Leonard Apeltsin, Aaron M Newman

  • 1Department of Pharmaceutical Chemistry, University of California San Francisco, San Francisco, California, USA. scooter@cgl.ucsf.edu

BMC Bioinformatics
|November 11, 2011
PubMed
Summary
This summary is machine-generated.

Scientists can now analyze diverse biological data with clusterMaker, a Cytoscape plugin offering multiple clustering algorithms and integrated visualizations for pattern discovery. This tool aids in identifying protein complexes and generating new biological hypotheses.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • The post-genomic era demands advanced computational tools for integrating diverse, high-throughput biological data.
  • Existing tools lack comprehensive capabilities for combined analysis of multiple experimental data types, particularly for protein-protein interactions.
  • Scientists need accessible platforms to identify biologically meaningful patterns within complex datasets.

Purpose of the Study:

  • To introduce clusterMaker, a Cytoscape plugin designed for integrated analysis and visualization of diverse biological data.
  • To provide a versatile tool implementing a wide array of clustering algorithms and visualization methods.
  • To facilitate the discovery of patterns and generation of hypotheses in biological datasets.

Main Methods:

  • Development of clusterMaker as a Cytoscape plugin.
  • Implementation of multiple clustering algorithms including hierarchical clustering, k-means, k-medoid, SCPS, AutoSOME, and MCL.
  • Integration of network, dendrogram, and heat map visualizations, dynamically linked within Cytoscape.

Main Results:

  • Demonstrated utility through three scenarios: analysis of mouse interactome and microarray data, identification of protein complexes in yeast, and cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily.
  • Enabled exploration of functionally enriched interactomes and application of fuzzy clustering for specific cellular phenotypes.
  • Facilitated detailed exploration of protein complexes and annotation of enzymes within superfamilies.

Conclusions:

  • clusterMaker offers a powerful, integrated solution for analyzing and visualizing biological data within Cytoscape.
  • The plugin provides unique algorithms and visualizations, enhancing hypothesis generation and confirmation.
  • clusterMaker is readily available through the Cytoscape plugin manager, promoting wider adoption.