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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...
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...
Statistical Software for Data Analysis and Clinical Trials01:12

Statistical Software for Data Analysis and Clinical Trials

Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
Statgraphics01:10

Statgraphics

Statgraphics is a comprehensive statistical software suite designed for both basic and advanced data analysis. Originating in 1980 at Princeton University under Dr. Neil W. Polhemus, it was one of the pioneering tools for statistical computing on personal computers, with its public release in 1982 marking an early milestone in data science software. Over the years, it has evolved into a robust platform for data science, offering tools for regression analysis, ANOVA, multivariate statistics,...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Kendall's Coefficient of Concordance01:20

Kendall's Coefficient of Concordance

Kendall's Coefficient of Concordance (W), also known as Kendall's W, is a non-parametric statistical measure used to assess the agreement or concordance between multiple raters or judges when they rank a set of items. It is often used when you have ordinal data (ranks) and you want to see if there is consistency or consensus among the raters. It is widely applied in research areas such as psychology, medicine, and social sciences, where multiple judges are asked to rank or rate subjects or...

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Updated: Jun 16, 2026

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

ConsensusCluster: a software tool for unsupervised cluster discovery in numerical data.

Michael Seiler1, C Chris Huang, Sandor Szalma

  • 1BioMaPS Institute, Rutgers University, Piscataway, New Jersey 08854, USA.

Omics : a Journal of Integrative Biology
|February 10, 2010
PubMed
Summary
This summary is machine-generated.

ConsensusCluster software provides robust clustering for high-dimensional single nucleotide polymorphism (SNP) and gene expression data. It enhances data analysis by identifying hidden patterns for biological interpretation.

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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Published on: February 15, 2017

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Last Updated: Jun 16, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
05:12

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

Published on: January 16, 2019

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • High-dimensional data analysis is crucial in genomics.
  • Clustering algorithms are essential for stratifying complex biological datasets.
  • Existing software may lack robustness in analyzing single nucleotide polymorphism (SNP) and gene expression data.

Purpose of the Study:

  • To develop a stand-alone software tool, ConsensusCluster, for robust analysis of high-dimensional SNP and gene expression microarray data.
  • To implement consensus clustering and principal component analysis for reliable data stratification.
  • To provide a user-friendly tool for uncovering hidden patterns in biological data.

Main Methods:

  • Consensus clustering algorithm combined with principal component analysis.
  • Data and sample resampling to ensure robustness.
  • Averaging results from multiple clustering algorithms (K-Means, Partition Around Medoids, Self-Organizing Map, Hierarchical).
  • Generation of consensus matrix heatmaps and feature logs for visual representation and interpretation.

Main Results:

  • ConsensusCluster produces more robust and reliable clusters compared to common software packages.
  • The software effectively stratifies high-dimensional data into distinct clusters.
  • Identifies key features distinguishing clusters, aiding biological interpretation.
  • Provides a visual heatmap for intuitive understanding of cluster membership.

Conclusions:

  • ConsensusCluster is a powerful unsupervised learning tool for biological data analysis.
  • The software offers enhanced accuracy and stability in clustering.
  • It facilitates the discovery of novel biological insights from complex genomic datasets.
  • The free and accessible nature of ConsensusCluster promotes its adoption in research.