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Related Concept Videos

Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Signals01:30

Classification of Signals

In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Classification of Leukocytes01:30

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...

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Related Experiment Video

Updated: Jun 14, 2026

Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis
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Characterization of In Vitro Differentiation of Human Primary Keratinocytes by RNA-Seq Analysis

Published on: May 16, 2020

Integrating gene expression and GO classification for PCA by preclustering.

Jorn R De Haan1, Ester Piek, Rene C van Schaik

  • 1Institute for Molecules and Materials, Analytical Chemistry, Radboud University Nijmegen, Heyendaalseweg 135, Nijmegen, The Netherlands.

BMC Bioinformatics
|March 30, 2010
PubMed
Summary

Preclustering gene expression data reveals hidden patterns within heterogeneous Gene Ontology (GO) categories. This approach enhances the interpretability of biological data, uncovering specific cellular functions and developmental processes previously obscured by group averaging.

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Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Gene expression data analysis often summarizes profiles by Gene Ontology (GO) categories.
  • Heterogeneous expression profiles within GO classes can obscure important experimental findings.
  • A novel preclustering method is proposed to address this heterogeneity.

Purpose of the Study:

  • To improve the analysis of gene expression data by identifying homogeneous subclasses within GO categories.
  • To overcome the limitations of summarizing heterogeneous expression profiles.
  • To enhance the interpretability of biological findings from microarray data.

Main Methods:

  • Application of a preclustering technique to GO categories.
  • Analysis of two distinct microarray datasets: Saccharomyces cerevisiae and human Mesenchymal Stem Cells (MSC).
  • Utilizing Intra Cluster Correlation (ICC) to assess cluster tightness and relevance.

Main Results:

  • Preclustering enabled association of the "cell wall organization and biogenesis" GO class with specific cell cycle phases in yeast.
  • In human MSC differentiation, preclustering improved associations for the heterogeneous GO term "skeletal development".
  • Demonstrated improved identification of biologically relevant patterns in both datasets.

Conclusions:

  • The proposed preclustering method significantly enhances the interpretability of results from Principal Component Analysis (PCA).
  • This approach offers a more nuanced understanding of gene function and biological processes by dissecting heterogeneous GO categories.