The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods

Pingping Guan, Irini A. Doytchinova, Darren R. Flower

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

Abstract

Biological experiments often produce enormous amount of data, which are usually analyzed by data clustering. Cluster analysis refers to statistical methods that are used to assign data with similar properties into several smaller, more meaningful groups. Two commonly used clustering techniques are introduced in the following section: principal component analysis (PCA) and hierarchical clustering. PCA calculates the variance between variables and groups them into a few uncorrelated groups or principal components (PCs) that are orthogonal to each other. Hierarchical clustering is carried out by separating data into many clusters and merging similar clusters together. Here, we use an example of human leukocyte antigen (HLA) supertype classification to demonstrate the usage of the two methods. Two programs, Generating Optimal Linear Partial Least Square Estimations (GOLPE) and Sybyl, are used for PCA and hierarchical clustering, respectively. However, the reader should bear in mind that the methods have been incorporated into other software as well, such as SIMCA, statistiXL, and R.
Original languageEnglish
Title of host publicationImmunoioformatics
Subtitle of host publicationprediciting immunogenicity in silco
EditorsDarren R. Flower
Place of PublicationTotowa, NJ (US)
PublisherHumana Press
Pages143-154
Number of pages12
ISBN (Electronic)978-1-60327-118-9
ISBN (Print)978-1-58829-699-3
DOIs
Publication statusPublished - 16 Jul 2007

Publication series

NameMethods in molecular biology™
PublisherHumana Press
Volume409
ISSN (Print)1064-3745

Fingerprint

HLA Antigens
Cluster Analysis
Principal Component Analysis
Least-Squares Analysis
N-cyclopropyl adenosine-5'-carboxamide
Analysis of Variance
Software

Keywords

  • HLA
  • MHC
  • supertype
  • principal component analysis
  • hierarchical clustering
  • GOLPE

Cite this

Guan, P., Doytchinova, I. A., & Flower, D. R. (2007). The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods. In D. R. Flower (Ed.), Immunoioformatics: prediciting immunogenicity in silco (pp. 143-154). (Methods in molecular biology™; Vol. 409). Totowa, NJ (US): Humana Press. https://doi.org/10.1007/978-1-60327-118-9_9
Guan, Pingping ; Doytchinova, Irini A. ; Flower, Darren R. / The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods. Immunoioformatics: prediciting immunogenicity in silco. editor / Darren R. Flower. Totowa, NJ (US) : Humana Press, 2007. pp. 143-154 (Methods in molecular biology™).
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Guan, P, Doytchinova, IA & Flower, DR 2007, The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods. in DR Flower (ed.), Immunoioformatics: prediciting immunogenicity in silco. Methods in molecular biology™, vol. 409, Humana Press, Totowa, NJ (US), pp. 143-154. https://doi.org/10.1007/978-1-60327-118-9_9

The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods. / Guan, Pingping; Doytchinova, Irini A.; Flower, Darren R.

Immunoioformatics: prediciting immunogenicity in silco. ed. / Darren R. Flower. Totowa, NJ (US) : Humana Press, 2007. p. 143-154 (Methods in molecular biology™; Vol. 409).

Research output: Chapter in Book/Report/Conference proceedingChapter (peer-reviewed)

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Guan P, Doytchinova IA, Flower DR. The classification of HLA supertypes by GRID/CPCA and hierarchical clustering methods. In Flower DR, editor, Immunoioformatics: prediciting immunogenicity in silco. Totowa, NJ (US): Humana Press. 2007. p. 143-154. (Methods in molecular biology™). https://doi.org/10.1007/978-1-60327-118-9_9