R Classes and Methods for SNP Array Data
Last Update: 2021-03-04
Search more information of high quality chemicals, good prices and reliable suppliers, visit
The Bioconductor project is an “open source and open development software project for the analysis and comprehension of genomic data” (1), primarily based on the R programming language. Infrastructure packages, such as Biobase, are maintained by Bioconductor core developers and serve several key roles to the broader community of Bioconductor software developers and users. In particular, Biobase introduces an S4 class, the eSet, for high-dimensional assay data. Encapsulating the assay data as well as meta-data on the samples, features, and experiment in the eSet class definition ensures propagation of the relevant sample and feature meta-data throughout an analysis. Extending the eSet class promotes code reuse through inheritance as well as interoperability with other R packages and is less error-prone. Recently proposed class definitions for high-throughput SNP arrays extend the eSet class. This chapter highlights the advantages of adopting and extending Biobase class definitions through a working example of one implementation of classes for the analysis of high-throughput SNP arrays.
This article is an English version of an article which is originally in the Chinese language on echemi.com and is provided for information purposes only.
This website makes no representation or warranty of any kind, either expressed or implied, as to the accuracy, completeness ownership or reliability of
the article or any translations thereof. If you have any concerns or complaints relating to the article, please send an email, providing a detailed
description of the concern or complaint, to firstname.lastname@example.org
. A staff member will contact you within 5 working days. Once verified, infringing content
will be removed immediately.