Data Structures
One of the best features of Bioconductor is the use of structured data to represent biological concepts. This section presents a few important classes that are used through Bioconductor.
Bioconductor classes are implemented using formal class methods;
see Chapter 10 for more details. Most of these
classes inherit from the basic classes in the Biobase package, so you can use the same
methods to work with different types of objects. For example, you can
use the same method to read phenotype data for expression data from
different vendors (such as Affymetrix arrays and Illumina arrays). You
could also use the same method to read phenotype data for expression
data from completely different types of data (such as gene expression
data and proteomic data).
Objects in Bioconductor contain many different types of
information about an experiment: the experimental platform,
information about the samples, information about the phenotypes, the
experimental results, and almost anything else that is relevant for
describing an experiment or the results of the experiment. Classes
defined in the Biobase package
provide a general framework that fits many different types of
experimental data. Classes defined in other packages can be used to
represent data from specific types of microarrays, often for specific
products from specific vendors. This section contains descriptions of
a few key classes defined in Biobase.
eSet
eSet is a virtual class that is used by many Bioconductor functions. ...
Get R in a Nutshell now with the O’Reilly learning platform.
O’Reilly members experience books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.
