Information Technology Reference
In-Depth Information
Since the development of biclustering algorithms, many softwares are designed
to include several algorithms, including BicAT [2], BicOverlapper [48], BiVisu [10],
toolbox by R(biclust) [32] etc. These software or packages allow to do data pro-
cessing, bicluster analysis, and visualization of results and can be used directly to
construct images.
In the toolbox named BicAT [2], it provides different facilities for data prepara-
tion, inspection, and postprocessing such as discretization, filtering of biclusters ac-
cordingly. Several algorithms of biclustering such as Bimax, CC, XMotifs, OPSM
are included, and three methods of viewing data including matrix (heatmap), ex-
pression, and analysis are presented. The software BicOverlapper [48] is a tool for
overlapping biclusters visualization. It can use three different kinds of data files
of original data matrix and resulted biclusters to construct beautiful and colorful
images such as heatmaps, parallel coordinates, TRN graph, bubble map, and over-
lapper. The BiVisu [10] is also a software tool for bicluster detection and visualiza-
tion. Besides bicluster detection, BiVisu also provides functions for preprocessing,
filtering, and bicluster analysis. Another software is a package written by R [32], bi-
clust, which contains a collection of bicluster algorithms, such as Bimax, CC, plaid,
spectral, xMotifs, preprocessing methods for two way data, and validation and visu-
alization techniques for bicluster results. For individual biclustering software, there
are also some packages available [55, 5].
6.1.5 Outline
In this chapter, we will follow the reviews of [37, 55, 5] and try to include the most
recent algorithms and advancements of biclustering. The perspective of this chapter
is of mathematical view, including linear algebra, optimization programming, bipar-
tite graphs, probabilistic or statistical models, information theory, and time series.
Section 6.1 has reviewed the motivation, data, objective, history, and softwares of
biclustering. In Section 6.2, the bicluster type and biclustering structures are for-
mally defined in a mathematical way. The most recent biclustering algorithms are
reviewed in Section 6.3 and a comparison score is also defined. The application
of biclustering in computational neuroscience will be reviewed in Section 6.4 and
conclusions and future works are in Section 6.5.
6.2 Biclustering Types and Structures
6.2.1 Notations
As mentioned in Section 6.1.2, the expression matrix is mostly used in biclustering.
Let
A
a
ij
)
n
×
m
denote the sample-feature expression matrix, where there are
n
rows representing
n
samples,
m
columns representing
m
features, and the entry
a
ij
=(
