Chris Walsh <Chris.Walsh@sci.monash.edu.au> and
Ralph MacNally ,Ralph.MacNally@sci.monash.edu.au>
Variance partition of a multivariate data set

Chris Walsh <Chris.Walsh@sci.monash.edu.au> and
Ralph Mac Nally <Ralph.MacNally@sci.monash.edu.au>

Chris Walsh <cwalsh@unimelb.edu.au> and Ralph Mac Nally
<Ralph.MacNally@sci.monash.edu.au>

c(person("Chris", "Walsh", role = c("aut","cre"), email =
"cwalsh@unimelb.edu.au"), person("Ralph", "Mac Nally", role =
"aut", email = "Ralph.MacNally@sci.monash.edu.au"))

Partitioning of the independent and joint contributions of each
variable in a multivariate data set, to a linear regression by hierarchical
decomposition of goodness-of-fit measures of regressions using all subsets
of predictors in the data set. (i.e., model (1), (2), ..., (N), (1,2), ...,
(1,N), ..., (1,2,3,...,N)). A Z-score based estimate of the 'importance' of
each predictor is provided by using a randomisation test.
c(person("Chris", "Walsh", role = c("aut","cre"),
email = "cwalsh@unimelb.edu.au"),
person("Ralph", "Mac Nally", role = "aut",
email = "Ralph.MacNally@gmail.com"))
Chris Walsh [aut, cre],
Ralph Mac Nally [aut]

Chris Walsh <Chris.Walsh@sci.monash.edu.au> and
Ralph MacNally <Ralph.MacNally@sci.monash.edu.au>

c(person("Calvin", "Chi", email = "calvin.chi@berkeley.edu", role = c("aut", "cre", "cph")),
person("Woojoo", "Lee", role = c("ctb")),
person("Donghwan", "Lee", role = c("ctb")),
person("Youngjo", "Lee", role = c("ctb")),
person("Yudi", "Pawitan", role = c("ctb")))
Bipartite graph-based hierarchical clustering, developed for pharmacogenomic
datasets and datasets sharing the same data structure. The goal is to
construct a hierarchical clustering of groups of samples based on
association patterns between two sets of variables. In the context of
pharmacogenomic datasets, the samples are cell lines, and the two sets of
variables are typically expression levels and drug sensitivity values.
For this method, sparse canonical correlation analysis from
Lee, W., Lee, D., Lee, Y. and Pawitan, Y. (2011) <doi:10.2202/1544-6115.1638>
is first applied to extract association patterns for each group of samples.
Then, a nuclear norm-based dissimilarity measure is used to construct a
dissimilarity matrix between groups based on the extracted associations.
Finally, hierarchical clustering is applied.
Calvin Chi [aut, cre, cph],
Woojoo Lee [ctb],
Donghwan Lee [ctb],
Youngjo Lee [ctb],
Yudi Pawitan [ctb]