When studying omics data, it is common to have more than two datasets
measured over the same samples. For instance, the miRNA, mRNA and
protein expresssion levels may have been taken for each individual
within the study (as in the breast.TCGA dataset). The
methods featured in the ‘Single Omics’ and ‘Multi omics’ sections cannot
address all three datasets at the same time.
A generalised form of PLS (and its supervised counterpart, PLS-DA) is
utilised within the mixOmics package to achieve
N-integration - integration of two or more datasets that are measured
across the same \(N\) samples. The aim
is to identify correlated variables across these datasets, and in a
supervised analysis, to explain the categorical outcome.
mixOmics features multiblock PLS as the unsupervised
approach and multiblock PLS-DA (referred to as DIABLO) as the supervised
method. Both these techniques have sparse variants as in many omics
contexts feature selection is important. All these methods extend the
generalised Canonical Correlation Analysis (gCCA) [1] and sparse gCCA
[2] from the RGCCA package to this integrative
framework.
It is strongly advised that prior to the use of any N-integrative method, users begin individual and pairwise analyses of their data using the standard forms of these methods (i.e. (s)PLS). This will provide useful insight into the structure and major sources of variation within the data and will guide the more complex decisions required when extending into N-integrative methods.
When undergoing any method within mixOmics, the user
should be considering the biological question under inspection. This is
especially true when using the N-integrative framework, such that the
‘design’ of the model can be specified. ‘Design’ refers to the
relationship structure between the various inputted dataframes. As a
functional parameter, this is a matrix, where each value (between 0-1)
represents the strength of the relationship to be modeled between two
given dataframes. For the breast.TCGA data which contains
three dataframes:
design = matrix(1, ncol = 3, nrow = 3,
dimnames = list(c("mirna", "mrna", "protein"),
c("mirna", "mrna", "protein")))
diag(design) = 0
design## mirna mrna protein
## mirna 0 1 1
## mrna 1 0 1
## protein 1 1 0This is the default matrix that is used, as can be specified by
setting design = "full". Similarly, setting
design = "null" will produce a matrix full of zeroes. Note
that the diagonal is all set to zeroes so that the relationship of a
dataset to itself is not considered.
Inputting design = 0.5 will produce the following
matrix, and works for any value between 0 and 1:
## mirna mrna protein
## mirna 0.0 0.5 0.5
## mrna 0.5 0.0 0.5
## protein 0.5 0.5 0.0In multiblock (s)PLS, if Y is provided instead of
indY, the design matrix needs to be adjusted
to include the relationship of each X dataset with
Y.