Creates an object that can be used as input for the (s)PLS functions from the mixOmics package. It contains the two omics datasets selected, restricted to common samples.
Usage
get_input_spls(mo_data, mode, datasets = names(mo_data), multilevel = NULL)Arguments
- mo_data
A MultiDataSet::MultiDataSet object.
- mode
Character, the mode of PLS to use of the analysis (see sPLS documentation). Should be one of
'regression','canonical','invariant'or'classic'.- datasets
Character vector of length 2, the names of the datasets from
mo_datato include in the analysis.- multilevel
Character vector of length 1 or 3 to be used as information about repeated measurements. See Details. Default value is
NULL, i.e. the multilevel option will not be used.
Value
A list, in which each element corresponds to one omics dataset, with
samples as rows and features as columns. The mode to use for the analysis
is stored in the mode attribute of the returned object.
Details
multilevel argument: enables the multilevel option (see mixOmics site) to deal with repeated
measurements. mixOmics::spls() enables one- and two-factor decomposition.
For one-factor decomposition, multilevel argument should be the name of the
column in the samples metadata that gives the ID of the observation units
(e.g. the ID of the subjects that were measured several times). The resulting
design matrix (stored in the multilevel argument of the returned object)
will be a data-frame with one column which gives the ID (as integer) of the
observation units corresponding to each sample in the omics datasets. For
two-factor decomposition, multilevel should be of length 3. The first
value, similarly to the one-factor decomposition, should be the name of the
column in the samples metadata that gives the ID of the observation units
(e.g. the ID of the subjects that were measured several times). The second
and third values should be the name of the columns in the samples metadata
that give the two factors considered. The resulting design matrix (stored in
the multilevel argument of the returned object) will be a data-frame with
three columns: the first column gives the ID (as integer) of the observation
units corresponding to each sample in the omics datasets; the second and
third columns give the levels of the two factors.