Description Usage Arguments Details Value Author(s) References See Also Examples

View source: R/wrapper.sgcca.R

Wrapper function to perform Sparse Generalised Canonical Correlation Analysis (sGCCA), a generalised approach for the integration of multiple datasets. For more details, see the `help(sgcca)`

from the RGCCA package.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 |

`X` |
a list of data sets (called 'blocks') matching on the same samples. Data in the list should be arranged in samples x variables. |

`design` |
numeric matrix of size (number of blocks in X) x (number of blocks in X) with values between 0 and 1. Each value indicates the strenght of the relationship to be modelled between two blocks using sGCCA; a value of 0 indicates no relationship, 1 is the maximum value. If |

`penalty` |
numeric vector of length the number of blocks in |

`ncomp` |
the number of components to include in the model. Default to 1. |

`keepX` |
A vector of same length as X. Each entry keepX[i] is the number of X[[i]]-variables kept in the model. |

`scheme` |
Either "horst", "factorial" or "centroid" (Default: "horst"). |

`mode` |
character string. What type of algorithm to use, (partially) matching
one of |

`scale` |
boleean. If scale = TRUE, each block is standardized to zero means and unit variances (default: TRUE) |

`init` |
Mode of initialization use in the algorithm, either by Singular Value Decompostion of the product of each block of X with Y ("svd") or each block independently ("svd.single") . Default to "svd.single". |

`tol` |
Convergence stopping value. |

`max.iter` |
integer, the maximum number of iterations. |

`near.zero.var` |
boolean, see the internal |

`all.outputs` |
boolean. Computation can be faster when some specific (and non-essential) outputs are not calculated. Default = |

This wrapper function performs sGCCA (see RGCCA) with *1, … ,*`ncomp`

components on each block data set.
A supervised or unsupervised model can be run. For a supervised model, the `unmap`

function should be used as an input data set.
More details can be found on the package RGCCA.

Note that this function is the same as `block.spls`

with different default arguments.

More details about the PLS modes in `?pls`

.

`wrapper.sgcca`

returns an object of class `"sgcca"`

, a list
that contains the following components:

`data` |
the input data set (as a list). |

`design` |
the input design. |

`variates` |
the sgcca components. |

`loadings` |
the loadings for each block data set (outer wieght vector). |

`loadings.star` |
the laodings, standardised. |

`penalty` |
the input penalty parameter. |

`scheme` |
the input schme. |

`ncomp` |
the number of components included in the model for each block. |

`crit` |
the convergence criterion. |

`AVE` |
Indicators of model quality based on the Average Variance Explained (AVE): AVE(for one block), AVE(outer model), AVE(inner model).. |

`names` |
list containing the names to be used for individuals and variables. |

More details can be found in the references.

Arthur Tenenhaus, Vincent Guillemot and Kim-Anh Lê Cao.

Tenenhaus A. and Tenenhaus M., (2011), Regularized Generalized Canonical Correlation Analysis, Psychometrika, Vol. 76, Nr 2, pp 257-284.

Tenenhaus A., Phillipe C., Guillemot, V., Lê Cao K-A., Grill J., Frouin, V. Variable Selection For Generalized Canonical Correlation Analysis. 2013. (in revision)

`wrapper.sgcca`

, `plotIndiv`

, `plotVar`

, `wrapper.rgcca`

and http://www.mixOmics.org for more details.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 | ```
## Not run:
data(nutrimouse)
# need to unmap the Y factor diet if you pretend this is not a classification pb.
# see also the function block.splsda for discriminant analysis where you dont
# need to unmap Y.
Y = unmap(nutrimouse$diet)
data = list(gene = nutrimouse$gene, lipid = nutrimouse$lipid, Y = Y)
# with this design, gene expression and lipids are connected to the diet factor
# design = matrix(c(0,0,1,
# 0,0,1,
# 1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
# with this design, gene expression and lipids are connected to the diet factor
# and gene expression and lipids are also connected
design = matrix(c(0,1,1,
1,0,1,
1,1,0), ncol = 3, nrow = 3, byrow = TRUE)
#note: the penalty parameters will need to be tuned
wrap.result.sgcca = wrapper.sgcca(X = data, design = design, penalty = c(.3,.5, 1),
ncomp = 2,
scheme = "centroid")
wrap.result.sgcca
#did the algo converge?
wrap.result.sgcca$crit # yes
## End(Not run)
``` |

```
Loading required package: MASS
Loading required package: lattice
Loading required package: ggplot2
Loaded mixOmics 6.2.0
Visit http://www.mixOmics.org for more details about our methods.
Any bug reports or comments? Notify us at mixomics at math.univ-toulouse.fr or https://bitbucket.org/klecao/package-mixomics/issues
Thank you for using mixOmics!
Warning messages:
1: In rgl.init(initValue, onlyNULL) : RGL: unable to open X11 display
2: 'rgl_init' failed, running with rgl.useNULL = TRUE
3: .onUnload failed in unloadNamespace() for 'rgl', details:
call: fun(...)
error: object 'rgl_quit' not found
Call:
wrapper.sgcca(X = data, design = design, penalty = c(0.3, 0.5, 1), ncomp = 2, scheme = "centroid")
sGCCA with 2 components on block 1 named gene
sGCCA with 2 components on block 2 named lipid
sGCCA with 2 components on block 3 named Y
Dimension of block 1 is 40 120
Dimension of block 2 is 40 21
Dimension of block 3 is 40 5
Selection of 15 17 variables on each of the sGCCA components on the block 1
Selection of 7 7 variables on each of the sGCCA components on the block 2
Selection of 5 5 variables on each of the sGCCA components on the block 3
Main numerical outputs:
--------------------
loading vectors: see object$loadings
variates: see object$variates
variable names: see object$names
Functions to visualise samples:
--------------------
plotIndiv, plotArrow
Functions to visualise variables:
--------------------
plotVar, plotLoadings, network
Other functions:
--------------------
selectVar
[[1]]
[1] 9.736265 14.049702 14.567438 14.912354 15.097442 15.153714 15.165594
[8] 15.168647 15.169410 15.169594 15.169638 15.169648 15.169650 15.169651
[15] 15.169651 15.169651 15.169651 15.169651 15.169651 15.169651 15.169651
[22] 15.169651 15.169651 15.169651 15.169651 15.169651 15.169651 15.169651
[[2]]
[1] 13.86290 14.30810 14.35909 14.37165 14.37542 14.37654 14.37683 14.37690
[9] 14.37691 14.37692 14.37692 14.37692 14.37692 14.37692 14.37692 14.37692
[17] 14.37692 14.37692 14.37692 14.37692 14.37692 14.37692 14.37692 14.37692
```

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