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

View source: R/mint.block.plsda.R

Function to integrate data sets measured on the same samples (N-integration) and to combine multiple independent studies measured on the same variables or predictors (P-integration) using variants of multi-group and generalised PLS-DA for supervised classification.

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

`X` |
A list of data sets (called 'blocks') measured on the same samples. Data in the list should be arranged in samples x variables, with samples order matching in all data sets. |

`Y` |
A factor or a class vector indicating the discrete outcome of each sample. |

`indY` |
To be supplied if Y is missing, indicates the position of the matrix / vector response in the list |

`study` |
factor indicating the membership of each sample to each of the studies being combined |

`ncomp` |
Number of components to include in the model (see Details). Default to 2. |

`design` |
numeric matrix of size (number of blocks in X) x (number of blocks in X) with 0 or 1 values. A value of 1 (0) indicates a relationship (no relationship) between the blocks to be modelled. If |

`scheme` |
Either "horst", "factorial" or "centroid". Default = |

`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 = |

`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 = |

The function fits multi-group generalised PLS models with a specified number of `ncomp`

components.
A factor indicating the discrete outcome needs to be provided, either by `Y`

or by its position `indY`

in the list of blocks `X`

.

`X`

can contain missing values. Missing values are handled by being disregarded during the cross product computations in the algorithm `block.pls`

without having to delete rows with missing data. Alternatively, missing data can be imputed prior using the `nipals`

function.

The type of algorithm to use is specified with the `mode`

argument. Four PLS
algorithms are available: PLS regression `("regression")`

, PLS canonical analysis
`("canonical")`

, redundancy analysis `("invariant")`

and the classical PLS
algorithm `("classic")`

(see References and more details in `?pls`

).

`mint.block.plsda`

returns an object of class `"mint.plsda", "block.plsda"`

, a list
that contains the following components:

`X` |
the centered and standardized original predictor matrix. |

`Y` |
the centered and standardized original response vector or matrix. |

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

`mode` |
the algorithm used to fit the model. |

`mat.c` |
matrix of coefficients from the regression of X / residual matrices X on the X-variates, to be used internally by |

`variates` |
list containing the |

`loadings` |
list containing the estimated loadings for the variates. |

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

`nzv` |
list containing the zero- or near-zero predictors information. |

`tol` |
the tolerance used in the iterative algorithm, used for subsequent S3 methods |

`max.iter` |
the maximum number of iterations, used for subsequent S3 methods |

`iter` |
Number of iterations of the algorthm for each component |

Florian Rohart, Benoit Gautier, Kim-Anh Lê Cao

On multi-group PLS:

Rohart F, Eslami A, Matigian, N, Bougeard S, Lê Cao K-A (2017). MINT: A multivariate integrative approach to identify a reproducible biomarker signature across multiple experiments and platforms. BMC Bioinformatics 18:128.

Eslami, A., Qannari, E. M., Kohler, A., and Bougeard, S. (2014). Algorithms for multi-group PLS. J. Chemometrics, 28(3), 192-201.

On multiple integration with PLSDA:

Singh A., Gautier B., Shannon C., Vacher M., Rohart F., Tebbutt S. and Lê Cao K.A. (2016).
DIABLO: multi omics integration for biomarker discovery. BioRxiv available here: http://biorxiv.org/content/early/2016/08/03/067611
Tenenhaus A., Philippe C., Guillemot V, Lê Cao K.A., Grill J, Frouin V. Variable selection for generalized canonical correlation analysis. *Biostatistics*. kxu001

Gunther O., Shin H., Ng R. T. , McMaster W. R., McManus B. M. , Keown P. A. , Tebbutt S.J. , Lê Cao K-A. , (2014) Novel multivariate methods for integration of genomics and proteomics data: Applications in a kidney transplant rejection study, OMICS: A journal of integrative biology, 18(11), 682-95.

mixOmics article:

Rohart F, Gautier B, Singh A, Lê Cao K-A. mixOmics: an R package for 'omics feature selection and multiple data integration. PLoS Comput Biol 13(11): e1005752

`spls`

, `summary`

,
`plotIndiv`

, `plotVar`

, `predict`

, `perf`

, `mint.block.spls`

, `mint.block.plsda`

, `mint.block.splsda`

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

1 2 | ```
# we will soon provide more examples on our website (data too large to be included in the package
# and still in active development)
``` |

```
Loading required package: MASS
Loading required package: lattice
Loading required package: ggplot2
Loaded mixOmics 6.3.2
Thank you for using mixOmics!
How to apply our methods: http://www.mixOmics.org for some examples.
Questions or comments: email us at mixomics[at]math.univ-toulouse.fr
Any bugs? https://bitbucket.org/klecao/package-mixomics/issues
Cite us: citation('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
```

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