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

Function to integrate and combine multiple independent studies measured on the same variables or predictors (P-integration) using variants of multi-group PLS (unsupervised analysis).

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

`X` |
numeric matrix of predictors combining multiple independent studies
on the same set of predictors. |

`Y` |
Matrix or vector response for a multivariate regression framework.
Data should be continuous variables (see |

`ncomp` |
Integer, the number of components to include in the model. Default to 2. |

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

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

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

`tol` |
Numeric, convergence stopping value. |

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

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

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

`mint.pls`

fits a vertical PLS-DA models with `ncomp`

components
in which several independent studies measured on the same variables are
integrated. The aim is to explain the continuous outcome `Y`

. The
`study`

factor indicates the membership of each sample in each study.
We advise to only combine studies with more than 3 samples as the function
performs internal scaling per study.

Multi (continuous)response are supported. `X`

and `Y`

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

).

Useful graphical outputs are available, e.g. `plotIndiv`

,
`plotLoadings`

, `plotVar`

.

`mint.pls`

returns an object of class `"mint.pls", "pls"`

,
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. |

`study` |
The study grouping factor |

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

`variates` |
list containing the variates of X - global variates. |

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

`variates.partial` |
list containing the variates of X relative to each study - partial variates. |

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

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

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

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

`explained_variance` |
Percentage of explained variance for each component and each study (note that contrary to PCA, this amount may not decrease as the aim of the method is not to maximise the variance, but the covariance between data sets). |

Florian Rohart, Kim-Anh Lê Cao, Al J Abadi

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.

`spls`

, `summary`

, `plotIndiv`

,
`plotVar`

, `predict`

, `perf`

,
`mint.spls`

, `mint.plsda`

, `mint.splsda`

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

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 | ```
data(stemcells)
# for the purpose of this example, we artificially
# create a continuous response Y by taking gene 1.
res = mint.pls(X = stemcells$gene[,-1], Y = stemcells$gene[,1], ncomp = 3,
study = stemcells$study)
plotIndiv(res)
#plot study-specific outputs for all studies
plotIndiv(res, study = "all.partial")
## Not run:
#plot study-specific outputs for study "2"
plotIndiv(res, study = "2", col = 1:3, legend = TRUE)
## End(Not run)
``` |

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