Cesaire J. K. Fouodo
Recent technological advances have enabled the simultaneous collection of multi-omics data, i.e., different types or modalities of molecular data across various organ tissues of patients. For integrative predictive modeling, the analysis of such data is particularly challenging. Ideally, data from the different modalities are measured in the same individuals, allowing for early or intermediate integrative techniques. However, they are often not applicable when patient data only partially overlap, which requires either reducing the datasets or imputing missing values. Additionally, the characteristics of each data modality may necessitate specific statistical methods rather than applying the same method across all modalities. Late integration modeling approaches analyze each data modality separately to obtain modality-specific predictions. These predictions are then integrated to train aggregative models like Lasso, random forests, or compute the weighted mean of modality-specific predictions.
We introduce the R package fuseMLR for late integration prediction modeling. This comprehensive package enables users to define a training process with multiple data modalities and modality-specific machine learning methods. The package is user-friendly, facilitates variable selection and training of different models across modalities, and automatically performs aggregation once modality-specific training is completed. We simulated multi-omics data to illustrate the usage of our new package for conducting late-stage multi-omics integrative modeling.
fuseMLR is an object-oriented package based on R6 version 2.5.1.
Refer to the vignette (section Usage below) for a quick overview of
how to use the package.
The following figure illustrates the general architecture of fuseMLR:
The general architecture of fuseMLR includes the collection classes
Training, TrainLayer, and TrainMetaLayer. TrainLayer and
TrainMetaLayer are stored within a Training instance, while
TrainData, Lrner, and VarSel (for variable selection) are stored
within a TrainLayer or MetaTrainLayer instance. An Training object
can be used to automatically conduct layer-specific variable selection
and train layer-specfic learner and the meta-learner. Analogously, a
Testing can be set up and predicted after the training.
Install the release version from CRAN with
install.packages("fuseMLR")Install the development version from GitHub with
devtools::install_github("imbs-hl/fuseMLR")Refer to our vignette to understand how fuseMLR works.
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