Unsupervised multiple kernel learning for heterogeneous data integration

Abstract

Motivation: Recent high-throughput sequencing advances have expanded the breadth of available omics datasets and the integrated analysis of multiple datasets obtained on the same samples has allowed to gain important insights in a wide range of applications. However, the integration of various sources of information remains a challenge for systems biology since produced datasets are often of heterogeneous types, with the need of developing generic methods to take their different specificities into account. Results: We propose a multiple kernel framework that allows to integrate multiple datasets of various types into a single exploratory analysis. Several solutions are provided to learn either a consensus metakernel or a meta-kernel that preserves the original topology of the datasets. We applied our framework to analyse two public multi-omics datasets. First, the multiple metagenomic datasets, collected during the TARA Oceans expedition, was explored to demonstrate that our method is able to retrieve previous findings in a single KPCA as well as to provide a new image of the sample structures when a larger number of datasets are included in the analysis. To perform this analysis, a generic procedure is also proposed to improve the interpretability of the kernel PCA in regards with the original data. Second, the multi-omics breast cancer datasets, provided by The Cancer Genome Atlas, is analysed using a kernel Self-Organizing Maps with both single and multi-omics strategies. The comparison of this two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. Contact: jerome.mariette@inra.fr and nathalie.villa-vialaneix@inra.fr Availability and implementation: Proposed methods are available in the R package mixKernel, released on CRAN. It is fully compatible with the mixOmics package and a tutorial describing the approach can be found on mixOmics web site http://mixomics.org/mixkernel/.

A multiple kernel density clustering algorithm for incomplete datasets in bioinformatics

“…However, the class labels of training data samples may not always be available prior to execute the MKL task in some real-world scenarios, such as clustering and dimension reduction. Unsupervised Multiple Kernel Learning(UMKL) determines a linear combination of multiple basis kernels by learning from unlabeled data samples, and the generated kernel can be used in data mining, such as clustering and classifying, as it is supposed to provide an integrated feature of input datasets [ 31 ]. Thus, to apply multiple kernels to clustering, MKDCI obtain an optimal kernel by the UMKL method.…”

“…To apply the UMKL method, the input dataset is split into a training set and a test set with the ratio of 70:30 by randomly sampling, i.e., they account for 70% and 30% of entire input dataset respectively. According to each predefined basis kernel, m kernel matrices are computed for the training data samples, the parameters γ 1 and B are estimated by cross-validation on the training data samples, and the above optimization problem can be solved with the algorithm discussed in [ 31 ]. Thus, by training on the unlabeled input dataset with the UMKL method, an optimally combined kernel k (·,·) with the weights of the predefined basis kernels μ t are learned.…”

MiBiOmics: An interactive web application for multi-omics data exploration and integration

“…In this section, it is also possible to detect and remove potential outlier samples. To allow new users to easily test the functionality of MiBiOmics, we provide two example datasets: the breast TGCA datasets from The Cancer Genome Atlas [8] allows to explore assocations between miRNAs, mRNAs and proteins in different breast cancer subtypes; and a dataset from the Tara Oceans Expeditions [9,10] to explore prokaryotic community compositions across depth and geographic locations.…”

Heterogeneous multiple kernel learning for breast cancer outcome evaluation

“…Multiple kernel learning (MKL) algorithms have been proved to be effective tools to solve learning problems such as classification or regression. Jérôme Mariette et al [11] applied MKL on breast cancer heterogeneous data and achieved a good performance through the experiments. Arezou et al [12] proposed an MKL method, which employs the gene expression profiles to predict cancer and achieves a satisfactory predictive performance.…”