Name
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 meta-kernel 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 kernel PCA 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 these two approaches demonstrates the benefit of our integration method to improve the representation of the studied biological system. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1093/bioinformatics/btx682 | BIOINFORMATICS |
Field | DocType | Volume |
Data integration,Data mining,Text mining,Computer science,Multiple kernel learning,Bioinformatics | Journal | 34 |
Issue | ISSN | Citations |
6 | 1367-4803 | 4 |
PageRank | References | Authors |
0.41 | 8 | 2 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jérôme Mariette | 1 | 9 | 2.18 |
| Nathalie Villa-Vialaneix | 2 | 72 | 10.94 |