PathME: pathway based multi-modal sparse autoencoders for clustering of patient-level multi-omics data

Background Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and he...

Celý popis

Uložené v:

Podrobná bibliografia
Vydané v:	BMC bioinformatics Ročník 21; číslo 1; s. 146 - 20
Hlavní autori:	Lemsara, Amina, Ouadfel, Salima, Fröhlich, Holger
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	London BioMed Central 16.04.2020 BioMed Central Ltd Springer Nature B.V BMC
Predmet:	Algorithms Analysis Bias Bioinformatics Biomedical and Life Sciences Cancer Clustering Computational Biology/Bioinformatics Computer Appl. in Life Sciences Deep learning Deoxyribonucleic acid DNA DNA methylation Gene expression Generalized linear models Genetic research Learning algorithms Life Sciences Machine learning Machine Learning and Artificial Intelligence in Bioinformatics Machine learning for computational and systems biology Medical treatment Methodology Methodology Article Methods Methylation Microarrays miRNA Multi-omics Mutation Noise reduction Patient clustering Patients Ribonucleic acid RNA Sensory integration Sparsity Subgroups Therapeutics Deep learning Multi-omics Patient clustering
ISSN:	1471-2105, 1471-2105
On-line prístup:	Získať plný text
Tagy:	Pridať tag Žiadne tagy, Buďte prvý, kto otaguje tento záznam!

Popis
Shrnutí:	Background Recent years have witnessed an increasing interest in multi-omics data, because these data allow for better understanding complex diseases such as cancer on a molecular system level. In addition, multi-omics data increase the chance to robustly identify molecular patient sub-groups and hence open the door towards a better personalized treatment of diseases. Several methods have been proposed for unsupervised clustering of multi-omics data. However, a number of challenges remain, such as the magnitude of features and the large difference in dimensionality across different omics data sources. Results We propose a multi-modal sparse denoising autoencoder framework coupled with sparse non-negative matrix factorization to robustly cluster patients based on multi-omics data. The proposed model specifically leverages pathway information to effectively reduce the dimensionality of omics data into a pathway and patient specific score profile. In consequence, our method allows us to understand, which pathway is a feature of which particular patient cluster. Moreover, recently proposed machine learning techniques allow us to disentangle the specific impact of each individual omics feature on a pathway score. We applied our method to cluster patients in several cancer datasets using gene expression, miRNA expression, DNA methylation and CNVs, demonstrating the possibility to obtain biologically plausible disease subtypes characterized by specific molecular features. Comparison against several competing methods showed a competitive clustering performance. In addition, post-hoc analysis of somatic mutations and clinical data provided supporting evidence and interpretation of the identified clusters. Conclusions Our suggested multi-modal sparse denoising autoencoder approach allows for an effective and interpretable integration of multi-omics data on pathway level while addressing the high dimensional character of omics data. Patient specific pathway score profiles derived from our model allow for a robust identification of disease subgroups.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1471-2105 1471-2105
DOI:	10.1186/s12859-020-3465-2