Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Vahabi, Nasim; Michailidis, George

doi:10.3389/fgene.2022.854752

Cited by 69 publications

(43 citation statements)

References 114 publications

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“…MCIA is considered one of the best-performing algorithms for the benchmarking of joint multi-omics dimensionality reduction approaches in the case of cancer datasets and provides an effective elucidation of relationships among the studied datasets ( Cantini et al, 2021 ). Moreover, it can be considered a variation of the canonical correspondence analysis (CCA) method ( Vahabi and Michailidis, 2022 ). The applicability of other CCA extensions could also be tested for simultaneous feature selection and classification in microbial datasets in the future.…”

Section: Discussionmentioning

confidence: 99%

Application of data integration for rice bacterial strain selection by combining their osmotic stress response and plant growth-promoting traits

Devarajan

Truu²,

Gopalasubramaniam³

et al. 2022

Front. Microbiol.

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Agricultural application of plant-beneficial bacteria to improve crop yield and alleviate the stress caused by environmental conditions, pests, and pathogens is gaining popularity. However, before using these bacterial strains in plant experiments, their environmental stress responses and plant health improvement potential should be examined. In this study, we explored the applicability of three unsupervised machine learning-based data integration methods, including principal component analysis (PCA) of concatenated data, multiple co-inertia analysis (MCIA), and multiple kernel learning (MKL), to select osmotic stress-tolerant plant growth-promoting (PGP) bacterial strains isolated from the rice phyllosphere. The studied datasets consisted of direct and indirect PGP activity measurements and osmotic stress responses of eight bacterial strains previously isolated from the phyllosphere of drought-tolerant rice cultivar. The production of phytohormones, such as indole-acetic acid (IAA), gibberellic acid (GA), abscisic acid (ABA), and cytokinin, were used as direct PGP traits, whereas the production of hydrogen cyanide and siderophore and antagonistic activity against the foliar pathogens Pyricularia oryzae and Helminthosporium oryzae were evaluated as measures of indirect PGP activity. The strains were subjected to a range of osmotic stress levels by adding PEG 6000 (0, 11, 21, and 32.6%) to their growth medium. The results of the osmotic stress response experiments showed that all bacterial strains accumulated endogenous proline and glycine betaine (GB) and exhibited an increase in growth, when osmotic stress levels were increased to a specific degree, while the production of IAA and GA considerably decreased. The three applied data integration methods did not provide a similar grouping of the strains. Especially deviant was the ordination of microbial strains based on the PCA of concatenated data. However, all three data integration methods indicated that the strains Bacillus altitudinis PB46 and B. megaterium PB50 shared high similarity in PGP traits and osmotic stress response. Overall, our results indicate that data integration methods complement the single-table data analysis approach and improve the selection process for PGP microbial strains.

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Section: Discussionmentioning

confidence: 99%

Application of data integration for rice bacterial strain selection by combining their osmotic stress response and plant growth-promoting traits

Devarajan

Truu²,

Gopalasubramaniam³

et al. 2022

Front. Microbiol.

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“…Previous multi-omics integration methods have been focused mostly on feature extraction and matrix factorization strategies. While the inferred latent factors have been found accurate in stratifying samples into clusters and can be predictive of clinical outcome, there is little evidence that the discovered factors would represent actual signaling pathways in the cell [10,11].…”

Section: Discussionmentioning

confidence: 99%

“…Unfortunately, the cost and feasibility of acquiring time-course data present a limitation especially in patient samples and in animal models. 4 The current array of multi-omics integration methods mainly consist of dimensionality reduction and clustering methods that aim to stratify multi-omics data according to disease subtype or patient characteristics and assist in biomarker discovery [10][11][12]. The module discovery methods such as WGCNA and correlation networks have been expanded and applied to multi-omics data [13,14], but beyond these efforts the multi-omics integration methods that aim to learn cell signaling networks seem to rely on previous knowledge [15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

De Novo multi-omics pathway analysis (DMPA) designed for prior data independent inference of cell signaling pathways

Vaparanta

Merilahti

Ojala

et al. 2022

Preprint

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New tools for unbiased cell signaling pathway inference from multi-omics data are needed. Here we propose a new unbiased method, the unbiased pathway analysis (UPA), to model and combine omics data into regulatory complexes and pathways. UPA was validated with publicly available omics data and was found accurate in discovering protein-protein interactions, kinase substrate phosphosite relationships, transcription factor target gene relationships, and signaling pathways. Transcription factor, kinase, subcellular location and function prediction algorithms were devised for transcriptome, phosphoproteome and interactome regulatory complexes and pathways, respectively. To apply UPA in a biologically relevant context, interactome, phosphoproteome, transcriptome and proteome data were collected from analyses carried out using melanoma cells to address gamma-secretase cleavage-dependent signaling characteristics of the receptor tyrosine kinase TYRO3. Significant differences were discovered between full-length and gamma-secretase cleaved TYRO3 when signaling modules associated with cell cycle and growth, cell adhesion and motility, cell morphology, or immune responses were compared. The pathways modeled with UPA reflected both the predicted function and direction of the predicted function in validation experiments.

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“…Therefore, to demonstrate the incompleteness of the available information, we mainly focus on the most advanced areas, using high-throughput NGS technologies. Interested readers can find information on multi-omics in the latest reviews [ 18 , 19 , 20 , 21 ]. We use two of the best-studied organisms, E. coli and C. elegans , to provide illustrative insight into the degree of data incompleteness.…”

Section: Incompleteness Of Genomic Datamentioning

confidence: 99%

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

Kondratyeva

Alekseenko

Чернов

2022

Biology

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In this brief review, we attempt to demonstrate that the incompleteness of data, as well as the intrinsic heterogeneity of biological systems, may form very strong and possibly insurmountable barriers for researchers trying to decipher the mechanisms of the functioning of live systems. We illustrate this challenge using the two most studied organisms: E. coli, with 34.6% genes lacking experimental evidence of function, and C. elegans, with identified proteins for approximately 50% of its genes. Another striking example is an artificial unicellular entity named JCVI-syn3.0, with a minimal set of genes. A total of 31.5% of the genes of JCVI-syn3.0 cannot be ascribed a specific biological function. The human interactome mapping project identified only 5–10% of all protein interactions in humans. In addition, most of the available data are static snapshots, and it is barely possible to generate realistic models of the dynamic processes within cells. Moreover, the existing interactomes reflect the de facto interaction but not its functional result, which is an unpredictable emerging property. Perhaps the completeness of molecular data on any living organism is beyond our reach and represents an unsolvable problem in biology.

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Unsupervised Multi-Omics Data Integration Methods: A Comprehensive Review

Cited by 69 publications

References 114 publications

Application of data integration for rice bacterial strain selection by combining their osmotic stress response and plant growth-promoting traits

Application of data integration for rice bacterial strain selection by combining their osmotic stress response and plant growth-promoting traits

De Novo multi-omics pathway analysis (DMPA) designed for prior data independent inference of cell signaling pathways

Data Incompleteness May form a Hard-to-Overcome Barrier to Decoding Life’s Mechanism

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