Predicting microbiomes through a deep latent space

Predicting microbiomes through a deep latent space. García-Jiménez, B., Muñoz, J., Cabello, S., Medina, J., & Wilkinson, M., D. bioRxiv, 2020.

Website doi abstract bibtex 15 downloads

Motivation Microbial communities influence their environment by modifying the availability of compounds such as nutrients or chemical elicitors. Knowing the microbial composition of a site is therefore relevant to improving productivity or health. However, sequencing facilities are not always available, or may be prohibitively expensive in some cases. Thus, it would be desirable to computationally predict the microbial composition from more accessible, easily-measured features.Results Integrating Deep Learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance values into a deep latent space representation. Then, we design a model to predict the deep latent space and, consequently, to predict the complete microbial composition using environmental features as input. The performance of our system is examined using the rhizosphere microbiome of Maize. We reconstruct the microbial composition (717 taxa) from the deep latent space (10 values) with high fidelity (¿0.9 Pearson correlation). We then successfully predict microbial composition from environmental variables such as plant age, temperature or precipitation (0.73 Pearson correlation, 0.42 Bray-Curtis). We extend this to predict microbiome composition under hypothetical scenarios, such as future climate change conditions. Finally, via transfer learning, we predict microbial composition in a distinct scenario with only a hundred sequences, and distinct environmental features. We propose that our deep latent space may assist microbiome-engineering strategies when technical or financial resources are limited, through predicting current or future microbiome compositions.Availability Software, results, and data are available at https://github.com/jorgemf/DeepLatentMicrobiomeCompeting Interest StatementThe authors have declared no competing interest.

@article{
 title = {Predicting microbiomes through a deep latent space},
 type = {article},
 year = {2020},
 pages = {2020.04.27.063974},
 websites = {http://biorxiv.org/content/early/2020/04/28/2020.04.27.063974.abstract},
 day = {28},
 id = {1afe7444-ae88-366b-adfe-dd830cad559e},
 created = {2020-08-12T08:24:06.771Z},
 file_attached = {false},
 profile_id = {fa910c8b-8889-3a42-afc9-302da7e3933a},
 group_id = {ff1f9038-dd83-321a-9605-910d757253bb},
 last_modified = {2020-08-12T08:24:06.771Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {true},
 hidden = {false},
 source_type = {Preprint},
 private_publication = {false},
 abstract = {Motivation Microbial communities influence their environment by modifying the availability of compounds such as nutrients or chemical elicitors. Knowing the microbial composition of a site is therefore relevant to improving productivity or health. However, sequencing facilities are not always available, or may be prohibitively expensive in some cases. Thus, it would be desirable to computationally predict the microbial composition from more accessible, easily-measured features.Results Integrating Deep Learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance values into a deep latent space representation. Then, we design a model to predict the deep latent space and, consequently, to predict the complete microbial composition using environmental features as input. The performance of our system is examined using the rhizosphere microbiome of Maize. We reconstruct the microbial composition (717 taxa) from the deep latent space (10 values) with high fidelity (¿0.9 Pearson correlation). We then successfully predict microbial composition from environmental variables such as plant age, temperature or precipitation (0.73 Pearson correlation, 0.42 Bray-Curtis). We extend this to predict microbiome composition under hypothetical scenarios, such as future climate change conditions. Finally, via transfer learning, we predict microbial composition in a distinct scenario with only a hundred sequences, and distinct environmental features. We propose that our deep latent space may assist microbiome-engineering strategies when technical or financial resources are limited, through predicting current or future microbiome compositions.Availability Software, results, and data are available at https://github.com/jorgemf/DeepLatentMicrobiomeCompeting Interest StatementThe authors have declared no competing interest.},
 bibtype = {article},
 author = {García-Jiménez, Beatriz and Muñoz, Jorge and Cabello, Sara and Medina, Joaquín and Wilkinson, Mark D},
 doi = {10.1101/2020.04.27.063974},
 journal = {bioRxiv}
}

Downloads: 15

{"_id":"9hFonAG2MZ39TPkpB","bibbaseid":"garcajimnez-muoz-cabello-medina-wilkinson-predictingmicrobiomesthroughadeeplatentspace-2020","authorIDs":["3D2gJyDWo9oBKnSCp","zTw6W3kTiuRT7TGS9"],"author_short":["García-Jiménez, B.","Muñoz, J.","Cabello, S.","Medina, J.","Wilkinson, M., D."],"bibdata":{"title":"Predicting microbiomes through a deep latent space","type":"article","year":"2020","pages":"2020.04.27.063974","websites":"http://biorxiv.org/content/early/2020/04/28/2020.04.27.063974.abstract","day":"28","id":"1afe7444-ae88-366b-adfe-dd830cad559e","created":"2020-08-12T08:24:06.771Z","file_attached":false,"profile_id":"fa910c8b-8889-3a42-afc9-302da7e3933a","group_id":"ff1f9038-dd83-321a-9605-910d757253bb","last_modified":"2020-08-12T08:24:06.771Z","read":false,"starred":false,"authored":false,"confirmed":"true","hidden":false,"source_type":"Preprint","private_publication":false,"abstract":"Motivation Microbial communities influence their environment by modifying the availability of compounds such as nutrients or chemical elicitors. Knowing the microbial composition of a site is therefore relevant to improving productivity or health. However, sequencing facilities are not always available, or may be prohibitively expensive in some cases. Thus, it would be desirable to computationally predict the microbial composition from more accessible, easily-measured features.Results Integrating Deep Learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance values into a deep latent space representation. Then, we design a model to predict the deep latent space and, consequently, to predict the complete microbial composition using environmental features as input. The performance of our system is examined using the rhizosphere microbiome of Maize. We reconstruct the microbial composition (717 taxa) from the deep latent space (10 values) with high fidelity (¿0.9 Pearson correlation). We then successfully predict microbial composition from environmental variables such as plant age, temperature or precipitation (0.73 Pearson correlation, 0.42 Bray-Curtis). We extend this to predict microbiome composition under hypothetical scenarios, such as future climate change conditions. Finally, via transfer learning, we predict microbial composition in a distinct scenario with only a hundred sequences, and distinct environmental features. We propose that our deep latent space may assist microbiome-engineering strategies when technical or financial resources are limited, through predicting current or future microbiome compositions.Availability Software, results, and data are available at https://github.com/jorgemf/DeepLatentMicrobiomeCompeting Interest StatementThe authors have declared no competing interest.","bibtype":"article","author":"García-Jiménez, Beatriz and Muñoz, Jorge and Cabello, Sara and Medina, Joaquín and Wilkinson, Mark D","doi":"10.1101/2020.04.27.063974","journal":"bioRxiv","bibtex":"@article{\n title = {Predicting microbiomes through a deep latent space},\n type = {article},\n year = {2020},\n pages = {2020.04.27.063974},\n websites = {http://biorxiv.org/content/early/2020/04/28/2020.04.27.063974.abstract},\n day = {28},\n id = {1afe7444-ae88-366b-adfe-dd830cad559e},\n created = {2020-08-12T08:24:06.771Z},\n file_attached = {false},\n profile_id = {fa910c8b-8889-3a42-afc9-302da7e3933a},\n group_id = {ff1f9038-dd83-321a-9605-910d757253bb},\n last_modified = {2020-08-12T08:24:06.771Z},\n read = {false},\n starred = {false},\n authored = {false},\n confirmed = {true},\n hidden = {false},\n source_type = {Preprint},\n private_publication = {false},\n abstract = {Motivation Microbial communities influence their environment by modifying the availability of compounds such as nutrients or chemical elicitors. Knowing the microbial composition of a site is therefore relevant to improving productivity or health. However, sequencing facilities are not always available, or may be prohibitively expensive in some cases. Thus, it would be desirable to computationally predict the microbial composition from more accessible, easily-measured features.Results Integrating Deep Learning techniques with microbiome data, we propose an artificial neural network architecture based on heterogeneous autoencoders to condense the long vector of microbial abundance values into a deep latent space representation. Then, we design a model to predict the deep latent space and, consequently, to predict the complete microbial composition using environmental features as input. The performance of our system is examined using the rhizosphere microbiome of Maize. We reconstruct the microbial composition (717 taxa) from the deep latent space (10 values) with high fidelity (¿0.9 Pearson correlation). We then successfully predict microbial composition from environmental variables such as plant age, temperature or precipitation (0.73 Pearson correlation, 0.42 Bray-Curtis). We extend this to predict microbiome composition under hypothetical scenarios, such as future climate change conditions. Finally, via transfer learning, we predict microbial composition in a distinct scenario with only a hundred sequences, and distinct environmental features. We propose that our deep latent space may assist microbiome-engineering strategies when technical or financial resources are limited, through predicting current or future microbiome compositions.Availability Software, results, and data are available at https://github.com/jorgemf/DeepLatentMicrobiomeCompeting Interest StatementThe authors have declared no competing interest.},\n bibtype = {article},\n author = {García-Jiménez, Beatriz and Muñoz, Jorge and Cabello, Sara and Medina, Joaquín and Wilkinson, Mark D},\n doi = {10.1101/2020.04.27.063974},\n journal = {bioRxiv}\n}","author_short":["García-Jiménez, B.","Muñoz, J.","Cabello, S.","Medina, J.","Wilkinson, M., D."],"urls":{"Website":"http://biorxiv.org/content/early/2020/04/28/2020.04.27.063974.abstract"},"biburl":"https://bibbase.org/service/mendeley/fa910c8b-8889-3a42-afc9-302da7e3933a","bibbaseid":"garcajimnez-muoz-cabello-medina-wilkinson-predictingmicrobiomesthroughadeeplatentspace-2020","role":"author","metadata":{"authorlinks":{"wilkinson, m":"https://bibbase.org/service/mendeley/17c87d5d-2470-32d7-b273-0734a1d9195f","garcía-jiménez, b":"https://bibbase.org/service/mendeley/fa910c8b-8889-3a42-afc9-302da7e3933a/group/ff1f9038-dd83-321a-9605-910d757253bb"}},"downloads":15},"bibtype":"article","creationDate":"2020-08-12T08:25:48.284Z","downloads":15,"keywords":[],"search_terms":["predicting","microbiomes","through","deep","latent","space","garcía-jiménez","muñoz","cabello","medina","wilkinson"],"title":"Predicting microbiomes through a deep latent space","year":2020,"biburl":"https://bibbase.org/service/mendeley/fa910c8b-8889-3a42-afc9-302da7e3933a","dataSources":["u3DebWvhQaEque62E","ya2CyA73rpZseyrZ8","2252seNhipfTmjEBQ"]}