Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin
To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individuals by molecular signatures in the form of machine-l...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-02-01
|
| Series: | International Journal of Molecular Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1422-0067/23/5/2481 |
| _version_ | 1797474997963325440 |
|---|---|
| author | Magdalena Kircher Elisa Chludzinski Jessica Krepel Babak Saremi Andreas Beineke Klaus Jung |
| author_facet | Magdalena Kircher Elisa Chludzinski Jessica Krepel Babak Saremi Andreas Beineke Klaus Jung |
| author_sort | Magdalena Kircher |
| collection | DOAJ |
| description | To better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individuals by molecular signatures in the form of machine-learning models with genes as predictor variables. Early diagnosis of patients by molecular signatures could also contribute to better treatments. An approach that has rarely been considered for machine-learning models in the context of transcriptomics is data augmentation. For other data types it has been shown that augmentation can improve classification accuracy and prevent overfitting. Here, we compare three strategies for data augmentation of DNA microarray and RNA-seq data from two selected studies on respiratory diseases of viral origin. The first study involves samples of patients with either viral or bacterial origin of the respiratory disease, the second study involves patients with either SARS-CoV-2 or another respiratory virus as disease origin. Specifically, we reanalyze these public datasets to study whether patient classification by transcriptomic signatures can be improved when adding artificial data for training of the machine-learning models. Our comparison reveals that augmentation of transcriptomic data can improve the classification accuracy and that fewer genes are necessary as explanatory variables in the final models. We also report genes from our signatures that overlap with signatures presented in the original publications of our example data. Due to strict selection criteria, the molecular role of these genes in the context of respiratory infectious diseases is underlined. |
| first_indexed | 2024-03-09T20:38:54Z |
| format | Article |
| id | doaj.art-6a71f870d6a74b468ab4ef8b64fe00d9 |
| institution | Directory Open Access Journal |
| issn | 1661-6596 1422-0067 |
| language | English |
| last_indexed | 2024-03-09T20:38:54Z |
| publishDate | 2022-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | International Journal of Molecular Sciences |
| spelling | doaj.art-6a71f870d6a74b468ab4ef8b64fe00d92023-11-23T23:04:44ZengMDPI AGInternational Journal of Molecular Sciences1661-65961422-00672022-02-01235248110.3390/ijms23052481Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral OriginMagdalena Kircher0Elisa Chludzinski1Jessica Krepel2Babak Saremi3Andreas Beineke4Klaus Jung5Institute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, GermanyDepartment of Pathology, University of Veterinary Medicine Hannover, Buenteweg 17, 30559 Hannover, GermanyInstitute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, GermanyInstitute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, GermanyDepartment of Pathology, University of Veterinary Medicine Hannover, Buenteweg 17, 30559 Hannover, GermanyInstitute for Animal Breeding and Genetics, University of Veterinary Medicine Hannover, Buenteweg 17p, 30559 Hannover, GermanyTo better understand the molecular basis of respiratory diseases of viral origin, high-throughput gene-expression data are frequently taken by means of DNA microarray or RNA-seq technology. Such data can also be useful to classify infected individuals by molecular signatures in the form of machine-learning models with genes as predictor variables. Early diagnosis of patients by molecular signatures could also contribute to better treatments. An approach that has rarely been considered for machine-learning models in the context of transcriptomics is data augmentation. For other data types it has been shown that augmentation can improve classification accuracy and prevent overfitting. Here, we compare three strategies for data augmentation of DNA microarray and RNA-seq data from two selected studies on respiratory diseases of viral origin. The first study involves samples of patients with either viral or bacterial origin of the respiratory disease, the second study involves patients with either SARS-CoV-2 or another respiratory virus as disease origin. Specifically, we reanalyze these public datasets to study whether patient classification by transcriptomic signatures can be improved when adding artificial data for training of the machine-learning models. Our comparison reveals that augmentation of transcriptomic data can improve the classification accuracy and that fewer genes are necessary as explanatory variables in the final models. We also report genes from our signatures that overlap with signatures presented in the original publications of our example data. Due to strict selection criteria, the molecular role of these genes in the context of respiratory infectious diseases is underlined.https://www.mdpi.com/1422-0067/23/5/2481data augmentationdeep learninggenerative adversarial networkstranscriptomic datahigh-dimensional dataviral acute respiratory illness |
| spellingShingle | Magdalena Kircher Elisa Chludzinski Jessica Krepel Babak Saremi Andreas Beineke Klaus Jung Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin International Journal of Molecular Sciences data augmentation deep learning generative adversarial networks transcriptomic data high-dimensional data viral acute respiratory illness |
| title | Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin |
| title_full | Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin |
| title_fullStr | Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin |
| title_full_unstemmed | Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin |
| title_short | Augmentation of Transcriptomic Data for Improved Classification of Patients with Respiratory Diseases of Viral Origin |
| title_sort | augmentation of transcriptomic data for improved classification of patients with respiratory diseases of viral origin |
| topic | data augmentation deep learning generative adversarial networks transcriptomic data high-dimensional data viral acute respiratory illness |
| url | https://www.mdpi.com/1422-0067/23/5/2481 |
| work_keys_str_mv | AT magdalenakircher augmentationoftranscriptomicdataforimprovedclassificationofpatientswithrespiratorydiseasesofviralorigin AT elisachludzinski augmentationoftranscriptomicdataforimprovedclassificationofpatientswithrespiratorydiseasesofviralorigin AT jessicakrepel augmentationoftranscriptomicdataforimprovedclassificationofpatientswithrespiratorydiseasesofviralorigin AT babaksaremi augmentationoftranscriptomicdataforimprovedclassificationofpatientswithrespiratorydiseasesofviralorigin AT andreasbeineke augmentationoftranscriptomicdataforimprovedclassificationofpatientswithrespiratorydiseasesofviralorigin AT klausjung augmentationoftranscriptomicdataforimprovedclassificationofpatientswithrespiratorydiseasesofviralorigin |