MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data
Abstract Background Advancements in genomic sequencing continually improve personalized medicine, and recent breakthroughs generate multimodal data on a cellular level. We introduce MOSCATO, a technique for selecting features across multimodal single-cell datasets that relate to clinical outcomes. W...
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| Format: | Article |
| Language: | English |
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BMC
2022-08-01
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| Series: | BMC Genomics |
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| Online Access: | https://doi.org/10.1186/s12864-022-08759-3 |
| _version_ | 1798038047702384640 |
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| author | Lorin M. Towle-Miller Jeffrey C. Miecznikowski |
| author_facet | Lorin M. Towle-Miller Jeffrey C. Miecznikowski |
| author_sort | Lorin M. Towle-Miller |
| collection | DOAJ |
| description | Abstract Background Advancements in genomic sequencing continually improve personalized medicine, and recent breakthroughs generate multimodal data on a cellular level. We introduce MOSCATO, a technique for selecting features across multimodal single-cell datasets that relate to clinical outcomes. We summarize the single-cell data using tensors and perform regularized tensor regression to return clinically-associated variable sets for each ‘omic’ type. Results Robustness was assessed over simulations based on available single-cell simulation methods, and applicability was assessed through an example using CITE-seq data to detect genes associated with leukemia. We find that MOSCATO performs favorably in selecting network features while also shown to be applicable to real multimodal single-cell data. Conclusions MOSCATO is a useful analytical technique for supervised feature selection in multimodal single-cell data. The flexibility of our approach enables future extensions on distributional assumptions and covariate adjustments. |
| first_indexed | 2024-04-11T21:34:53Z |
| format | Article |
| id | doaj.art-599f573e38a246dfb75c1e20205d5651 |
| institution | Directory Open Access Journal |
| issn | 1471-2164 |
| language | English |
| last_indexed | 2024-04-11T21:34:53Z |
| publishDate | 2022-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Genomics |
| spelling | doaj.art-599f573e38a246dfb75c1e20205d56512022-12-22T04:01:48ZengBMCBMC Genomics1471-21642022-08-0123111510.1186/s12864-022-08759-3MOSCATO: a supervised approach for analyzing multi-Omic single-Cell dataLorin M. Towle-Miller0Jeffrey C. Miecznikowski1University at BuffaloUniversity at BuffaloAbstract Background Advancements in genomic sequencing continually improve personalized medicine, and recent breakthroughs generate multimodal data on a cellular level. We introduce MOSCATO, a technique for selecting features across multimodal single-cell datasets that relate to clinical outcomes. We summarize the single-cell data using tensors and perform regularized tensor regression to return clinically-associated variable sets for each ‘omic’ type. Results Robustness was assessed over simulations based on available single-cell simulation methods, and applicability was assessed through an example using CITE-seq data to detect genes associated with leukemia. We find that MOSCATO performs favorably in selecting network features while also shown to be applicable to real multimodal single-cell data. Conclusions MOSCATO is a useful analytical technique for supervised feature selection in multimodal single-cell data. The flexibility of our approach enables future extensions on distributional assumptions and covariate adjustments.https://doi.org/10.1186/s12864-022-08759-3Tensor regressionSingle-cell sequencingMulti-omicsMultimodalNetwork analysis |
| spellingShingle | Lorin M. Towle-Miller Jeffrey C. Miecznikowski MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data BMC Genomics Tensor regression Single-cell sequencing Multi-omics Multimodal Network analysis |
| title | MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data |
| title_full | MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data |
| title_fullStr | MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data |
| title_full_unstemmed | MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data |
| title_short | MOSCATO: a supervised approach for analyzing multi-Omic single-Cell data |
| title_sort | moscato a supervised approach for analyzing multi omic single cell data |
| topic | Tensor regression Single-cell sequencing Multi-omics Multimodal Network analysis |
| url | https://doi.org/10.1186/s12864-022-08759-3 |
| work_keys_str_mv | AT lorinmtowlemiller moscatoasupervisedapproachforanalyzingmultiomicsinglecelldata AT jeffreycmiecznikowski moscatoasupervisedapproachforanalyzingmultiomicsinglecelldata |