Partial label learning for automated classification of single-cell transcriptomic profiles.
Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for ind...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2024-04-01
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| Series: | PLoS Computational Biology |
| Online Access: | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012006&type=printable |
| _version_ | 1797200761917014016 |
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| author | Malek Senoussi Thierry Artieres Paul Villoutreix |
| author_facet | Malek Senoussi Thierry Artieres Paul Villoutreix |
| author_sort | Malek Senoussi |
| collection | DOAJ |
| description | Single-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for individual cells. Because of the fast accumulation of datasets and the high dimensionality of the data, it has become challenging to explore and annotate single-cell transcriptomic profiles by hand. To overcome this challenge, automated classification methods are needed. Classical approaches rely on supervised training datasets. However, due to the difficulty of obtaining data annotated at single-cell resolution, we propose instead to take advantage of partial annotations. The partial label learning framework assumes that we can obtain a set of candidate labels containing the correct one for each data point, a simpler setting than requiring a fully supervised training dataset. We study and extend when needed state-of-the-art multi-class classification methods, such as SVM, kNN, prototype-based, logistic regression and ensemble methods, to the partial label learning framework. Moreover, we study the effect of incorporating the structure of the label set into the methods. We focus particularly on the hierarchical structure of the labels, as commonly observed in developmental processes. We show, on simulated and real datasets, that these extensions enable to learn from partially labeled data, and perform predictions with high accuracy, particularly with a nonlinear prototype-based method. We demonstrate that the performances of our methods trained with partially annotated data reach the same performance as fully supervised data. Finally, we study the level of uncertainty present in the partially annotated data, and derive some prescriptive results on the effect of this uncertainty on the accuracy of the partial label learning methods. Overall our findings show how hierarchical and non-hierarchical partial label learning strategies can help solve the problem of automated classification of single-cell transcriptomic profiles, interestingly these methods rely on a much less stringent type of annotated datasets compared to fully supervised learning methods. |
| first_indexed | 2024-04-24T07:36:48Z |
| format | Article |
| id | doaj.art-dfa1a00e5afd4346b4bbafaa59159d9b |
| institution | Directory Open Access Journal |
| issn | 1553-734X 1553-7358 |
| language | English |
| last_indexed | 2024-04-24T07:36:48Z |
| publishDate | 2024-04-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS Computational Biology |
| spelling | doaj.art-dfa1a00e5afd4346b4bbafaa59159d9b2024-04-20T05:31:09ZengPublic Library of Science (PLoS)PLoS Computational Biology1553-734X1553-73582024-04-01204e101200610.1371/journal.pcbi.1012006Partial label learning for automated classification of single-cell transcriptomic profiles.Malek SenoussiThierry ArtieresPaul VilloutreixSingle-cell RNA sequencing (scRNASeq) data plays a major role in advancing our understanding of developmental biology. An important current question is how to classify transcriptomic profiles obtained from scRNASeq experiments into the various cell types and identify the lineage relationship for individual cells. Because of the fast accumulation of datasets and the high dimensionality of the data, it has become challenging to explore and annotate single-cell transcriptomic profiles by hand. To overcome this challenge, automated classification methods are needed. Classical approaches rely on supervised training datasets. However, due to the difficulty of obtaining data annotated at single-cell resolution, we propose instead to take advantage of partial annotations. The partial label learning framework assumes that we can obtain a set of candidate labels containing the correct one for each data point, a simpler setting than requiring a fully supervised training dataset. We study and extend when needed state-of-the-art multi-class classification methods, such as SVM, kNN, prototype-based, logistic regression and ensemble methods, to the partial label learning framework. Moreover, we study the effect of incorporating the structure of the label set into the methods. We focus particularly on the hierarchical structure of the labels, as commonly observed in developmental processes. We show, on simulated and real datasets, that these extensions enable to learn from partially labeled data, and perform predictions with high accuracy, particularly with a nonlinear prototype-based method. We demonstrate that the performances of our methods trained with partially annotated data reach the same performance as fully supervised data. Finally, we study the level of uncertainty present in the partially annotated data, and derive some prescriptive results on the effect of this uncertainty on the accuracy of the partial label learning methods. Overall our findings show how hierarchical and non-hierarchical partial label learning strategies can help solve the problem of automated classification of single-cell transcriptomic profiles, interestingly these methods rely on a much less stringent type of annotated datasets compared to fully supervised learning methods.https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012006&type=printable |
| spellingShingle | Malek Senoussi Thierry Artieres Paul Villoutreix Partial label learning for automated classification of single-cell transcriptomic profiles. PLoS Computational Biology |
| title | Partial label learning for automated classification of single-cell transcriptomic profiles. |
| title_full | Partial label learning for automated classification of single-cell transcriptomic profiles. |
| title_fullStr | Partial label learning for automated classification of single-cell transcriptomic profiles. |
| title_full_unstemmed | Partial label learning for automated classification of single-cell transcriptomic profiles. |
| title_short | Partial label learning for automated classification of single-cell transcriptomic profiles. |
| title_sort | partial label learning for automated classification of single cell transcriptomic profiles |
| url | https://journals.plos.org/ploscompbiol/article/file?id=10.1371/journal.pcbi.1012006&type=printable |
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