scMUSCL: Multi-Source Transfer Learning for Clustering scRNA-seq Data Article Swipe
Arash Khoeini
,
Funda Sar
,
Yen‐Yi Lin
,
Colin C. Collins
,
Martin Ester
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.04.22.590645
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1101/2024.04.22.590645
Motivation scRNA-seq analysis relies heavily on single-cell clustering to perform many downstream functions. Several machine learning methods have been proposed to improve the clustering of single cells, yet most of these methods are fully unsupervised and ignore the wealth of publicly available annotated datasets from single-cell experiments. Cells are high-dimensional entities, and unsupervised clustering might find clusters without biological meaning. Exploiting relevant annotated scRNA-seq dataset as the learning reference can provide an algorithm with the knowledge that guides it to better estimate the number of clusters and find meaningful clusters in the target dataset. Results In this paper, we propose Single Cell MUlti-Source CLustering, scMUSCL, a novel transfer learning method for finding clusters of cells in a target dataset by transferring knowledge from multiple annotated source (reference) datasets. scMUSCL relies on a deep neural network to extract domain and batch invariant cell representations, and it effectively addresses discrepancies across multiple source datasets and between source and target datasets in the new representation space. Unlike existing methods, scMUSCL does not need to know the number of clusters in the target dataset in advance and it does not require batch correction between source and target datasets. We conduct extensive experiments using 20 real-life datasets and show that scMUSCL outperforms the existing unsupervised and transfer-learning-based methods in almost all experiments. In particular, we show that scMUSCL outperforms the state-of-the-art transfer-learning-based scRNA-seq clustering method, MARS, by a large margin. Availability The Python implementation of scMUSCL is available at https://github.com/arashkhoeini/scMUSCL
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- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2024.04.22.590645
- https://www.biorxiv.org/content/biorxiv/early/2024/04/26/2024.04.22.590645.full.pdf
- OA Status
- green
- References
- 28
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4395663708
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https://openalex.org/W4395663708Canonical identifier for this work in OpenAlex
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https://doi.org/10.1101/2024.04.22.590645Digital Object Identifier
- Title
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scMUSCL: Multi-Source Transfer Learning for Clustering scRNA-seq DataWork title
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preprintOpenAlex work type
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enPrimary language
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2024Year of publication
- Publication date
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2024-04-26Full publication date if available
- Authors
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Arash Khoeini, Funda Sar, Yen‐Yi Lin, Colin C. Collins, Martin EsterList of authors in order
- Landing page
-
https://doi.org/10.1101/2024.04.22.590645Publisher landing page
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https://www.biorxiv.org/content/biorxiv/early/2024/04/26/2024.04.22.590645.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://www.biorxiv.org/content/biorxiv/early/2024/04/26/2024.04.22.590645.full.pdfDirect OA link when available
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0Total citation count in OpenAlex
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| abstract_inverted_index.advance | 182 |
| abstract_inverted_index.between | 154, 190 |
| abstract_inverted_index.conduct | 196 |
| abstract_inverted_index.dataset | 65, 119, 180 |
| abstract_inverted_index.extract | 137 |
| abstract_inverted_index.finding | 112 |
| abstract_inverted_index.heavily | 5 |
| abstract_inverted_index.improve | 22 |
| abstract_inverted_index.machine | 15 |
| abstract_inverted_index.margin. | 235 |
| abstract_inverted_index.method, | 230 |
| abstract_inverted_index.methods | 17, 32, 213 |
| abstract_inverted_index.network | 135 |
| abstract_inverted_index.perform | 10 |
| abstract_inverted_index.propose | 100 |
| abstract_inverted_index.provide | 71 |
| abstract_inverted_index.require | 187 |
| abstract_inverted_index.scMUSCL | 129, 167, 206, 223, 241 |
| abstract_inverted_index.without | 58 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.analysis | 3 |
| abstract_inverted_index.clusters | 57, 86, 90, 113, 176 |
| abstract_inverted_index.dataset. | 94 |
| abstract_inverted_index.datasets | 44, 152, 158, 202 |
| abstract_inverted_index.estimate | 82 |
| abstract_inverted_index.existing | 165, 209 |
| abstract_inverted_index.learning | 16, 68, 109 |
| abstract_inverted_index.meaning. | 60 |
| abstract_inverted_index.methods, | 166 |
| abstract_inverted_index.multiple | 124, 150 |
| abstract_inverted_index.proposed | 20 |
| abstract_inverted_index.publicly | 41 |
| abstract_inverted_index.relevant | 62 |
| abstract_inverted_index.scMUSCL, | 105 |
| abstract_inverted_index.transfer | 108 |
| abstract_inverted_index.addresses | 147 |
| abstract_inverted_index.algorithm | 73 |
| abstract_inverted_index.annotated | 43, 63, 125 |
| abstract_inverted_index.available | 42, 243 |
| abstract_inverted_index.datasets. | 128, 194 |
| abstract_inverted_index.entities, | 51 |
| abstract_inverted_index.extensive | 197 |
| abstract_inverted_index.invariant | 141 |
| abstract_inverted_index.knowledge | 76, 122 |
| abstract_inverted_index.real-life | 201 |
| abstract_inverted_index.reference | 69 |
| abstract_inverted_index.scRNA-seq | 2, 64, 228 |
| abstract_inverted_index.Exploiting | 61 |
| abstract_inverted_index.Motivation | 1 |
| abstract_inverted_index.biological | 59 |
| abstract_inverted_index.clustering | 8, 24, 54, 229 |
| abstract_inverted_index.correction | 189 |
| abstract_inverted_index.downstream | 12 |
| abstract_inverted_index.functions. | 13 |
| abstract_inverted_index.meaningful | 89 |
| abstract_inverted_index.(reference) | 127 |
| abstract_inverted_index.CLustering, | 104 |
| abstract_inverted_index.effectively | 146 |
| abstract_inverted_index.experiments | 198 |
| abstract_inverted_index.outperforms | 207, 224 |
| abstract_inverted_index.particular, | 219 |
| abstract_inverted_index.single-cell | 7, 46 |
| abstract_inverted_index.Availability | 236 |
| abstract_inverted_index.MUlti-Source | 103 |
| abstract_inverted_index.experiments. | 47, 217 |
| abstract_inverted_index.transferring | 121 |
| abstract_inverted_index.unsupervised | 35, 53, 210 |
| abstract_inverted_index.discrepancies | 148 |
| abstract_inverted_index.implementation | 239 |
| abstract_inverted_index.representation | 162 |
| abstract_inverted_index.high-dimensional | 50 |
| abstract_inverted_index.representations, | 143 |
| abstract_inverted_index.state-of-the-art | 226 |
| abstract_inverted_index.transfer-learning-based | 212, 227 |
| abstract_inverted_index.https://github.com/arashkhoeini/scMUSCL | 245 |
| cited_by_percentile_year | |
| corresponding_author_ids | https://openalex.org/A5076952899 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 5 |
| corresponding_institution_ids | https://openalex.org/I18014758 |
| citation_normalized_percentile.value | 0.07111789 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |