scMeFormer : a transformer-based deep learning model for imputing DNA methylation states in single cells enhances the detection of epigenetic alterations in schizophrenia
DNA methylation (DNAm), a crucial epigenetic mark, plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable the profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across four single-nucleus DNAm datasets generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. Furthermore, we applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, and we expect scMeFormer to be a valuable tool for single-cell DNAm studies.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - year:2024 |
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| Enthalten in: |
bioRxiv : the preprint server for biology - (2024) vom: 25. Jan. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Zhou, Jiyun [VerfasserIn] |
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| Anmerkungen: |
Date Revised 16.02.2024 published: Electronic Citation Status PubMed-not-MEDLINE |
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| doi: |
10.1101/2024.01.25.577200 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM368173275 |
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| 100 | 1 | |a Zhou, Jiyun |e verfasserin |4 aut | |
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| 500 | |a published: Electronic | ||
| 500 | |a Citation Status PubMed-not-MEDLINE | ||
| 520 | |a DNA methylation (DNAm), a crucial epigenetic mark, plays a key role in gene regulation, mammalian development, and various human diseases. Single-cell technologies enable the profiling of DNAm states at cytosines within the DNA sequence of individual cells, but they often suffer from limited coverage of CpG sites. In this study, we introduce scMeFormer, a transformer-based deep learning model designed to impute DNAm states for each CpG site in single cells. Through comprehensive evaluations, we demonstrate the superior performance of scMeFormer compared to alternative models across four single-nucleus DNAm datasets generated by distinct technologies. Remarkably, scMeFormer exhibits high-fidelity imputation, even when dealing with significantly reduced coverage, as low as 10% of the original CpG sites. Furthermore, we applied scMeFormer to a single-nucleus DNAm dataset generated from the prefrontal cortex of four schizophrenia patients and four neurotypical controls. This enabled the identification of thousands of differentially methylated regions associated with schizophrenia that would have remained undetectable without imputation and added granularity to our understanding of epigenetic alterations in schizophrenia within specific cell types. Our study highlights the power of deep learning in imputing DNAm states in single cells, and we expect scMeFormer to be a valuable tool for single-cell DNAm studies | ||
| 650 | 4 | |a Preprint | |
| 700 | 1 | |a Luo, Chongyuan |e verfasserin |4 aut | |
| 700 | 1 | |a Liu, Hanqing |e verfasserin |4 aut | |
| 700 | 1 | |a Heffel, Matthew G |e verfasserin |4 aut | |
| 700 | 1 | |a Straub, Richard E |e verfasserin |4 aut | |
| 700 | 1 | |a Kleinman, Joel E |e verfasserin |4 aut | |
| 700 | 1 | |a Hyde, Thomas M |e verfasserin |4 aut | |
| 700 | 1 | |a Ecker, Joseph R |e verfasserin |4 aut | |
| 700 | 1 | |a Weinberger, Daniel R |e verfasserin |4 aut | |
| 700 | 1 | |a Han, Shizhong |e verfasserin |4 aut | |
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