vmrseq: probabilistic modeling of single-cell methylation heterogeneity
Abstract Single-cell DNA methylation measurements reveal genome-scale inter-cellular epigenetic heterogeneity, but extreme sparsity and noise challenges rigorous analysis. Previous methods to detect variably methylated regions (VMRs) have relied on predefined regions or sliding windows and report re...
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| Format: | Article |
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BMC
2024-12-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-024-03457-7 |
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| author | Ning Shen Keegan Korthauer |
| author_facet | Ning Shen Keegan Korthauer |
| author_sort | Ning Shen |
| collection | DOAJ |
| description | Abstract Single-cell DNA methylation measurements reveal genome-scale inter-cellular epigenetic heterogeneity, but extreme sparsity and noise challenges rigorous analysis. Previous methods to detect variably methylated regions (VMRs) have relied on predefined regions or sliding windows and report regions insensitive to heterogeneity level present in input. We present vmrseq, a statistical method that overcomes these challenges to detect VMRs with increased accuracy in synthetic benchmarks and improved feature selection in case studies. vmrseq also highlights context-dependent correlations between methylation and gene expression, supporting previous findings and facilitating novel hypotheses on epigenetic regulation. vmrseq is available at https://github.com/nshen7/vmrseq . |
| format | Article |
| id | doaj-art-f000009323f248df9c9e6badf7c59c3b |
| institution | Kabale University |
| issn | 1474-760X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj-art-f000009323f248df9c9e6badf7c59c3b2025-01-05T12:31:57ZengBMCGenome Biology1474-760X2024-12-0125112710.1186/s13059-024-03457-7vmrseq: probabilistic modeling of single-cell methylation heterogeneityNing Shen0Keegan Korthauer1Department of Statistics, University of British ColumbiaDepartment of Statistics, University of British ColumbiaAbstract Single-cell DNA methylation measurements reveal genome-scale inter-cellular epigenetic heterogeneity, but extreme sparsity and noise challenges rigorous analysis. Previous methods to detect variably methylated regions (VMRs) have relied on predefined regions or sliding windows and report regions insensitive to heterogeneity level present in input. We present vmrseq, a statistical method that overcomes these challenges to detect VMRs with increased accuracy in synthetic benchmarks and improved feature selection in case studies. vmrseq also highlights context-dependent correlations between methylation and gene expression, supporting previous findings and facilitating novel hypotheses on epigenetic regulation. vmrseq is available at https://github.com/nshen7/vmrseq .https://doi.org/10.1186/s13059-024-03457-7DNA methylationSingle-cell bisulfite sequencingEpigenetic heterogeneityHidden Markov model |
| spellingShingle | Ning Shen Keegan Korthauer vmrseq: probabilistic modeling of single-cell methylation heterogeneity Genome Biology DNA methylation Single-cell bisulfite sequencing Epigenetic heterogeneity Hidden Markov model |
| title | vmrseq: probabilistic modeling of single-cell methylation heterogeneity |
| title_full | vmrseq: probabilistic modeling of single-cell methylation heterogeneity |
| title_fullStr | vmrseq: probabilistic modeling of single-cell methylation heterogeneity |
| title_full_unstemmed | vmrseq: probabilistic modeling of single-cell methylation heterogeneity |
| title_short | vmrseq: probabilistic modeling of single-cell methylation heterogeneity |
| title_sort | vmrseq probabilistic modeling of single cell methylation heterogeneity |
| topic | DNA methylation Single-cell bisulfite sequencing Epigenetic heterogeneity Hidden Markov model |
| url | https://doi.org/10.1186/s13059-024-03457-7 |
| work_keys_str_mv | AT ningshen vmrseqprobabilisticmodelingofsinglecellmethylationheterogeneity AT keegankorthauer vmrseqprobabilisticmodelingofsinglecellmethylationheterogeneity |