Cargando…
Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data
Single-cell DNA methylation data has become increasingly abundant and has uncovered many genes with a positive correlation between expression and promoter methylation, challenging the common dogma based on bulk data. However, computational tools for analyzing single-cell methylome data are lagging f...
Autores principales: | , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Cold Spring Harbor Laboratory Press
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7849382/ https://www.ncbi.nlm.nih.gov/pubmed/33219054 http://dx.doi.org/10.1101/gr.267047.120 |
_version_ | 1783645290270556160 |
---|---|
author | Uzun, Yasin Wu, Hao Tan, Kai |
author_facet | Uzun, Yasin Wu, Hao Tan, Kai |
author_sort | Uzun, Yasin |
collection | PubMed |
description | Single-cell DNA methylation data has become increasingly abundant and has uncovered many genes with a positive correlation between expression and promoter methylation, challenging the common dogma based on bulk data. However, computational tools for analyzing single-cell methylome data are lagging far behind. A number of tasks, including cell type calling and integration with transcriptome data, requires the construction of a robust gene activity matrix as the prerequisite but challenging task. The advent of multi-omics data enables measurement of both DNA methylation and gene expression for the same single cells. Although such data is rather sparse, they are sufficient to train supervised models that capture the complex relationship between DNA methylation and gene expression and predict gene activities at single-cell level. Here, we present methylome association by predictive linkage to expression (MAPLE), a computational framework that learns the association between DNA methylation and expression using both gene- and cell-dependent statistical features. Using multiple data sets generated with different experimental protocols, we show that using predicted gene activity values significantly improves several analysis tasks, including clustering, cell type identification, and integration with transcriptome data. Application of MAPLE revealed several interesting biological insights into the relationship between methylation and gene expression, including asymmetric importance of methylation signals around transcription start site for predicting gene expression, and increased predictive power of methylation signals in promoters located outside CpG islands and shores. With the rapid accumulation of single-cell epigenomics data, MAPLE provides a general framework for integrating such data with transcriptome data. |
format | Online Article Text |
id | pubmed-7849382 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Cold Spring Harbor Laboratory Press |
record_format | MEDLINE/PubMed |
spelling | pubmed-78493822021-02-04 Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data Uzun, Yasin Wu, Hao Tan, Kai Genome Res Method Single-cell DNA methylation data has become increasingly abundant and has uncovered many genes with a positive correlation between expression and promoter methylation, challenging the common dogma based on bulk data. However, computational tools for analyzing single-cell methylome data are lagging far behind. A number of tasks, including cell type calling and integration with transcriptome data, requires the construction of a robust gene activity matrix as the prerequisite but challenging task. The advent of multi-omics data enables measurement of both DNA methylation and gene expression for the same single cells. Although such data is rather sparse, they are sufficient to train supervised models that capture the complex relationship between DNA methylation and gene expression and predict gene activities at single-cell level. Here, we present methylome association by predictive linkage to expression (MAPLE), a computational framework that learns the association between DNA methylation and expression using both gene- and cell-dependent statistical features. Using multiple data sets generated with different experimental protocols, we show that using predicted gene activity values significantly improves several analysis tasks, including clustering, cell type identification, and integration with transcriptome data. Application of MAPLE revealed several interesting biological insights into the relationship between methylation and gene expression, including asymmetric importance of methylation signals around transcription start site for predicting gene expression, and increased predictive power of methylation signals in promoters located outside CpG islands and shores. With the rapid accumulation of single-cell epigenomics data, MAPLE provides a general framework for integrating such data with transcriptome data. Cold Spring Harbor Laboratory Press 2021-01 /pmc/articles/PMC7849382/ /pubmed/33219054 http://dx.doi.org/10.1101/gr.267047.120 Text en © 2021 Uzun et al.; Published by Cold Spring Harbor Laboratory Press http://creativecommons.org/licenses/by/4.0/ This article, published in Genome Research, is available under a Creative Commons License (Attribution 4.0 International), as described at http://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Method Uzun, Yasin Wu, Hao Tan, Kai Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data |
title | Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data |
title_full | Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data |
title_fullStr | Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data |
title_full_unstemmed | Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data |
title_short | Predictive modeling of single-cell DNA methylome data enhances integration with transcriptome data |
title_sort | predictive modeling of single-cell dna methylome data enhances integration with transcriptome data |
topic | Method |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7849382/ https://www.ncbi.nlm.nih.gov/pubmed/33219054 http://dx.doi.org/10.1101/gr.267047.120 |
work_keys_str_mv | AT uzunyasin predictivemodelingofsinglecelldnamethylomedataenhancesintegrationwithtranscriptomedata AT wuhao predictivemodelingofsinglecelldnamethylomedataenhancesintegrationwithtranscriptomedata AT tankai predictivemodelingofsinglecelldnamethylomedataenhancesintegrationwithtranscriptomedata |