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Activity Number:
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535
- Contributed Poster Presentations: Section on Statistics in Genomics and Genetics
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #330874
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Title:
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Modeling Missingness to Reduce Bias in Single-Cell DNA Methylation Data
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Author(s):
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Divy Kangeyan* and Martin Aryee
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Companies:
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Harvard University and Harvard University
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Keywords:
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single-cell analysis;
methylation;
missing data
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Abstract:
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Single-cell analysis has the potential to improve the understanding of cellular heterogeneity by obtaining individual cellular profiles instead of aggregate information that is usually seen in bulk level analysis. DNA methylation at CpG dinucleotides is an important epigenetic phenomenon that regulates gene expression. At present several single-cell methylation protocols exist to understand disease and normal state mechanisms but they all suffer from low coverage due to the low quantity of input DNA in a single-cell. On average, only about 1-10% of CpGs are observed in typical single-cell libraries. We show how missingness of methylation status can bias mean methylation estimates and clustering analyses. We propose a joint analysis approach that leverages either bulk sequencing data or a consensus generated from a large number of single-cells, to infer bias-corrected single-cell methylation status. In this approach we model and explicitly adjust for biases that arise due to missingness. Understanding and correcting the biases that exist in single-cell methylation data will be crucial to make robust biological conclusions about individual cells based on methylation profiles.
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Authors who are presenting talks have a * after their name.
