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Activity Number:
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181
- Statistical Methods in Gene Expression Data Analysis II
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Type:
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Contributed
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Date/Time:
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Tuesday, August 4, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Statistics in Genomics and Genetics
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Abstract #313680
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Title:
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Quantile Normalization of Single-Cell RNA-Seq Read Counts Without Unique Molecular Identifiers
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Author(s):
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Frederick Townes* and Rafael Irizarry
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Companies:
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Princeton University and Harvard T.H. Chan School of Public Health
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Keywords:
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single-cell;
RNA-seq;
UMIs;
normalization;
gene expression
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Abstract:
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Single-cell RNA-seq (scRNA-seq) profiles gene expression of individual cells. Unique molecular identifiers (UMIs) remove duplicates in read counts resulting from polymerase chain reaction, a major source of noise. For scRNA-seq data lacking UMIs, we propose quasi-UMIs: quantile normalization of read counts to a compound Poisson distribution empirically derived from UMI datasets. When applied to ground-truth datasets having both reads and UMIs, quasi-UMI normalization has higher accuracy than alternatives such as census counts. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.
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Authors who are presenting talks have a * after their name.
