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Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers.


ABSTRACT: 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 competing methods. Using quasi-UMIs enables methods designed specifically for UMI data to be applied to non-UMI scRNA-seq datasets.

SUBMITTER: Townes FW 

PROVIDER: S-EPMC7333325 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

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Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers.

Townes F William FW   Irizarry Rafael A RA  

Genome biology 20200703 1


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 com  ...[more]

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