bioRxiv · 10.1101/817031
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 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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Townes, F. W., Irizarry, R. A.. 2019-10-24. Quantile normalization of single-cell RNA-seq read counts without unique molecular identifiers. https://doi.org/10.1101/817031
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