BUTTERFLY: addressing the pooled amplification paradox with unique molecular identifiers in single-cell RNA-seq

The incorporation of unique molecular identifiers (UMIs) in single-cell RNA-seq assays makes possible the identification of duplicated molecules, thereby facilitating the counting of distinct molecules from sequenced reads. However, we show that the naive removal of duplicates can lead to a bias due to a pooled amplification paradox, and we propose an improved quantification method based on unseen species modeling. Our correction called BUTTERFLY uses a zero truncated negative binomial estimator implemented in the kallisto bustools workflow. We demonstrate its efficacy across cell types and genes and show that in some cases it can invert the relative abundance of genes.

Automated summary

This study introduces UMIs in single-cell RNA-seq assays to identify duplicated molecules, presenting an improved quantification method called BUTTERFLY based on unseen species modeling. This method effectively addresses the bias caused by the naive removal of duplicates by demonstrating its efficacy across cell types and genes, sometimes even inverting the relative abundance of genes.