Journal
CELL SYSTEMS
Volume 14, Issue 7, Pages 620-+Publisher
CELL PRESS
DOI: 10.1016/j.cels.2023.06.002
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SiftCell is a suite of software tools that can identify and visualize cell-containing and cell-free droplets, as well as quantify the contribution of ambient RNAs for each droplet. It provides a more comprehensive and accurate way to perform upstream quality control in scRNA-seq than existing methods.
Single-cell RNA sequencing (scRNA-seq) massively profiles transcriptomes of individual cells encapsulated in barcoded droplets in parallel. However, in real-world scRNA-seq data, many barcoded droplets do not contain cells, but instead, they capture a fraction of ambient RNAs released from damaged or lysed cells. A typical first step to analyze scRNA-seq data is to filter out cell-free droplets and isolate cell-containing droplets, but distinguishing them is often challenging; incorrect filtering may mislead the downstream anal-ysis substantially. We propose SiftCell, a suite of software tools to identify and visualize cell-containing and cell-free droplets in manifold space via randomization (SiftCell-Shuffle) to classify between the two types of droplets (SiftCell-Boost) and to quantify the contribution of ambient RNAs for each droplet (SiftCell-Mix). By applying our method to datasets obtained by various single-cell platforms, we show that SiftCell provides a streamlined way to perform upstream quality control of scRNA-seq, which is more comprehensive and accurate than existing methods.
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