ArticleCell systems2023
SiftCell: A robust framework to detect and isolate cell-containing droplets from single-cell RNA sequence reads.
Article in Cell systems, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A who's who of cell types in skeletal muscle.Communications biology · 2026Review
- Concepts and new developments in droplet-based single cell multi-omics.Trends in biotechnology · 2024Review
- The Advancement and Application of the Single-Cell Transcriptome in Biological and Medical Research.Biology · 2024Review
- Isolation of planarian viable cells using fluorescence-activated cell sorting for advancing single-cell transcriptome analysis.Genes to cells : devoted to molecular & cellular mechanisms · 2023Article
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Authors and funding
4 authors.
Funding
Abstract
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 analysis 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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Registered trials
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