SiftCell:从单细胞 RNA 序列读数中检测和分离含细胞液滴的稳健框架。

IF 9 1区 生物学 Q1 BIOCHEMISTRY & MOLECULAR BIOLOGY
Jingyue Xi, Sung Rye Park, Jun Hee Lee, Hyun Min Kang
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引用次数: 0

摘要

单细胞 RNA 测序(scRNA-seq)可对封装在条形编码液滴中的单个细胞的转录组进行大规模并行剖析。然而,在实际的 scRNA-seq 数据中,许多条形编码液滴并不包含细胞,而是捕获了一部分从受损或裂解细胞中释放出来的环境 RNA。分析 scRNA-seq 数据的第一步通常是过滤掉不含细胞的液滴并分离出含细胞的液滴,但区分它们往往很困难;不正确的过滤可能会对下游分析产生重大误导。我们提出的 SiftCell 是一套软件工具,可通过随机化(SiftCell-Shuffle)识别流形空间中的含细胞液滴和无细胞液滴并将其可视化,对这两种液滴进行分类(SiftCell-Boost),并量化每个液滴的环境 RNA 贡献(SiftCell-Mix)。通过将我们的方法应用于各种单细胞平台获得的数据集,我们表明 SiftCell 提供了一种简化的方法来执行 scRNA-seq 的上游质量控制,它比现有方法更全面、更准确。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

SiftCell: A robust framework to detect and isolate cell-containing droplets from single-cell RNA sequence reads.

SiftCell: A robust framework to detect and isolate cell-containing droplets from single-cell RNA sequence reads.

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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来源期刊
Cell Systems
Cell Systems Medicine-Pathology and Forensic Medicine
CiteScore
16.50
自引率
1.10%
发文量
84
审稿时长
42 days
期刊介绍: In 2015, Cell Systems was founded as a platform within Cell Press to showcase innovative research in systems biology. Our primary goal is to investigate complex biological phenomena that cannot be simply explained by basic mathematical principles. While the physical sciences have long successfully tackled such challenges, we have discovered that our most impactful publications often employ quantitative, inference-based methodologies borrowed from the fields of physics, engineering, mathematics, and computer science. We are committed to providing a home for elegant research that addresses fundamental questions in systems biology.
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