Classification of low quality cells from single-cell RNA-seq data.

> 商业许可源文 · EUROPE_PMC · [CC-BY](https://creativecommons.org/licenses/by/)

书目信息

引用：Ilicic T, Kim JK, Kolodziejczyk AA, Bagger FO, McCarthy DJ, Marioni JC, Teichmann SA. (2016). Classification of low quality cells from single-cell RNA-seq data. Genome biology. PMID 26887813 · PMC4758103 · DOI 10.1186/s13059-016-0888-1

证据类型：PRIMARY_RESEARCH

主题：rna-seq、single-cell

被引次数（采集时）：542

原始记录：[Europe PMC](https://europepmc.org/article/MED/26887813)

来源许可：[CC-BY](https://creativecommons.org/licenses/by/)

作者摘要（按来源许可复用）

Single-cell RNA sequencing (scRNA-seq) has broad applications across biomedical research. One of the key challenges is to ensure that only single, live cells are included in downstream analysis, as the inclusion of compromised cells inevitably affects data interpretation. Here, we present a generic approach for processing scRNA-seq data and detecting low quality cells, using a curated set of over 20 biological and technical features. Our approach improves classification accuracy by over 30 % compared to traditional methods when tested on over 5,000 cells, including CD4+ T cells, bone marrow dendritic cells, and mouse embryonic stem cells.

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