Journal
GENOME BIOLOGY
Volume 17, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13059-016-0888-1
Keywords
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Funding
- BBSRC CASE Studentship
- Abcam plc
- Lister Institute
- Lundbeck Foundation
- WTSI
- EMBL
- Biotechnology and Biological Sciences Research Council [1300642] Funding Source: researchfish
- Cancer Research UK [22231] Funding Source: researchfish
- Lundbeck Foundation [R193-2015-1611, R182-2014-3881] Funding Source: researchfish
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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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