Classification of low quality cells from single-cell RNA-seq data
By Tomislav Ilicic, Jong Kyoung Kim, Aleksandra A. Kolodziejczyk, Frederik Otzen Bagger, Davis J. McCarthy, John C. Marioni, Sarah A. Teichmann. Genome Biology, 2016
616 citations recorded by the source corpus.
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.
Subfields: Cancer Research, Molecular Biology
View Paper: https://www.semanticscholar.org/paper/25678ae046e9483522ee4595924a92c907e94a9e
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In Cancer Research
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- Rhonda L. Bacher
- Allon Wagner
- Christopher S. McGinnis
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In Molecular Biology
- Rahul Satija
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- Peter Smibert
- Efthymia Papalexi
- Andrew W. Butler
- Paul J. Hoffman
- Christopher S. McGinnis
Citations in the corpus
- DoubletFinder: Doublet Detection in Single-Cell RNA Sequencing Data Using Artificial Nearest Neighbors (2018, background): “Doublets are known to confound scRNA-seq data analysis (Stegle et al., 2015; Ilicic et al., 2016), and it is common practice to mitigate these effects by sequencing far fewer cells than is theoretically possible in order to minimize doublet formation rates.”
- Integrating single-cell transcriptomic data across different conditions, technologies, and species (2018, methodology): “Additionally, we observed significant mitochondrial heterogeneity within each data set, in keeping with previous reports 58 , and regressed out mitochondrial heterogeneity from each data set before running CCA.”
- Design and computational analysis of single-cell RNA-sequencing experiments (2016, background): “Ilicic et al. [16] recently developed a supervised classification approach for identifying low-quality cells in the single-cell setting.”
- Revealing the vectors of cellular identity with single-cell genomics (2016, methodology): “Indeed, a recent study 44 reported that technical quality features were highly correlated with biological cell type.”