Design and Analysis of Single-Cell Sequencing Experiments.

By Dominic Grün, Alexander van Oudenaarden. Cell, 2015

504 citations recorded by the source corpus.

Subfields: Cancer Research, Genetics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fe76bb72850460675c3f87741751bcd8674d2c10

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Citations in the corpus

Design and computational analysis of single-cell RNA-sequencing experiments (2016, methodology): “Details are provided in reviews by Grün and van Oudenaarden [7], Saliba et al. [5] and Kolodziejczyk et al. [6].”
Gene Regulatory Network Inference from Single-Cell Data Using Multivariate Information Measures (2016, background): “…and impact of different sources of noise are poorly understood, and numerous factors contribute to the biological heterogeneity observed within cell populations (Stegle et al. 2015, Pierson et al. 2015, Grün et al. 2015, Bacher & Kendziorski 2016, Liu et al. 2016, Buettner et al. 2015).”
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs (2017, unknown): “A straight-forward solution for this issue is to downsample overrepresented libraries [30]. zUMIs has an built-in function for downsampling datasets to a user-specified number of reads or a range of reads.”
Revealing the vectors of cellular identity with single-cell genomics (2016, background): “The utility of the ERCC spike-ins in modeling technical noise is further curtailed by differences between them and endogenous RNA 58,75 , and it has been reported 68 that their counts may be affected by biological (rather than technical) factors in an experiment and that technical covariates may…”
Single-cell RNA-seq reveals new types of human blood dendritic cells, monocytes and progenitors (2017, methodology): “To overcome some of these limitations, we used single-cell RNA sequencing (scRNA-seq) (4, 5) to better assess the diversity of blood DCs and monocytes, leading us to identify new subtypes of DCs and monocytes, refine their existing classification, and pinpoint a precursor of cDCs in the blood.”
Pooling across cells to normalize single-cell RNA sequencing data with many zero counts (2016, background): “Spike-in normalization also depends on several assumptions [4, 7, 8], the violations of which may compromise performance [9].”
CEL-Seq2: sensitive highly-multiplexed single-cell RNA-Seq (2016, background): “While Smart-Seq2 has an improved template switch step relative to Smart-Seq [5], previous work has also shown that C1-based transcriptomics shows increased sensitivity [14], thus making for an appropriate comparison.”