Accounting for technical noise in single-cell RNA-seq experiments

By P. Brennecke, Simon Anders, Jong Kyoung Kim, Aleksandra A. Kolodziejczyk, Xiuwei Zhang, V. Proserpio, Bianka Baying, Vladimir Benes, Sarah A. Teichmann, John C. Marioni and 1 more. Nature Methods, 2013

973 citations recorded by the source corpus.

Subfields: Molecular Biology

View Paper: https://www.semanticscholar.org/paper/1bd6b2b6db204d13165f805f38b4f38ddf7f2922

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Citations in the corpus

Modelling dropouts for feature selection in scRNASeq experiments (2016, methodology): “Highly Variable Genes (HVG) were calculated using the published method [2].”
A survey of best practices for RNA-seq data analysis (2016, background): “Methods that can quantify gene-level technical variation allow us to focus on biological variation that is likely to be of interest [179].”
Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, methodology): “This ampli ﬁ - cation can be effectively countered using spike-ins, such as the ERCC Spike-In Mix from Ambion 56 , but some droplet-based applications 43,57 cannot easily incorporate this system.”
Massively-parallel single nucleus RNA-seq with DroNc-seq (2017, neutral): “…variable genes, we fit a relationship between mean counts and coefficient of variation using a Gamma distribution on the data from all the genes 19,25 , and ranked genes by the extent of excess variation as a function of their mean expression (using a threshold of at least 0.2 difference in the…”
Normalizing single-cell RNA sequencing data: Challenges and opportunities (2017, methodology): “Extrinsic control genes have also been used in the context of scRNA-seq [6, 25, 33, 35, 36], where spike-in sequences are added to each cell’s lysate in a theoretically constant and known amount.”
Single-cell transcriptome sequencing: recent advances and remaining challenges (2016, methodology): “As previously mentioned, scRNA-seq data are very noisy, and separating biological variation from technical noise remains a non-trivial problem 35,36 .”
Revealing the vectors of cellular identity with single-cell genomics (2016, background): “They thus constitute a well-defined set of negative controls for the purpose of adjusting for the difference in total RNA content between cells 60,71 , as well as for quality complexity and the amount of cellular RNA, which together result in large variations in the number of genes detected in each…”