Validation of noise models for single-cell transcriptomics

By Dominic Grün, L. Kester, Alexander van Oudenaarden. Nature Methods, 2014

729 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/7b3a1c907ad5077f07fc7354838dbd2526319740

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Citations in the corpus

Single-cell RNA sequencing technologies and bioinformatics pipelines (2018, methodology): “These barcoding approaches leverage molecular counting and demonstrate better reproducibility than indirect quanti ﬁ cation of molecules using sequencing read-based terminologies, such as RPKM/FPKM (read/ fragment per kilobase per million mapped reads) 32,44 .”
Normalization and variance stabilization of single-cell RNA-seq data using regularized negative binomial regression (2019, methodology): “For newer measurements based on UMI, modeling strategies have focused primarily on the use of the NB distribution [14], potentially including an additional parameter to model zero-inflation (ZINB).”
Design and Analysis of Single-Cell Sequencing Experiments. (2015, methodology): “, 2008) yield deviating estimates (Grün et al., 2014).”
The technology and biology of single-cell RNA sequencing. (2015, methodology): “IVT is an alternative approach that is incorporated into the CEL-Seq (cell expression by linear amplification and sequencing) (Hashimshony et al., 2012) and MARSSeq (massively parallel RNA single-cell sequencing) (Jaitin et al., 2014) protocols.”
Computational and analytical challenges in single-cell transcriptomics (2015, background): “Recently, extrinsic spike-in molecules have been used to further decompose technical variability into two terms that correspond to sampling noise and heterogeneity in sequencing efficiency across cells 75 .”
Defining cell types and states with single-cell genomics (2015, methodology): “New normalization techniques based on exogenous “spike-in” standards or molecular barcoding (Fu et al. 2014) may be needed to attribute changes in expression across cell populations to genuine biology (Brennecke et al. 2013; Grün et al. 2014).”
Revealing the vectors of cellular identity with single-cell genomics (2016, methodology): “Pool/split experimental designs can further assist in evaluating the magnitude of observed variation that should be attributed to technical factors 71,74 .”
SCENIC: Single-cell regulatory network inference and clustering (2017, background): “This may indeed be challenging given that at the single cell level, gene expression may, at least in part, be disconnected from the dynamics of transcription factor inputs due to stochastic variation of gene expression consecutive to, for example, transcriptional bursting 21–23 .”
Single-cell mRNA quantification and differential analysis with Census (2017, methodology): “We have developed Census, a normalization algorithm that can convert relative expression levels from read counts into per-cell transcript counts without the need for spike-in standards or UMIs.”
Information Constraints on Auto-Encoding Variational Bayes (2018, background): “Such data can reveal a cell’s type [29, 30], if we can cope with a high level of technical noise [31].”