Computational and analytical challenges in single-cell transcriptomics

By Oliver Stegle, Sarah A. Teichmann, John C. Marioni. Nature reviews genetics, 2015

1,155 citations recorded by the source corpus.

Subfields: Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/3829a2e9370a2973451118acf9ef847f4fbaab62

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Citations in the corpus

Multiplexed droplet single-cell RNA-sequencing using natural genetic variation (2017, background): “Improvements in biochemistry 11, 12 and microfluidics 13, 14 continue to increase the number of cells and transcripts profiled per experiment.”
Single-Cell RNA-Seq Technologies and Related Computational Data Analysis (2019, methodology): “On the other hand, an increasing number of tools are specially proposed for analyzing scRNA-seq data, and each method has its own pros and cons (Stegle et al., 2015; Bacher and Kendziorski, 2016).”
Computational assignment of cell-cycle stage from single-cell transcriptome data. (2015, neutral): “Recent technological advances have helped to establish single-cell RNA-sequencing (scRNA-seq) as a robust and routine assay, enabling the transcriptional proﬁling of thousands of cells to be processed in an unbiased manner [1,2].”
The technology and biology of single-cell RNA sequencing. (2015, methodology): “Based on advances made in the single-cell qPCR field, whole-transcriptome analysis was performed, using microarrays (Kamme et al., 2003; Kurimoto et al., 2006, 2007; Subkhankulova et al., 2008).”
Wishbone identifies bifurcating developmental trajectories from single-cell data (2016, methodology): “To address gene drop-out [27], the data was transformed using PCA to identify “metagenes”.”
Dissecting Immune Circuits by Linking CRISPR-Pooled Screens with Single-Cell RNA-Seq. (2016, positive): “Yet, current single-cell technologies are descriptive by nature, lacking the ability to elucidate causality unless they are combined with gene loss-of-function or knockout (KO) models (Junker and van Oudenaarden, 2014; Moignard and Go¨ttgens, 2014; Stegle et al., 2015).”
Defining cell types and states with single-cell genomics (2015, background): “For a detailed discussion of these issues, see Stegle et al. (2015); Kolodziejczyk et al. (2015).”
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models (2017, methodology): “For example, single-cell RNA-sequencing (scRNA-seq) 19, 24 – 26 can theoretically measure the expression of all the genes in tens of thousands of cells in a single experiment 9, 10, 14, 15 .”
Modelling dropouts for feature selection in scRNASeq experiments (2016, background): “We considered both the top 3 components and just the second and third components since the first principal component is often dominated by technical variation [11,40].”
SCENIC: Single-cell regulatory network inference and clustering (2017, methodology): “SCENIC is a workflow based on three new R/bioconductor packages: (1) GENIE3, to identify potential TF targets based on co-expression, (2) RcisTarget, to perform the TFmotif enrichment analysis and identify the direct targets (regulons), and (3) AUCell, to score the activity of regulons (or other gen”
Computational principles and challenges in single-cell data integration (2021, background): “Because of the small amounts of starting material, single-cell technologies are inherently noisy and result in large amounts of technical noise 140 .”