Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells

By Florian Buettner, K. Natarajan, Francesco Paolo Casale, V. Proserpio, Antonio Scialdone, Fabian J Theis, Sarah A. Teichmann, John C. Marioni, Oliver Stegle. Nature Biotechnology, 2015

1,153 citations recorded by the source corpus.

Subfields: Cancer Research, Immunology, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/b6e1f669859c121e016394b2ade497ce79e04bef

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Citations in the corpus

CIDR: Ultrafast and accurate clustering through imputation for single-cell RNA-seq data (2016, methodology): “The CIDR algorithm The CIDR algorithm can be divided into the following five steps: (1) Identification of dropout candidates, (2) estimation of the relationship between dropout rate and gene expression levels, (3) calculation of dissimilarity between the imputed gene expression profiles for every pa”
Classification of low quality cells from single-cell RNA-seq data (2016, methodology): “training set mES [26]: E-MTAB-2600 mES [9]: E-MTAB-3749 Th2 [13]: E-MTAB-1499 BMDC [8]: E-GEOD-48968 UMI (Islam et al.”
An accurate and robust imputation method scImpute for single-cell RNA-seq data (2018, background): “Second, we show that scImpute correctly imputes the dropout values of 892 annotated cell cycle genes in 182 embryonic stem cells (ESCs) that had been staged for cell cycle phases (G1, G2M, and S) 20 .”
Single-cell entropy for accurate estimation of differentiation potency from a cell's transcriptome (2017, methodology): “There are five steps to the SCENT algorithm: (1) estimation of the differentiation potency of single cells via computation of signalling entropy, (2) inference of the potency state distribution across the single-cell population, (3) quantification of the intercellular heterogeneity of potency states”
Wishbone identifies bifurcating developmental trajectories from single-cell data (2016, methodology): “With similar reasoning, Buettner et al. 21 use latent variable models to remove the contribution of cell cycle in single-cell RNA-Seq.”
SINCERA: A Pipeline for Single-Cell RNA-Seq Profiling Analysis (2015, methodology): “We performed a comparative evaluation of SINCERA with three recently available singlecell RNA-seq analysis tools, SNN-Cliq [32], scLVM [27] and SINGuLAR Analysis Toolset (https://cn.”
Characterization of cell fate probabilities in single-cell data with Palantir (2019, neutral): “Expression of cell cycle genes can confound the ordering of cells in a differentiation trajectory. and hence we applied f-scLVM 53, 54 to factor out the cell-cycle effect across all cells.”
Perturb-Seq: Dissecting Molecular Circuits with Scalable Single-Cell RNA Profiling of Pooled Genetic Screens. (2016, methodology): “, the cell cycle in K562 cells) (Figures 2D, 2E, and 2G) (Buettner et al., 2015; Zeisel et al., 2015).”
Interpretable dimensionality reduction of single cell transcriptome data with deep generative models (2017, background): “An unsolved challenge is to develop robust computational methods to analyze large-scale single-cell data measuring the expression of dozens of protein markers to all the mRNA expression in tens of thousands to millions of cells in order to distill single-cell biology 20 – 23 .”