The technology and biology of single-cell RNA sequencing.

By Aleksandra A. Kolodziejczyk, Jong Kyoung Kim, Valentine Svensson, John C. Marioni, Sarah A. Teichmann. Molecules and Cells, 2015

1,313 citations recorded by the source corpus.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/779f1919ab4bac545818971e0e1aad0763db0304

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Citations in the corpus

A practical guide to single-cell RNA-sequencing for biomedical research and clinical applications (2017, background): “Approximately 20 different scRNA-seq protocols have been published to date, the fine details of which have been thoroughly discussed elsewhere [56].”
Revealing the vectors of cellular identity with single-cell genomics (2016, background): “The first is technical variation, which is due to factors such as differences in cell integrity and lysis, RNA capture and cDNA conversion, and detection 38,39 .”
SCODE: an efficient regulatory network inference algorithm from single-cell RNA-Seq during differentiation (2016, background): “With the advancement of single-cell RNA-Seq (scRNA-Seq), we can now quantify the expression of individual cells and analyze detailed differences among cells [1].”
Microglia states and nomenclature: A field at its crossroads (2022, neutral): “Another source of complexity comes from evident interspecies differences, 75–77 which can further hamper comparisons.”
Feature selection and dimension reduction for single-cell RNA-Seq based on a multinomial model (2019, methodology): “Early scRNA-Seq studies using protocols such as SMART-Seq2 [8] analyzed these read counts directly, and several methods were developed to facilitate this [9].”
An accurate and robust imputation method scImpute for single-cell RNA-seq data (2018, methodology): “Fortunately, single-cell RNA sequencing (scRNA-seq) technologies are now emerging as a powerful tool to capture transcriptome-wide cell-to-cell variability 3 – 5 .”
Ensemble deep learning in bioinformatics (2020, background): “Gene expression data including microarray, RNA-sequencing (RNA-seq) and, recently, single-cell RNA-seq (scRNA-seq) 63–65 , has been studied extensively to better understand complex diseases and to identify biomarkers that can guide therapeutic decision making.”
Exponential scaling of single-cell RNA-seq in the past decade (2017, background): “classify and characterise cells at the molecular level (Kolodziejczyk et al., 2015; Trapnell, 2015).”
A general and flexible method for signal extraction from single-cell RNA-seq data (2017, neutral): “Introduction Single-cell RNA sequencing (scRNA-seq) is a powerful and relatively young technique enabling the characterization of the molecular states of individual cells through their transcriptional profiles [1].”
PanglaoDB: a web server for exploration of mouse and human single-cell RNA sequencing data (2019, background): “Introduction Single-cell RNA sequencing (scRNA-seq) is a technology that measures gene expression at the single-cell level (1).”
Transcriptomics technologies (2017, background): “Transcriptomes of different disease states, tissues, or even single cells are now routinely generated [5][6][7].”
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).”