Entering the era of single-cell transcriptomics in biology and medicine

231 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning (2016, background): “The heterogeneity identified across individual cells can answer questions irresolvable by traditional ensemble-based methods, where gene expression measurements are averaged over a population of cells pooled together [6], [7].”
Transcriptomics technologies (2017, background): “Transcriptomes of different disease states, tissues, or even single cells are now routinely generated [5][6][7].”
zUMIs - A fast and flexible pipeline to process RNA sequencing data with UMIs (2017, unknown): “The recent development of increasingly sensitive protocols allows to generate RNA-seq libraries of single cells [1].”
Single-Cell DNA Methylome Sequencing and Bioinformatic Inference of Epigenomic Cell-State Dynamics (2015, methodology): “Recent advances in single-cell RNA sequencing demonstrate the value of a higher resolution view (Sandberg, 2014) and suggest that methods for”
Tn5 transposase and tagmentation procedures for massively scaled sequencing projects (2014, unknown): “This represents a severe constraint in many next-generation sequencing–based projects, especially now that the importance of obtaining expression profiles from hundreds or thousands of single cells is more fully appreciated (Eberwineetal.2014;Sandberg2014).”