Highly Parallel Genome-wide Expression Profiling of Individual Cells Using Nanoliter Droplets

TL;DR: It is identified that distinct neuronal subtypes are differentially affected by sensory experience in the olfactory bulb, charting the molecular profiles that arise during the maturation and integration of adult-born neurons and how they dynamically change in an activity-dependent manner.

TL;DR: In this paper, the authors extend the second step of UMAP to a parametric optimization over neural network weights, learning a relationship between data and embedding, and demonstrate that parametric UMAP performs comparably to its nonparametric counterpart while conferring the benefit of learned parametric mapping.

TL;DR: Developments in high-throughput single-cell RNA sequencing may now provide the statistical power to dissect the diverse cellular populations of tumors and inform the selection of targeted combination therapies and enrollment criteria for clinical trials.

TL;DR: This work used large-scale measurements across multiple cell models to characterize activities of sgRNAs containing mismatches to their target sites and derived rules governing mismatched sg RNA activity using deep learning, which enabled it to synthesize a compact sgRNA library to titrate expression of ~2,400 genes essential for robust cell growth and to construct an in silico s gRNA library spanning the human genome.

TL;DR: In this paper, a density-based notion of clusters is proposed to discover clusters of arbitrary shape, which can be used for class identification in large spatial databases and is shown to be more efficient than the well-known algorithm CLAR-ANS.

TL;DR: DBSCAN, a new clustering algorithm relying on a density-based notion of clusters which is designed to discover clusters of arbitrary shape, is presented which requires only one input parameter and supports the user in determining an appropriate value for it.

TL;DR: Seurat is a computational strategy to infer cellular localization by integrating single-cell RNA-seq data with in situ RNA patterns, and correctly localizes rare subpopulations, accurately mapping both spatially restricted and scattered groups.

TL;DR: A single-cell digital gene expression profiling assay with only a single mouse blastomere is described, which detected the expression of 75% more genes than microarray techniques and identified 1,753 previously unknown splice junctions called by at least 5 reads.