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Open AccessJournal ArticleDOI

The Human Cell Atlas

Aviv Regev, +81 more
- 05 Dec 2017 - 
- Vol. 6
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TLDR
An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease.
Abstract
The recent advent of methods for high-throughput single-cell molecular profiling has catalyzed a growing sense in the scientific community that the time is ripe to complete the 150-year-old effort to identify all cell types in the human body. The Human Cell Atlas Project is an international collaborative effort that aims to define all human cell types in terms of distinctive molecular profiles (such as gene expression profiles) and to connect this information with classical cellular descriptions (such as location and morphology). An open comprehensive reference map of the molecular state of cells in healthy human tissues would propel the systematic study of physiological states, developmental trajectories, regulatory circuitry and interactions of cells, and also provide a framework for understanding cellular dysregulation in human disease. Here we describe the idea, its potential utility, early proofs-of-concept, and some design considerations for the Human Cell Atlas, including a commitment to open data, code, and community.

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Citations
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Journal ArticleDOI

Comprehensive Integration of Single-Cell Data.

TL;DR: A strategy to "anchor" diverse datasets together, enabling us to integrate single-cell measurements not only across scRNA-seq technologies, but also across different modalities.
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Integrating single-cell transcriptomic data across different conditions, technologies, and species.

TL;DR: An analytical strategy for integrating scRNA-seq data sets based on common sources of variation is introduced, enabling the identification of shared populations across data sets and downstream comparative analysis.
Journal ArticleDOI

SCANPY: large-scale single-cell gene expression data analysis

TL;DR: This work presents Scanpy, a scalable toolkit for analyzing single-cell gene expression data that includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks, and AnnData, a generic class for handling annotated data matrices.
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Fast, sensitive and accurate integration of single-cell data with Harmony.

TL;DR: Harmony, for the integration of single-cell transcriptomic data, identifies broad and fine-grained populations, scales to large datasets, and can integrate sequencing- and imaging-based data.
Journal ArticleDOI

SCENIC: single-cell regulatory network inference and clustering.

TL;DR: On a compendium of single-cell data from tumors and brain, it is demonstrated that cis-regulatory analysis can be exploited to guide the identification of transcription factors and cell states.
References
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Journal ArticleDOI

Massively Parallel Single-Cell RNA-Seq for Marker-Free Decomposition of Tissues into Cell Types

TL;DR: An automated massively parallel single-cell RNA sequencing approach for analyzing in vivo transcriptional states in thousands of single cells is introduced and provides the ability to perform a bottom-up characterization of in vivo cell-type landscapes independent of cell markers or prior knowledge.
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Spatially resolved, highly multiplexed RNA profiling in single cells

TL;DR: This report reports multiplexed error-robust FISH (MERFISH), a single-molecule imaging method that allows thousands of RNA species to be imaged in single cells by using combinatorial FISH labeling with encoding schemes capable of detecting and/or correcting errors.
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viSNE enables visualization of high dimensional single-cell data and reveals phenotypic heterogeneity of leukemia

TL;DR: In this article, the authors present viSNE, a tool that allows one to map high-dimensional cytometry data onto two dimensions, yet conserve the highdimensional structure of the data by using all pairwise distances in high dimension to determine each cell's location in the plot.
Journal ArticleDOI

Functional roles for noise in genetic circuits

TL;DR: Examples and emerging principles that connect noise, the architecture of the gene circuits in which it is present, and the biological functions it enables are reviewed.
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