Data-Driven Phenotypic Dissection of AML Reveals Progenitor-like Cells that Correlate with Prognosis.
Jacob H. Levine,Erin F. Simonds,Sean C. Bendall,Kara L. Davis,El-ad David Amir,Michelle D. Tadmor,Oren Litvin,Harris G. Fienberg,Astraea Jager,Eli R. Zunder,Rachel Finck,Amanda Larson Gedman,Ina Radtke,James R. Downing,Dana Pe'er,Garry P. Nolan +15 more
TLDR
Using hematopoietic progenitors, a signaling-based measure of cellular phenotype was defined, which led to isolation of a gene expression signature that was predictive of survival in independent cohorts, yielding insights into AML pathophysiology.About:
This article is published in Cell.The article was published on 2015-07-02 and is currently open access. It has received 1649 citations till now.read more
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Comprehensive Integration of Single-Cell Data.
Tim Stuart,Andrew Butler,Paul J. Hoffman,Christoph Hafemeister,Efthymia Papalexi,William M. Mauck,Yuhan Hao,Marlon Stoeckius,Peter Smibert,Rahul Satija +9 more
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.
Journal ArticleDOI
Integrated analysis of multimodal single-cell data
Yuhan Hao,Stephanie Hao,Erica Andersen-Nissen,William M. Mauck,Shiwei Zheng,Andrew Butler,Maddie Jane Lee,Aaron J. Wilk,Charlotte A. Darby,Michael Zager,Paul Hoffman,Marlon Stoeckius,Efthymia Papalexi,Eleni P. Mimitou,Jaison Jain,Avi Srivastava,Tim Stuart,Lamar M. Fleming,Bertrand Z. Yeung,Angela J. Rogers,Juliana M. McElrath,Catherine A. Blish,Raphael Gottardo,Peter Smibert,Rahul Satija +24 more
TL;DR: Weighted-nearest neighbor analysis as mentioned in this paper is an unsupervised framework to learn the relative utility of each data type in each cell, enabling an integrative analysis of multiple modalities.
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.
Journal ArticleDOI
Dimensionality reduction for visualizing single-cell data using UMAP.
Etienne Becht,Leland McInnes,John Healy,Charles-Antoine Dutertre,Immanuel Kwok,Lai Guan Ng,Florent Ginhoux,Evan W. Newell,Evan W. Newell +8 more
TL;DR: Comparing the performance of UMAP with five other tools, it is found that UMAP provides the fastest run times, highest reproducibility and the most meaningful organization of cell clusters.
Journal ArticleDOI
A Unique Microglia Type Associated with Restricting Development of Alzheimer’s Disease
Hadas Keren-Shaul,Amit Spinrad,Assaf Weiner,Assaf Weiner,Orit Matcovitch-Natan,Raz Dvir-Szternfeld,Tyler K. Ulland,Eyal David,Kuti Baruch,David Lara-Astaiso,Beáta Tóth,Shalev Itzkovitz,Marco Colonna,Michal Schwartz,Ido Amit +14 more
TL;DR: A novel microglia type associated with neurodegenerative diseases (DAM) is described and it is revealed that the DAM program is activated in a two-step process that involves downregulation of microglian checkpoints, followed by activation of a Trem2-dependent program.
References
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Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles
Aravind Subramanian,Pablo Tamayo,Vamsi K. Mootha,Sayan Mukherjee,Benjamin L. Ebert,Michael A. Gillette,Amanda G. Paulovich,Scott L. Pomeroy,Todd R. Golub,Eric S. Lander,Jill P. Mesirov +10 more
TL;DR: The Gene Set Enrichment Analysis (GSEA) method as discussed by the authors focuses on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation.
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Visualizing Data using t-SNE
TL;DR: A new technique called t-SNE that visualizes high-dimensional data by giving each datapoint a location in a two or three-dimensional map, a variation of Stochastic Neighbor Embedding that is much easier to optimize, and produces significantly better visualizations by reducing the tendency to crowd points together in the center of the map.
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Community structure in social and biological networks
Michelle Girvan,Mark Newman +1 more
TL;DR: This article proposes a method for detecting communities, built around the idea of using centrality indices to find community boundaries, and tests it on computer-generated and real-world graphs whose community structure is already known and finds that the method detects this known structure with high sensitivity and reliability.
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Fast unfolding of communities in large networks
Vincent D. Blondel,Jean-Loup Guillaume,Jean-Loup Guillaume,Renaud Lambiotte,Renaud Lambiotte,Etienne Lefebvre +5 more
TL;DR: This work proposes a heuristic method that is shown to outperform all other known community detection methods in terms of computation time and the quality of the communities detected is very good, as measured by the so-called modularity.
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The Elements of Statistical Learning
TL;DR: Chapter 11 includes more case studies in other areas, ranging from manufacturing to marketing research, and a detailed comparison with other diagnostic tools, such as logistic regression and tree-based methods.
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