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

被引：0
|
作者：
F. Alexander Wolf
Philipp Angerer
Fabian J. Theis
机构：
[1] Institute of Computational Biology,Helmholtz Zentrum München – German Research Center for Environmental Health
[2] Technische Universität München,Department of Mathematics
来源：
Genome Biology | / 19卷
关键词：
Single-cell transcriptomics; Machine learning; Scalability; Graph analysis; Clustering; Pseudotemporal ordering; Trajectory inference; Differential expression testing; Visualization; Bioinformatics;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Scanpy is a scalable toolkit for analyzing single-cell gene expression data. It includes methods for preprocessing, visualization, clustering, pseudotime and trajectory inference, differential expression testing, and simulation of gene regulatory networks. Its Python-based implementation efficiently deals with data sets of more than one million cells (https://github.com/theislab/Scanpy). Along with Scanpy, we present AnnData, a generic class for handling annotated data matrices (https://github.com/theislab/anndata).
引用
收藏
相关论文
共 50 条
  • [31] Spatial reconstruction of single-cell gene expression data
    Rahul Satija
    Jeffrey A Farrell
    David Gennert
    Alexander F Schier
    Aviv Regev
    Nature Biotechnology, 2015, 33 : 495 - 502
  • [32] Spatial reconstruction of single-cell gene expression data
    Satija, Rahul
    Farrell, Jeffrey A.
    Gennert, David
    Schier, Alexander F.
    Regev, Aviv
    NATURE BIOTECHNOLOGY, 2015, 33 (05) : 495 - U206
  • [33] Large-scale neural model for visual attention: Integration of experimental single-cell and fMRI data
    Corchs, S
    Deco, G
    CEREBRAL CORTEX, 2002, 12 (04) : 339 - 348
  • [34] scSampler: fast diversity-preserving subsampling of large-scale single-cell transcriptomic data
    Song, Dongyuan
    Xi, Nan Miles
    Li, Jingyi Jessica
    Wang, Lin
    BIOINFORMATICS, 2022, 38 (11) : 3126 - 3127
  • [35] GENE DISCOVERY METHODS FROM LARGE-SCALE GENE EXPRESSION DATA
    Shimizu, Akifumi
    Yano, Kentaro
    QUANTUM BIO-INFORMATICS III: FROM QUANTUM INFORMATION TO BIO-INFORMATICS, 2010, 26 : 489 - +
  • [36] Latent cellular analysis robustly reveals subtle diversity in large-scale single-cell RNA-seq data
    Cheng, Changde
    Easton, John
    Rosencrance, Celeste
    Li, Yan
    Ju, Bensheng
    Williams, Justin
    Mulder, Heather L.
    Pang, Yakun
    Chen, Wenan
    Chen, Xiang
    NUCLEIC ACIDS RESEARCH, 2019, 47 (22)
  • [37] Bifurcation analysis of single-cell gene expression data reveals epigenetic landscape
    Marco, Eugenio
    Karp, Robert L.
    Guo, Guoji
    Robson, Paul
    Hart, Adam H.
    Trippa, Lorenzo
    Yuan, Guo-Cheng
    PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA, 2014, 111 (52) : E5643 - E5650
  • [38] Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq
    Bo Li
    Joshua Gould
    Yiming Yang
    Siranush Sarkizova
    Marcin Tabaka
    Orr Ashenberg
    Yanay Rosen
    Michal Slyper
    Monika S. Kowalczyk
    Alexandra-Chloé Villani
    Timothy Tickle
    Nir Hacohen
    Orit Rozenblatt-Rosen
    Aviv Regev
    Nature Methods, 2020, 17 : 793 - 798
  • [39] Cumulus provides cloud-based data analysis for large-scale single-cell and single-nucleus RNA-seq
    Li, Bo
    Gould, Joshua
    Yang, Yiming
    Sarkizova, Siranush
    Tabaka, Marcin
    Ashenberg, Orr
    Rosen, Yanay
    Slyper, Michal
    Kowalczyk, Monika S.
    Villani, Alexandra-Chloe
    Tickle, Timothy
    Hacohen, Nir
    Rozenblatt-Rosen, Orit
    Regev, Aviv
    NATURE METHODS, 2020, 17 (08) : 793 - +
  • [40] Large-scale single-cell trapping and imaging using microwell arrays
    Rettig, JR
    Folch, A
    ANALYTICAL CHEMISTRY, 2005, 77 (17) : 5628 - 5634
12345