4.5 Article

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

期刊

GENOME BIOLOGY
卷 19, 期 -, 页码 -

出版社

BMC
DOI: 10.1186/s13059-017-1382-0

关键词

Single-cell transcriptomics; Machine learning; Scalability; Graph analysis; Clustering; Pseudotemporal ordering; Trajectory inference; Differential expression testing; Visualization; Bioinformatics

资金

  1. Helmholtz Postdoc Programme, Initiative and Networking Fund of the Helmholtz Association
  2. German Research Foundation (DFG) within the Collaborative Research Centre [1243]

向作者/读者索取更多资源

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).

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.5
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据