Journal
GENOME BIOLOGY
Volume 19, Issue -, Pages -Publisher
BMC
DOI: 10.1186/s13059-017-1382-0
Keywords
Single-cell transcriptomics; Machine learning; Scalability; Graph analysis; Clustering; Pseudotemporal ordering; Trajectory inference; Differential expression testing; Visualization; Bioinformatics
Funding
- Helmholtz Postdoc Programme, Initiative and Networking Fund of the Helmholtz Association
- German Research Foundation (DFG) within the Collaborative Research Centre [1243]
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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).
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