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SCANPY: large-scale single-cell gene expression data analysis
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DOI：10.1186/s13059-017-1382-0.png)
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Abstract
En 中文
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).
Keywords:
Single-cell transcriptomics
Machine learning
Scalability
Graph analysis
Clustering
Pseudotemporal ordering
Trajectory inference
Differential expression testing
Visualization
Bioinformatics
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