Megascale cell-cell similarity network

Dataset information

This is a large single-cell RNA-sequencing dataset of embryonic mouse brain cells. The dataset is preprocessed using techniques for single-cell transcriptomics. We selected and filtered the cells based on established quality-control metrics, normalized and rescaled single-cell measurements, detected highly variable genes, and removed unwanted sources of variation. Nodes represent cells in the mouse brain and edges represent nearest neighbor similarities between the cells. An edge indicates that two cells have similar gene expression as determined by the diffusion pseudotime analysis.

Single-cell RNA-sequencing has transformed our understanding of complex cell populations and has enabled us to study the diversity of cell types and the tissue composition of cell populations. No classification of cells into cell types is known for this network.

References

• Prioritizing network communities. Marinka Zitnik, Rok Sosic, and Jure Leskovec. Nature Communications. 2018.
  

• Massively parallel digital transcriptional profiling of single cells. Zheng, G. X. et al. Nature Communications. 2017.
  

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