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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.


Dataset statistics
Nodes 1018524
Edges 24735503
Nodes in largest SCC 1018524
Fraction of nodes in largest SCC 1.000000
Edges in largest SCC 24735503
Fraction of edges in largest SCC 1.000000
Average clustering coefficient 0.143218
Number of triangles 1466910096
Fraction of closed triangles 0.037984
Diameter (longest shortest path) 15
90-percentile effective diameter 7.810476

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


Files

File Size Description
CC-Neuron_cci.tsv.gz 1.9GB Cell-cell similarity edge list