FAIRit-BRAIN aims to make single-cell RNAseq data more FAIR (Findable Accessible Interoperable Reusable).
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Click on project id/UMAP to go to project-specific page with additional statistics and plotting.


Data Source:

Most prevalent class:

Most prevalent subclass:

Publish Date:

Number of Cells

Gini Coefficient

Class Precision Recall

Subclass Precision Recall

Percent Dropout

Mitochondria AUROC





Sample Attributes:

Quality Control statistics

Note: PCA without zero-centering was used: the explained variance does not correspond to the exact statistical defintion. The first component, e.g., might be heavily influenced by different means. The following components often resemble the exact PCA very closely.

MetaMarkers: cell type annotation

MetaNeighbor accesses replicability across datasets within the BICCN

The Brain Initiative - Cell Census Network (BICCN) is a ... Here we take high quality scRNA seq data from the BICCN, and assess replicability of cell types across datasets using MetaNeighbor (cite). MetaNeighbor uses a neighbor voting algorithm... etc

Something about the hierarchical cell type annotations

MetaMarkers looks for specific and sensitive markers across the BICCN datasets

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