FAIRit-BRAIN

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.

Technology:

Data Source:

Most prevalent class:

Most prevalent subclass:

Publish Date:

Jul 2015Jun 2021

Number of Cells

2561,048,576256 1,024 4,096 16,384 65,5361,048,576

Gini Coefficient

0.10.70.10.220.340.460.580.7

Class Precision Recall

0.710.70.780.861

Subclass Precision Recall

0.410.40.640.881

Percent Dropout

1382132741556982

Mitochondria AUROC

0.10.80.10.240.380.520.660.8

Identifiers:

SRA:


Abstract:

Information:

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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