FAIRit-BRAIN
FAIRit-BRAIN aims to make single-cell RNAseq data more FAIR (Findable Accessible Interoperable Reusable).
Filter datasets (projects) based on desired constraints and quality control metrics.
Download metadata for resultant subset.
Click on project id/UMAP to go to project-specific page with additional statistics and plotting.
Filter datasets (projects) based on desired constraints and quality control metrics.
Download metadata for resultant subset.
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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