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Cell BLAST
✨ Generative ModelsCell Querying via Neural Embedding
RNA
Neural network VAE method for cell searching and annotation via unbiased cell embedding
Publications
Searching large-scale scRNA-seq databases via unbiased cell embedding
Complexity
★★☆
moderateInterpretability
★★☆
mediumArchitecture
VAE with Search
Latent Dim
10
Used in LAIOR Framework
Cell-to-Cell Similarity Search
Cell BLAST learns unbiased cell embeddings via VAE that enable accurate cell-to-cell similarity search across datasets
Main Idea
Enable accurate cell annotation by learning batch-corrected embeddings for similarity search with reconstruction
Key Components
VAE Encoder
Learns cell embeddings from expression
Batch Correction
Removes multi-level batch effects
Decoder
Reconstructs expression with uncertainty
Similarity Metric
Computes cell-to-cell similarity with uncertainty
Mathematical Formulation
L = E[log p(x|z)] - KL(q(z|x)||p(z)); Similarity based on probabilistic embeddings
Loss Functions
ELBO
Reconstruction + KL divergence
Data Flow
Query Cells → VAE Encoder → Batch Correction → Decoder → Similarity Search → Reference Matches
Architecture Details
Architecture Type
VAE with Cell Similarity Module
Input/Output Types
single-cell → reconstruction
Key Layers
EncoderBatchCorrectorDecoderSimilarityLayer
Frameworks
PyTorch
Tags
vaesearchannotationbatch-correctionrna