Erico Felden Pereira, Clarissa Stefani Teixeira, Rubian D Andrade, Adair da Silva-Lopes
[Elementary school teachers and their quality of life]
Revista de salud publica (Bogota, Colombia)
singlecell epigenomic data growing continuously unprecedented pace characteristics high dimensionality sparsity pose substantial challenges downstream analysis although deep learning modelsespecially variational autoencodershave widely used capture lowdimensional feature embeddings prevalent gaussian assumption somewhat disagrees real data models tend struggle incorporate reference information abundant cell atlases propose castle deep generative model based vectorquantized variational autoencoder framework extract discrete latent embeddings interpretably characterize singlecell chromatin accessibility sequencing data validate performance robustness castle accurate celltype identification reasonable visualization compared stateoftheart methods demonstrate advantages castle effective incorporation existing massive reference datasets weakly supervised supervised manner demonstrate castles capacity intuitively distilling celltypespecific feature spectra unveil cell heterogeneity biological implications quantitatively