Latent Variable Models
Statistical models that explain observed data through unobserved hidden variables capturing underlying biological structure or processes.
Latent Variable Models are statistical frameworks that posit unobserved (latent) variables to explain the structure and variability in observed biological data 2.
How It Works
In biological systems, observed measurements like gene expression profiles are driven by hidden factors — cell type identity, developmental stage, or metabolic state — that cannot be directly measured. Latent variable models formalize this by defining a generative process: latent variables are drawn from a prior distribution, then transformed through a model to produce the observed data.
Classical examples include principal component analysis (PCA), where latent variables are linear projections, and Gaussian mixture models, where a discrete latent variable assigns each observation to a cluster. In single-cell biology, these approaches reveal cell types and continuous developmental trajectories from high-dimensional expression data 1.
Deep generative models like variational autoencoders (VAEs) learn nonlinear mappings between a low-dimensional latent space and the high-dimensional observation space. scVI, built on this framework, models count-based single-cell RNA-seq data with library size and batch effects as additional latent variables 1.
Computational Considerations
Variational inference enables scalable training of deep latent variable models on datasets with millions of cells. The learned latent space supports downstream tasks including differential expression, data integration across experiments, and in silico perturbation prediction — all without re-running expensive wet-lab experiments 2.
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Variational autoencoders and deep generative models learn latent representations of single-cell data, enabling cell-type discovery and trajectory inference at scale.