Fig. 1: Overview of HYFA.
From: Hypergraph factorization for multi-tissue gene expression imputation
a, HYFA processes gene expression from a number of collected tissues (for example, accessible tissues) and infers the transcriptomes of uncollected tissues. b, Workflow of HYFA. The model receives as input a variable number of gene expression samples \({{{{\mathbf{x}}}}}_{i}^{(k)}\) corresponding to the collected tissues \(k\in {{{\mathcal{T}}}}(i)\) of a given individual i. The samples \({{{{\mathbf{x}}}}}_{i}^{(k)}\) are fed through an encoder that computes low-dimensional representations \({{{{\mathbf{e}}}}}_{ij}^{(k)}\) for each metagene j ∈ 1, …, M. A metagene is a latent, low-dimensional representation that captures certain gene expression patterns of the high-dimensional input sample. These representations are then used as hyperedge features in a message-passing neural network that operates on a hypergraph. In the hypergraph representation, each hyperedge labelled with \({{{{\mathbf{e}}}}}_{ij}^{(k)}\) connects an individual i with metagene j and tissue k if tissue k was collected for individual i, that is \(k\in {{{\mathcal{T}}}}(i)\). Through message passing, HYFA learns factorized representations of individual, tissue and metagene nodes. To infer the gene expression of an uncollected tissue u of individual i, the corresponding factorized representations are fed through an MLP that predicts low-dimensional features \({{{{\mathbf{e}}}}}_{ij}^{(u)}\) for each metagene j ∈ 1, …, M. HYFA finally processes these latent representations through a decoder that recovers the uncollected gene expression sample \({\hat{{{{\mathbf{x}}}}}}_{i}^{(u)}\).
