Fig. 1. GeneVector Framework.

Overview of GeneVector framework starting from single cell read counts. Mutual information is computed on the joint probability distribution of read counts for each gene pair. Each pair is used to train a single layer neural network where the MSE loss is evaluated from the model output (w₁^Tw₂) with the mutual information between genes. From the resulting weight matrix, a gene embedding, cell embedding, and co-expression similarity graph are constructed. Using vector space arithmetic, downstream analyses include identification of cell-specific metagenes, batch effect correction, and cell type classification.