Fig. 1: CellSpace learns a sequence-informed embedding of cells from scATAC-seq.
From: Scalable and unbiased sequence-informed embedding of single-cell ATAC-seq data with CellSpace
Overview of the CellSpace algorithm. a, CellSpace samples sequences from accessible events (peaks or tiles) to generate training examples, each consisting of an ordered list of overlapping k-mers from the sampled sequence, a positive cell (where the event is open) and a sample of negative cells (where the event in closed). b, CellSpace learns an embedding of k-mers and cells into the same latent space. For each training example, the embeddings of the corresponding k-mers and cells are updated to pull the induced sequence embedding towards the positive cell and away from the negative cells in the latent space; learning contextual information, represented by N-grams of nearby k-mers, improves the embedding. c, Once the embedding of cells and k-mers is trained, TF motifs can be mapped to the latent space, allowing cells to be scored for TF activities based on TF-cell similarities.
